Grain and Extent Considerations Are Integral for ...
Transcript of Grain and Extent Considerations Are Integral for ...
Article
Grain and Extent Considerations Are Integral forMonitoring Landscape-Scale Desired Conditions inFire-Adapted Forests dagger
Tzeidle N Wasserman 1 Andrew J Saacutenchez Meador 12 and Amy E M Waltz 1
1 Ecological Restoration Institute Northern Arizona University Flagstaff AZ 86011-5017 USA
AndrewSanchezMeadornauedu (AJSM) AmyWaltznauedu (AEMW)2 School of Forestry Northern Arizona University Flagstaff AZ 86011-5018 USA
Correspondence TzeidleWassermannauedu Tel +928-523-7488
dagger This manuscript is adapted from an annual report submitted to the USDA Forest Service which provided
funding for this research
Received 1 May 2019 Accepted 25 May 2019 Published 29 May 2019
Abstract Remotely-sensed data are commonly used to evaluate forest metrics such as canopy cover
to assess change detection and to inform land management planning Often canopy cover is measured
only at the scale of the spatial data product used in the analysis and there is a mismatch between the
management question and the scale of the data We compared four readily available remotely sensed
landscape data productsmdash Light detection and ranging (LiDAR) Landsat-8 Sentinel-2 and National
Agriculture Imagery Program (NAIP) imagery mdashat different spatial grains and multiple extents to
assess their consistency and efficacy for quantifying key landscape characteristics of forest canopy
patches and sensitivity to change We examined landscape-scale patterns of forest canopy cover
across three landscapes in northern Arizona and assessed their performance using six landscape
metrics Changes in grain and extent affect canopy cover patch metrics and the inferences that can be
made from each data product Overall data products performed differently across landscape metrics
When performing analyses and choosing data layers it is essential to match the scale of the data
product to the management question and understand the limitations inherent in using canopy cover
as a stand-alone metric
Keywords canopy cover landsat LiDAR sentinel naip landscape metrics spatial resolution patch
1 Introduction
United States federal land management agencies are directed to manage national forests to promote
resilient landscapes defined as within their natural range of variability [1] Public lands in the western
US require restoration to address the uncharacteristic current conditions such as high tree densities
high fuel loads bark beetle infestations and subsequent high fire hazard [1ndash3] Currently forests
are subject to a variety of disturbances including large wildfires insects pathogens and post-fire
flooding While these disturbances were always components of the historical ecosystem they are now
strongly influenced by human activities and climate change and uncharacteristic disturbances have
become more frequent higher in intensity and cover larger areas than historically [45] USDA Forest
Service restoration goals need to be accomplished at an accelerated pace to maintain resiliency [6ndash8]
In addition public lands have seen both leadership direction and political pressure to include increased
participation in planning and decision making from multiple agencies and stakeholders [9] As a
result both land managers and stakeholders are interested in understanding landscape-level processes
at scales appropriate to planning and implementation and quantifying metrics that are indicative of
resilient and restored ecosystems that capture the natural range of variability
Forests 2019 10 465 doi103390f10060465 wwwmdpicomjournalforests
Forests 2019 10 465 2 of 21
Understanding spatial and temporal patterns in forested landscapes is an important component of
ecosystem complexity and forest ecology However these are difficult concepts to quantify and capture
for land management practitioners and non-technical stakeholders [10ndash12] Forests are composed of a
mosaic of patches differing in their structure shape and spatial arrangement and these patterns affect
overall ecological processes [101314] Adjacent patches interact with one another to compose the
larger landscape and influence landscape patterns and processes at larger scales yet forest management
historically has been done on traditional stand-level scales Recent work examining the distribution and
proportion of forest attributes across the landscape informs questions concerning forest restoration and
resilience wildfire hydrology and wildlife habitat management [1013] but is not fully incorporated
into land management planning and active management
Quantifying spatial heterogeneity of forest structure can be used as a tool to measure the ecological
integrity of a system and forest structural and compositional complexity Scale is an important
component of our ecological view of the landscape Metrics used to describe forest structure and spatial
heterogeneity of a landscape will vary dependent on the scale at which they are examined [1015]
Scale within a landscape context can be defined in terms of both grain and extent [15ndash17] The spatial
grain of the landscape is the smallest resolution of the data Extent is the overall size of the landscape
or area encompassed by the observation Extent and grain determine the upper and lower limits of the
observation and changes in both grain and extent affect the patterns detected and the inferences we
make Canopy cover is a metric that is widely used in modeling species habitat relationships spatial
patterns fragmentation forest resilience stand density and many other areas of interest [131819]
It has also been used in land management direction as a surrogate for wildlife habitat quality However
canopy cover is measured or extracted in multiple ways at multiple spatial scales and extents that can
vary by the user-defined parameters Often this metric is used without explicitly defining how canopy
cover was measured or classified
Remote sensing is an effective tool for understanding current landscape conditions monitoring
forest change and understanding how forest ecosystems respond to disturbances The advancement
in technology and available data sources provide a means to assess a range of spatial information and
real-time data conduct surveys and provide continuous data sources across large landscapes [20]
There is often a lack of spatially explicit data that characterize forest heterogeneity at multiple scales
In many cases field or ground data are not readily available they are time-consuming and costly to
collect and are often not spatially replicated across large areas or available at the scale of the analysis
Therefore using readily available and spatially explicit remotely sensed data products can aid in
elucidating landscape patterns of canopy cover and other forest structure metrics when field data
are not available or not feasible Remotely sensed data products can provide spatially continuous
information with higher efficiency and lower cost
Light detection and ranging (LiDAR) has become increasingly useful in the three-dimensional
characterization of forest canopy and other forest attributes such as stand structure biomass and tree
density [2122] Airborne LiDAR data can be used to characterize tree-level forest structure and
over large areas [2324] and individual-tree detection methods can be used to characterize forest
structure tree spatial patterns and canopy metrics [2526] Additionally LiDAR can provide accurate
high-resolution descriptions of overstory structure and patterns with high accuracy at multiple
scales [27]
Remotely sensed data coupled with field observations can be an important data source to support
the monitoring of forest restoration via mechanical thinning and burning treatments and provide
baseline forest inventory data where there is none This study examines readily available remotely
sensed landscape data products to assess their consistency and efficacy for quantifying key landscape
characteristics of forest canopy patches and sensitivity to change due to forest management actions
or diversity In this analysis we compared four remotely sensed data products at different spatial
grains and multiple spatial extents to examine landscape-scale patterns of forest canopy cover across
three landscapes in northern Arizona USA Our objectives were to observe how changes in grain and
Forests 2019 10 465 3 of 21
extent affect canopy cover patch characteristics We specifically asked 1) How do readily available
and repeatable remotely sensed productsmdashLandsat-8 Sentinel-2 the National Agriculture Imagery
Program and LiDARmdashthat are at different spatial grains compare in their ability to accurately predict
canopy cover and 2) how do forest canopy patch metrics vary across remotely sensed data products
and spatial extents
2 Materials and Methods
21 Study Area
We examined landscape patterns of canopy cover across three ponderosa pinegambel oak
(Pinus ponderosa var scopulorum EngelmQuercus gambelii Nutt) forests spanning a gradient of
intact un-managed ponderosa pine forest to a highly managed forest in northern Arizona USA
Our three landscapes were Powell Plateau and Tepeats Creek near the North Rim of the Grand Canyon
and Windmill DrawJacks Canyon located on the Mogollon Rim (Figure 1) Powell Plateau (~1800 ha
in size) represents a relict landscape of ponderosa pine and oak on limestone soils that has remained a
relatively intact functional landscape with a natural fire disturbance history into the 20th century [28]
Tepeats Creek (~2100 ha) represents a ponderosa pine and oak forest on limestone soils with fire
exclusion and timber management since the late 19th century and Windmill DrawJacks Canyon
(~2400 ha hereafter referred to as ldquoWindmill Drawrdquo) represents a ponderosa pine and oak forest on
basalt soils with fire exclusion and timber management since the late 19th century both typical of
current southwestern forest conditions [29]
Figure 1 Map of study landscapes in northern Arizona USA Powell Plateau and Tepeats Creek on the
North Rim of the Grand Canyon and Windmill DrawJacks Canyon on the Mogollon Rim
22 Remotely Sensed Data
We used remotely sensed data from four sources to examine patterns of canopy cover across
our three study landscapes We used Landsat-8 data (30 m cell resolution) Sentinel-2 data (10 m
cell resolution) orthographic aerial imagery from the National Agriculture Imagery Program (NAIP)
(1 m cell resolution) and aerial LiDAR data (1 m cell resolution) across our three landscapes In this
analysis we compared landscape metrics calculated from these four spatial products (ie different
Forests 2019 10 465 4 of 21
grainsmdashLandsat-8 Sentinel-2 NAIP and LiDAR) and then make comparisons across landscapes
The Landsat and Sentinel data are easily available to land management agencies and are released
relatively often while NAIP imagery is released every two to four years depending on location LiDAR
although more accurate is currently at a higher cost and flown by individual agencies or coalitions
of natural resource management stakeholders Currently it is not consistently collected across the
nation but the USGS 3D Elevation Program (3DEP) has set a goal to complete nationwide LiDAR data
coverage by 2023 [30] Our objective was to address how remotely sensed canopy cover patch metrics
vary over multiple spatial extents and how the grain (cell resolution) of normally used products affects
landscape metrics commonly used to assess restoration success and thus patterns detected
Airborne LiDAR data were used to create a digital elevation model (DEM) digital surface model
(DSM) and a canopy height model (CHM) for each site at 1 m spatial resolution The Airborne LiDAR
data were collected over two acquisition periods AugustndashSept 2012 for Powell and Tepeats Creek and
AugustndashSept 2013 for Windmill Draw The data were requisitioned by the USDA Forest Service and
acquired by Quantum Spatial using Leica ALS50 Phase II and ALS60 systems either dual mounted
or separately in a Cessna Caravan and a Partenavia fixed-wing aircraft respectively The sensor
was flown at an altitude of 900 m aboveground with ge50 side-lap (ge100 overlap) resulting in an
average volumetric point-density of ge8 pulses per m2 These data were then classified as ground or
non-ground by the vendor using TerraScan v12004 and v13008 which uses the Axelsson algorithm
to classify the DTM points [31] The horizontal and vertical positioning errors in the point cloud data
were le15 cm Resulting CHM pixels above 2 m were classified as either canopy or not canopy with no
effort undertaken to differentiate between deciduous and coniferous vegetation
Additionally we utilized National Agriculture Imagery Program (NAIP) imagery collected for
Arizona in 2015 This imagery is available free via Google Earth Engine (GEE) [32] and includes four
bands (red green blue and infrared) with a 1 m ground sample distance At a minimum the horizontal
accuracy of the imagery must be within 6 m and cloud cover must be less than 10 per quarter USGS
topographic quadrangle tile NAIP Imagery was loaded into R (httpswwwr-projectorg) using
the raster package (httpscranr-projectorgwebpackagesraster) and a ratio band was calculated
by dividing the near-infrared and red (bands 4 and 1 respectively) [33] The ratio band was merged
with the red green blue and near-infrared bands Test classifications indicated that 25 classes limited
confusion between tree canopy and forest openings and a 25-class unsupervised classification was
run using the RStoolbox package (httpscranr-projectorgwebpackagesRStoolbox) and then each
class was evaluated by overlaying with LiDAR CHM to verify canopy and not canopy classes We
then classified the resulting layer into a binary map of canopy and not canopy cover at 1 m spatial
resolution and georeferenced this layer to the LiDAR CHM We then generated 1000 random points
within each landscape and performed an accuracy assessment using the LiDAR classified canopy and
not canopy image as truth Resulting overall classification accuracies were 633 618 and 763 for
Powell Tepeats Creek and Windmill Draw areas respectively
Sentinel-2 is part of the Copernicus Earth Observation Program of the European Union We utilized
cloud-free (lt10) fall imagery (October 2015 for Windmill Draw and August 2016 for Powell plateau
and Tepeats Creek) from Sentinel-2 were used to characterize canopy cover across the study area
The satellite is equipped with a wide swath multi spectral instrument (MSI) which collects near-nadir
imagery in 13 spectral bands (coastal aerosol red green blue three red edge two near-infrared
water vapor and three shortwave infrared) ranging from 10 to 60 m resolution [34] For this
study we used the 10 m resolution visible and near-infrared bands and each Sentinel-2 image was
atmospherically corrected to bottom of atmosphere reflectance using the Sen2Cor algorithm provided
by the European Space Agency Sentinel Application Platform [35] Using the same approach as we
used with the NAIP imagery we performed a 25-class unsupervised classification with the resulting
image being reclassified into a binary map of canopy and not canopy cover at 10 m spatial resolution
To perform an accuracy assessment with this product we aggregated the LiDAR canopy and not canopy
image to 10 m using the raster package and reclassified pixels to canopy where ge50 (an approximate
Forests 2019 10 465 5 of 21
non-linear interpolation based on the FAO classification using 10 cover at 05 ha [36] Hansen et alrsquos
classification of cover using 25 at 30 m [37] and an assumed 90 cover at 1 m for LiDAR) of the
LiDAR canopy image pixels were classified as canopy This layer was then compared to the classified
Sentinel-2 canopy image using 1000 randomly generated points and resulting overall classification
accuracies were 724 568 and 631 for Powell Tepeats Creek and Windmill Draw respectively
Lastly Landsat-8 imagery was used to assess canopy cover at the coarsest grain 30 m spatial
resolution We obtained Landsat-8 operational land imagery from the fall (OctoberndashNovember) 2015
These images contain seven bands (five visible and near-infrared bands and two short-wave infrared)
processed to orthorectified surface reflectance by GEE using the Landsat surface reflectance code
(LaSRC) which makes use of the coastal aerosol band to perform aerosol inversion tests uses auxiliary
climate data from MODIS and uses a unique radiative transfer model We used a cloud cloud-shadow
and cirrus cloud mask included in the SR data using CFMASK [38] As with the other products
we performed a 25-class unsupervised classification with the resulting image being reclassified into
a binary map of canopy and not canopy cover at 30 m spatial resolution To perform an accuracy
assessment with this product we aggregated the LiDAR canopy and not canopy image to 30 m using
the raster package and reclassified pixels to canopy where ge25 [37] of the LiDAR canopy image
pixels were classified as canopy This layer was then compared to the classified Landsat-8 canopy
image using 1000 randomly generate points and resulting overall classification accuracies were 746
693 and 853 for Powell Tepeats Creek and Windmill Draw respectively
23 Use of LiDAR as Reference Data
Airborne LiDAR is a widely accepted method of collecting and generating precise georeferenced
spatial information about features of the earthrsquos surface shape and physical characteristics [39]
Recent advances have produced mapping technologies that allow scientists to examine environmental
features such as forests watersheds and ecosystem attributes with accuracy and precision [39]
LiDAR technology provides horizontal and vertical information at high spatial resolution and vertical
accuracies It has been used to accurately assess forest canopy metrics and other forest attributes
such as stand structure biomass and tree density [2122] LiDAR provides very detailed wall-to-wall
maps of forest canopy cover (via reclassification of the CHM to covernot cover binary maps) that
are problematic and laborious to produce with traditional field and photointerpretation methods
Furthermore validating the accuracy of LiDAR-derived canopy cover using highly uncertain field
observations should be avoided as the results of ocular and vertical sighting methods are prone to
sampling problems and are difficult to confirm [40] As a result of these uncertainties LiDAR-derived
canopy cover maps are increasingly being used as reference data for assessing accuracies of alternative
methods which typically show agreement with LiDAR-derived canopy cover estimates but are subject
to high levels of bias (underestimate low and to overestimate high canopy cover) and imprecision [41ndash43]
Given that LiDAR-derived estimates are continuous and high resolution making them useful in forest
monitoring and management we chose to use LiDAR-derived metrics as reference data and compare
the performance of the three other spatially derived products to LiDAR
For each landscape the four data products that vary in grain (Landsat-8 30 m Sentinel-2 10 m
NAIP 1 m and LiDAR 1 m) were compared over multiple spatial extents (Figure 2) For all four
data products each patch was delineated by contiguous pixels of canopy cover and we used the
eight neighbor rule for cell-based indices- meaning contiguous cells were defined as those that were
either directly or adjacently contiguous After classification of each data layer into canopynot canopy
we used landscape metrics to quantify key characteristics of forest canopy patches and to quantify forest
cover patterns and make comparisons among different landscapes A total of six patch metrics were
examined to explain the variability of patches across landscapes of different extent (Table 1) With our
simple binary classification the proportion of landscape (PLand) patch density (PD) and mean patch
area (MPA) are basic descriptors of the canopy distribution These characteristics inform the descriptors
Forests 2019 10 465 6 of 21
of spatial connectedness and distribution including the aggregation index (AI) patch cohesion index
(PCI) and landscape division index (LDI)
Figure 2 Remotely sensed data products and associated grain of each data source Landsat-8 (30 m)
Sentinel-2 (10 m) NAIP (1 m) and LiDAR (1 m) The top panel (a) shows the canopy cover of Windmill
Draw landscape with the blue areas representing the relative size of each extent (10ndash1000 ha) analyzed
The lower panel (b) shows size of the spatial grain (resolution) and level of information available for
each data product within an example extent
Table 1 Description of landscape metrics evaluated across grain and extent on three landscapes
in northern Arizona USA Metrics derived from FRAGSTATS (wwwumassedulandecoresearch
fragstatsfragstatshtml)
Metric
bull PLand (proportion of landscape) is the total proportion of the patchmdashcanopy covermdashacross each landscape
bull PD (patch density) is the number of patches of canopy cover per unit area hectares in this analysis
bull MPA (mean patch area) is the average area of patches (ha) composed of canopy cover across each landscape
bull AI (aggregation index) is the area of canopy cover on the landscape that has like adjacencies of canopyclumped into a compact patch and is weighted proportionally by its area in the landscape It is a landscapemetric of how aggregated canopy cover is at each extent
bull PCI (patch cohesion index) is a connectivity metric that measures the physical connectedness of thecorresponding patch type Patch cohesion increases as canopy cover becomes more clumped or aggregated inits distribution and more physically connected
bull LDI (landscape division index) is the probability that two randomly chosen pixels in the landscape are notin the same patch and is based on the cumulative patch area distribution The sum is across the proportionalarea of each patch of canopy cover
For each landscape and each remote sensing product we performed a landscape pattern analysis
of canopy cover using the SDM Tool package (httpsCRANR-projectorgpackage=SDMTools)
in R (R Studio httpswwwr-projectorg) SDM Tools calculates landscape metrics (similar to
FRAGSTATS [44]) and outputs values for each metric We sampled each landscape metric across five
to seven different spatial extents 10 25 50 75 100 300 600 1000 ha We performed 100 Markov
Chain Monte Carlo (MCMC) simulations to sample each landscape at these multiple extents Our three
Forests 2019 10 465 7 of 21
landscapes varied in size therefore all three landscapes did not get sampled at the largest extents
Powell Plateau was sampled 10 handash300 ha Tepeats Creek was sampled 10 handash600 ha and Windmill
Draw was sampled 10 handash1000 ha (Figure 2)
3 Results
General patterns of patch characteristics were observed across spatial extents and between the four
data products (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) that match our understanding of
expected changes of patches with scale and grain This study more specifically tested readily available
remotely sensed data products to assess their consistency and efficacy for quantifying key characteristics
of forest canopy and sensitivity to change due to forest management or diversity Here we report the
differences in data products (grain) and how the patterns change across spatial extents
31 Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics ofCanopy Cover
The proportion of landscape (PLand) comprised of canopy cover was underpredicted by NAIP
and overpredicted by Landsat-8 and Sentinel-2 across our three landscapes PLand was best predicted
by the Sentinel-2 data (10 m) which produced means most similar to the LiDAR data (1 m) on Powell
Plateau our remnant functional landscape with an intact natural disturbance regime (Table 2 Figure 3)
NAIP (1 m) while still underestimated effective1y best characterized PLand on Windmill Draw
a highly managed forest landscape experiencing both fire exclusion and extensive timber management
Landsat-8 (30 m) overestimated PLand on all three landscapes
Table 2 Proportion of landscape (PLand) comprised of canopy cover across each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
Mean values and standard error (SE) are shown
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80633
(0019)0619
(0017)0635
(0013)0648
(0011)0660
(0009)0584
(0000)
Sentinel-20568
(0011)0559
(0010)0569
(0006)0578
(0005)0577
(0004)0550
(0000)
NAIP0314
(0006)0319
(0005)0330
(0003)0332
(0003)0334
(0003)0329
(0000)
LiDAR0537
(0010)0535
(0007)0552
(0006)0560
(0005)0560
(0004)0543
(0000)
Tepeats Creek
Landsat-80743
(0013)0734
(0011)0742
(0009)0732
(0009)0737
(0008)0744
(0003)0748
(0001)
Sentinel-20824
(0011)0797
(0009)0807
(0008)0795
(0008)0796
(0007)0790
(0003)0797
(0000)
NAIP0439
(0007)0422
(0006)0427
(0005)0418
(0004)0417
(0004)0420
(0001)0418
(0000)
LiDAR0596
(0010)0571
(0008)0565
(0008)0560
(0007)0550
(0007)0546
(0002)0568
(0001)
Forests 2019 10 465 8 of 21
Table 2 Cont
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80835
(0010)0826
(0009)0832
(0007)0824
(0006)0838
(0005)0826
(0002)0828
(0001)0831
(0000)
Sentinel-20832
(0009)0819
(0009)0828
(0007)0816
(0007)0824
(0006)0813
(0002)0814
(0001)0809
(0001)
NAIP0356
(0008)0336
(0007)0347
(0006)0339
(0006)0341
(0005)0321
(0002)0321
(0001)0318
(0000)
LiDAR0492
(0010)0473
(0009)0484
(0008)0476
(0008)0480
(0007)0461
(0002)0467
(0001)0470
(0000)
Figure 3 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for the proportion of landscape (ha) under forested canopy cover for each landscape and data product
(Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets
with rescaled y-axis provided for clarity
The number of patches of canopy cover or patch density (PD) was not accurately predicted
by any of our spatial products across landscapes or spatial extents NAIP at 1 m resolution highly
over-predicted the patch density by 25 to 3 times across all three landscapes (Table 3 Figure 4)
The small pixel size can fragment patches that the LiDAR point cloud keeps aggregated thus resulting
in many more patches across the landscape at all extents Landsat (30 m) and Sentinel-2 (10 m) highly
underestimated PD at their native cell resolutions Mean Patch Area (MPA) the average area composed
of patches of canopy cover was highly overestimated (one to two orders of magnitude) by Landsat
and Sentinel-2 as compared to LiDAR (Table 4 Figure 5) NAIP underestimated mean patch area by
three to four times on all landscapes across all spatial extents
Forests 2019 10 465 9 of 21
Table 3 Patch density (PD) the average number of patches of canopy cover on each landscape
delineated by each data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Patch Density (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80214
(0015)0138
(0011)0090
(0006)0063
(0004)0061
(0004)0075
(0000)
Sentinel-21047
(0074)0883
(0053)0706
(0035)0597
(0028)0590
(0025)0673
(0002)
NAIP57969(1079)
56659(0695)
53808(0516)
53300(0499)
52735(0448)
50537(0025)
LiDAR15428(0661)
15170(0536)
13349(0377)
12601(0352)
12375(0302)
12950(0011)
Tepeats Creek
Landsat-80153
(0010)0085
(0007)0051
(0004)0041
(0003)0038
(0003)0019
(0001)0011
(0000)
Sentinel-20310
(0032)0259
(0023)0189
(0017)0207
(0015)0185
(0014)0182
(0005)0157
(0000)
NAIP41339(1283)
40980(1027)
39345(0835)
40110(0723)
39728(0626)
37624(0212)
34257(0041)
LiDAR15310(0750)
16167(0617)
16325(0616)
16551(0526)
16973(0487)
16740(0135)
15306(0022)
Windmill Draw
Landsat-80138
(0007)0056
(0004)0032
(0002)0030
(0003)0021
(0002)0013
(0001)0011
(0000)0008
(0000)
Sentinel-20205
(0018)0153
(0018)0099
(0009)0104
(0008)0081
(0006)0073
(0002)0072
(0001)0067
(0000)
NAIP52483(1502)
54286(1227)
52324(1362)
52001(1231)
52385(1146)
55683(0579)
54084(0352)
51462(0104)
LiDAR23018(0853)
23109(0691)
22289(0706)
21886(0663)
21978(0618)
22724(0238)
21838(0135)
21666(0040)
Figure 4 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch density (patches per ha) Patch density is the number of patches per landscape area under
forested canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1
m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 10 of 21
Table 4 Mean Patch Area (MPA) (ha) the average area composed of patches of canopy cover across
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Mean Patch Area (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-8 4647(0299)
9474(0776)
13988(1296)
19518(1840)
20520(2052)
7819(0035)
Sentinel-2 1094(0132)
1158(0129)
1096(0069)
1245(0067)
1206(0061)
0817(0002)
NAIP 0006(0000)
0006(0000)
0006(0000)
0006(0000)
0006(0000)
0007(0000)
LiDAR 0045(0003)
0042(0002)
0046(0002)
0048(0001)
0048(0001)
0042(0000)
Tepeats Creek
Landsat-8 6351(0264)
13949(0736)
25349(1542)
31540(2152)
34500(2566)
47600(2326)
71374(1401)
Sentinel-2 5420(0344)
7333(0700)
12646(1459)
8224(1062)
7709(0663)
4834(0187)
5082(0014)
NAIP 0012(0001)
0011(0000)
0012(0000)
0011(0000)
0011(0000)
0011(0000)
0012(0000)
LiDAR 0059(0006)
0045(0003)
0046(0004)
0041(0003)
0038(0002)
0033(0000)
0037(0000)
Windmill Draw
Landsat-8 7248(0255)
18257(0611)
33437(1340)
42230(2231)
58305(2789)
95256(6883)
85781(3900)
104836(0889)
Sentinel-2 6083(0302)
11371(0782)
17674(1437)
19018(2053)
20103(2224)
12738(0508)
11511(0152)
12085(0100)
NAIP 0008(0001)
0007(0000)
0008(0000)
0008(0000)
0007(0000)
0006(0000)
0006(0000)
0006(0000)
LiDAR 0035(0005)
0028(0004)
0030(0004)
0029(0003)
0028(0002)
0021(0000)
0022(0000)
0022(0000)
Figure 5 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for mean patch area (ha) (average size of patches) for each landscape and data product (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis
provided for clarity
Forests 2019 10 465 11 of 21
Canopy cover was highly aggregated an attribute that was detected by all four data products
across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately
on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8
best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and
Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and
across all extents especially on Powell Plateau
Figure 6 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are
to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8
30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and
standard error (SE) are shown
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-876761(1175)
76553(0986)
77397(0802)
78012(0712)
78950(0581)
74823(0021)
Sentinel-269008(0748)
68585(0636)
68873(0473)
69394(0406)
69600(0323)
66906(0009)
NAIP77402(0246)
77603(0191)
78206(0113)
78239(0122)
78429(0105)
78773(0003)
LiDAR91447(0146)
91467(0106)
91810(0074)
91921(0070)
91941(0063)
91810(0001)
Tepeats Creek
Landsat-882820(0625)
82027(0543)
81855(0490)
81684(0425)
81706(0426)
82238(0139)
82418(0047)
Sentinel-290755(0471)
89497(0390)
90100(0311)
89688(0269)
89613(0221)
89824(0094)
90086(0012)
NAIP81195(0239)
81281(0183)
81362(0150)
81272(0137)
81256(0121)
81784(0047)
81914(0013)
LiDAR91847(0181)
91468(0144)
91330(0146)
91286(0127)
91147(0118)
91083(0041)
91506(0013)
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 2 of 21
Understanding spatial and temporal patterns in forested landscapes is an important component of
ecosystem complexity and forest ecology However these are difficult concepts to quantify and capture
for land management practitioners and non-technical stakeholders [10ndash12] Forests are composed of a
mosaic of patches differing in their structure shape and spatial arrangement and these patterns affect
overall ecological processes [101314] Adjacent patches interact with one another to compose the
larger landscape and influence landscape patterns and processes at larger scales yet forest management
historically has been done on traditional stand-level scales Recent work examining the distribution and
proportion of forest attributes across the landscape informs questions concerning forest restoration and
resilience wildfire hydrology and wildlife habitat management [1013] but is not fully incorporated
into land management planning and active management
Quantifying spatial heterogeneity of forest structure can be used as a tool to measure the ecological
integrity of a system and forest structural and compositional complexity Scale is an important
component of our ecological view of the landscape Metrics used to describe forest structure and spatial
heterogeneity of a landscape will vary dependent on the scale at which they are examined [1015]
Scale within a landscape context can be defined in terms of both grain and extent [15ndash17] The spatial
grain of the landscape is the smallest resolution of the data Extent is the overall size of the landscape
or area encompassed by the observation Extent and grain determine the upper and lower limits of the
observation and changes in both grain and extent affect the patterns detected and the inferences we
make Canopy cover is a metric that is widely used in modeling species habitat relationships spatial
patterns fragmentation forest resilience stand density and many other areas of interest [131819]
It has also been used in land management direction as a surrogate for wildlife habitat quality However
canopy cover is measured or extracted in multiple ways at multiple spatial scales and extents that can
vary by the user-defined parameters Often this metric is used without explicitly defining how canopy
cover was measured or classified
Remote sensing is an effective tool for understanding current landscape conditions monitoring
forest change and understanding how forest ecosystems respond to disturbances The advancement
in technology and available data sources provide a means to assess a range of spatial information and
real-time data conduct surveys and provide continuous data sources across large landscapes [20]
There is often a lack of spatially explicit data that characterize forest heterogeneity at multiple scales
In many cases field or ground data are not readily available they are time-consuming and costly to
collect and are often not spatially replicated across large areas or available at the scale of the analysis
Therefore using readily available and spatially explicit remotely sensed data products can aid in
elucidating landscape patterns of canopy cover and other forest structure metrics when field data
are not available or not feasible Remotely sensed data products can provide spatially continuous
information with higher efficiency and lower cost
Light detection and ranging (LiDAR) has become increasingly useful in the three-dimensional
characterization of forest canopy and other forest attributes such as stand structure biomass and tree
density [2122] Airborne LiDAR data can be used to characterize tree-level forest structure and
over large areas [2324] and individual-tree detection methods can be used to characterize forest
structure tree spatial patterns and canopy metrics [2526] Additionally LiDAR can provide accurate
high-resolution descriptions of overstory structure and patterns with high accuracy at multiple
scales [27]
Remotely sensed data coupled with field observations can be an important data source to support
the monitoring of forest restoration via mechanical thinning and burning treatments and provide
baseline forest inventory data where there is none This study examines readily available remotely
sensed landscape data products to assess their consistency and efficacy for quantifying key landscape
characteristics of forest canopy patches and sensitivity to change due to forest management actions
or diversity In this analysis we compared four remotely sensed data products at different spatial
grains and multiple spatial extents to examine landscape-scale patterns of forest canopy cover across
three landscapes in northern Arizona USA Our objectives were to observe how changes in grain and
Forests 2019 10 465 3 of 21
extent affect canopy cover patch characteristics We specifically asked 1) How do readily available
and repeatable remotely sensed productsmdashLandsat-8 Sentinel-2 the National Agriculture Imagery
Program and LiDARmdashthat are at different spatial grains compare in their ability to accurately predict
canopy cover and 2) how do forest canopy patch metrics vary across remotely sensed data products
and spatial extents
2 Materials and Methods
21 Study Area
We examined landscape patterns of canopy cover across three ponderosa pinegambel oak
(Pinus ponderosa var scopulorum EngelmQuercus gambelii Nutt) forests spanning a gradient of
intact un-managed ponderosa pine forest to a highly managed forest in northern Arizona USA
Our three landscapes were Powell Plateau and Tepeats Creek near the North Rim of the Grand Canyon
and Windmill DrawJacks Canyon located on the Mogollon Rim (Figure 1) Powell Plateau (~1800 ha
in size) represents a relict landscape of ponderosa pine and oak on limestone soils that has remained a
relatively intact functional landscape with a natural fire disturbance history into the 20th century [28]
Tepeats Creek (~2100 ha) represents a ponderosa pine and oak forest on limestone soils with fire
exclusion and timber management since the late 19th century and Windmill DrawJacks Canyon
(~2400 ha hereafter referred to as ldquoWindmill Drawrdquo) represents a ponderosa pine and oak forest on
basalt soils with fire exclusion and timber management since the late 19th century both typical of
current southwestern forest conditions [29]
Figure 1 Map of study landscapes in northern Arizona USA Powell Plateau and Tepeats Creek on the
North Rim of the Grand Canyon and Windmill DrawJacks Canyon on the Mogollon Rim
22 Remotely Sensed Data
We used remotely sensed data from four sources to examine patterns of canopy cover across
our three study landscapes We used Landsat-8 data (30 m cell resolution) Sentinel-2 data (10 m
cell resolution) orthographic aerial imagery from the National Agriculture Imagery Program (NAIP)
(1 m cell resolution) and aerial LiDAR data (1 m cell resolution) across our three landscapes In this
analysis we compared landscape metrics calculated from these four spatial products (ie different
Forests 2019 10 465 4 of 21
grainsmdashLandsat-8 Sentinel-2 NAIP and LiDAR) and then make comparisons across landscapes
The Landsat and Sentinel data are easily available to land management agencies and are released
relatively often while NAIP imagery is released every two to four years depending on location LiDAR
although more accurate is currently at a higher cost and flown by individual agencies or coalitions
of natural resource management stakeholders Currently it is not consistently collected across the
nation but the USGS 3D Elevation Program (3DEP) has set a goal to complete nationwide LiDAR data
coverage by 2023 [30] Our objective was to address how remotely sensed canopy cover patch metrics
vary over multiple spatial extents and how the grain (cell resolution) of normally used products affects
landscape metrics commonly used to assess restoration success and thus patterns detected
Airborne LiDAR data were used to create a digital elevation model (DEM) digital surface model
(DSM) and a canopy height model (CHM) for each site at 1 m spatial resolution The Airborne LiDAR
data were collected over two acquisition periods AugustndashSept 2012 for Powell and Tepeats Creek and
AugustndashSept 2013 for Windmill Draw The data were requisitioned by the USDA Forest Service and
acquired by Quantum Spatial using Leica ALS50 Phase II and ALS60 systems either dual mounted
or separately in a Cessna Caravan and a Partenavia fixed-wing aircraft respectively The sensor
was flown at an altitude of 900 m aboveground with ge50 side-lap (ge100 overlap) resulting in an
average volumetric point-density of ge8 pulses per m2 These data were then classified as ground or
non-ground by the vendor using TerraScan v12004 and v13008 which uses the Axelsson algorithm
to classify the DTM points [31] The horizontal and vertical positioning errors in the point cloud data
were le15 cm Resulting CHM pixels above 2 m were classified as either canopy or not canopy with no
effort undertaken to differentiate between deciduous and coniferous vegetation
Additionally we utilized National Agriculture Imagery Program (NAIP) imagery collected for
Arizona in 2015 This imagery is available free via Google Earth Engine (GEE) [32] and includes four
bands (red green blue and infrared) with a 1 m ground sample distance At a minimum the horizontal
accuracy of the imagery must be within 6 m and cloud cover must be less than 10 per quarter USGS
topographic quadrangle tile NAIP Imagery was loaded into R (httpswwwr-projectorg) using
the raster package (httpscranr-projectorgwebpackagesraster) and a ratio band was calculated
by dividing the near-infrared and red (bands 4 and 1 respectively) [33] The ratio band was merged
with the red green blue and near-infrared bands Test classifications indicated that 25 classes limited
confusion between tree canopy and forest openings and a 25-class unsupervised classification was
run using the RStoolbox package (httpscranr-projectorgwebpackagesRStoolbox) and then each
class was evaluated by overlaying with LiDAR CHM to verify canopy and not canopy classes We
then classified the resulting layer into a binary map of canopy and not canopy cover at 1 m spatial
resolution and georeferenced this layer to the LiDAR CHM We then generated 1000 random points
within each landscape and performed an accuracy assessment using the LiDAR classified canopy and
not canopy image as truth Resulting overall classification accuracies were 633 618 and 763 for
Powell Tepeats Creek and Windmill Draw areas respectively
Sentinel-2 is part of the Copernicus Earth Observation Program of the European Union We utilized
cloud-free (lt10) fall imagery (October 2015 for Windmill Draw and August 2016 for Powell plateau
and Tepeats Creek) from Sentinel-2 were used to characterize canopy cover across the study area
The satellite is equipped with a wide swath multi spectral instrument (MSI) which collects near-nadir
imagery in 13 spectral bands (coastal aerosol red green blue three red edge two near-infrared
water vapor and three shortwave infrared) ranging from 10 to 60 m resolution [34] For this
study we used the 10 m resolution visible and near-infrared bands and each Sentinel-2 image was
atmospherically corrected to bottom of atmosphere reflectance using the Sen2Cor algorithm provided
by the European Space Agency Sentinel Application Platform [35] Using the same approach as we
used with the NAIP imagery we performed a 25-class unsupervised classification with the resulting
image being reclassified into a binary map of canopy and not canopy cover at 10 m spatial resolution
To perform an accuracy assessment with this product we aggregated the LiDAR canopy and not canopy
image to 10 m using the raster package and reclassified pixels to canopy where ge50 (an approximate
Forests 2019 10 465 5 of 21
non-linear interpolation based on the FAO classification using 10 cover at 05 ha [36] Hansen et alrsquos
classification of cover using 25 at 30 m [37] and an assumed 90 cover at 1 m for LiDAR) of the
LiDAR canopy image pixels were classified as canopy This layer was then compared to the classified
Sentinel-2 canopy image using 1000 randomly generated points and resulting overall classification
accuracies were 724 568 and 631 for Powell Tepeats Creek and Windmill Draw respectively
Lastly Landsat-8 imagery was used to assess canopy cover at the coarsest grain 30 m spatial
resolution We obtained Landsat-8 operational land imagery from the fall (OctoberndashNovember) 2015
These images contain seven bands (five visible and near-infrared bands and two short-wave infrared)
processed to orthorectified surface reflectance by GEE using the Landsat surface reflectance code
(LaSRC) which makes use of the coastal aerosol band to perform aerosol inversion tests uses auxiliary
climate data from MODIS and uses a unique radiative transfer model We used a cloud cloud-shadow
and cirrus cloud mask included in the SR data using CFMASK [38] As with the other products
we performed a 25-class unsupervised classification with the resulting image being reclassified into
a binary map of canopy and not canopy cover at 30 m spatial resolution To perform an accuracy
assessment with this product we aggregated the LiDAR canopy and not canopy image to 30 m using
the raster package and reclassified pixels to canopy where ge25 [37] of the LiDAR canopy image
pixels were classified as canopy This layer was then compared to the classified Landsat-8 canopy
image using 1000 randomly generate points and resulting overall classification accuracies were 746
693 and 853 for Powell Tepeats Creek and Windmill Draw respectively
23 Use of LiDAR as Reference Data
Airborne LiDAR is a widely accepted method of collecting and generating precise georeferenced
spatial information about features of the earthrsquos surface shape and physical characteristics [39]
Recent advances have produced mapping technologies that allow scientists to examine environmental
features such as forests watersheds and ecosystem attributes with accuracy and precision [39]
LiDAR technology provides horizontal and vertical information at high spatial resolution and vertical
accuracies It has been used to accurately assess forest canopy metrics and other forest attributes
such as stand structure biomass and tree density [2122] LiDAR provides very detailed wall-to-wall
maps of forest canopy cover (via reclassification of the CHM to covernot cover binary maps) that
are problematic and laborious to produce with traditional field and photointerpretation methods
Furthermore validating the accuracy of LiDAR-derived canopy cover using highly uncertain field
observations should be avoided as the results of ocular and vertical sighting methods are prone to
sampling problems and are difficult to confirm [40] As a result of these uncertainties LiDAR-derived
canopy cover maps are increasingly being used as reference data for assessing accuracies of alternative
methods which typically show agreement with LiDAR-derived canopy cover estimates but are subject
to high levels of bias (underestimate low and to overestimate high canopy cover) and imprecision [41ndash43]
Given that LiDAR-derived estimates are continuous and high resolution making them useful in forest
monitoring and management we chose to use LiDAR-derived metrics as reference data and compare
the performance of the three other spatially derived products to LiDAR
For each landscape the four data products that vary in grain (Landsat-8 30 m Sentinel-2 10 m
NAIP 1 m and LiDAR 1 m) were compared over multiple spatial extents (Figure 2) For all four
data products each patch was delineated by contiguous pixels of canopy cover and we used the
eight neighbor rule for cell-based indices- meaning contiguous cells were defined as those that were
either directly or adjacently contiguous After classification of each data layer into canopynot canopy
we used landscape metrics to quantify key characteristics of forest canopy patches and to quantify forest
cover patterns and make comparisons among different landscapes A total of six patch metrics were
examined to explain the variability of patches across landscapes of different extent (Table 1) With our
simple binary classification the proportion of landscape (PLand) patch density (PD) and mean patch
area (MPA) are basic descriptors of the canopy distribution These characteristics inform the descriptors
Forests 2019 10 465 6 of 21
of spatial connectedness and distribution including the aggregation index (AI) patch cohesion index
(PCI) and landscape division index (LDI)
Figure 2 Remotely sensed data products and associated grain of each data source Landsat-8 (30 m)
Sentinel-2 (10 m) NAIP (1 m) and LiDAR (1 m) The top panel (a) shows the canopy cover of Windmill
Draw landscape with the blue areas representing the relative size of each extent (10ndash1000 ha) analyzed
The lower panel (b) shows size of the spatial grain (resolution) and level of information available for
each data product within an example extent
Table 1 Description of landscape metrics evaluated across grain and extent on three landscapes
in northern Arizona USA Metrics derived from FRAGSTATS (wwwumassedulandecoresearch
fragstatsfragstatshtml)
Metric
bull PLand (proportion of landscape) is the total proportion of the patchmdashcanopy covermdashacross each landscape
bull PD (patch density) is the number of patches of canopy cover per unit area hectares in this analysis
bull MPA (mean patch area) is the average area of patches (ha) composed of canopy cover across each landscape
bull AI (aggregation index) is the area of canopy cover on the landscape that has like adjacencies of canopyclumped into a compact patch and is weighted proportionally by its area in the landscape It is a landscapemetric of how aggregated canopy cover is at each extent
bull PCI (patch cohesion index) is a connectivity metric that measures the physical connectedness of thecorresponding patch type Patch cohesion increases as canopy cover becomes more clumped or aggregated inits distribution and more physically connected
bull LDI (landscape division index) is the probability that two randomly chosen pixels in the landscape are notin the same patch and is based on the cumulative patch area distribution The sum is across the proportionalarea of each patch of canopy cover
For each landscape and each remote sensing product we performed a landscape pattern analysis
of canopy cover using the SDM Tool package (httpsCRANR-projectorgpackage=SDMTools)
in R (R Studio httpswwwr-projectorg) SDM Tools calculates landscape metrics (similar to
FRAGSTATS [44]) and outputs values for each metric We sampled each landscape metric across five
to seven different spatial extents 10 25 50 75 100 300 600 1000 ha We performed 100 Markov
Chain Monte Carlo (MCMC) simulations to sample each landscape at these multiple extents Our three
Forests 2019 10 465 7 of 21
landscapes varied in size therefore all three landscapes did not get sampled at the largest extents
Powell Plateau was sampled 10 handash300 ha Tepeats Creek was sampled 10 handash600 ha and Windmill
Draw was sampled 10 handash1000 ha (Figure 2)
3 Results
General patterns of patch characteristics were observed across spatial extents and between the four
data products (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) that match our understanding of
expected changes of patches with scale and grain This study more specifically tested readily available
remotely sensed data products to assess their consistency and efficacy for quantifying key characteristics
of forest canopy and sensitivity to change due to forest management or diversity Here we report the
differences in data products (grain) and how the patterns change across spatial extents
31 Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics ofCanopy Cover
The proportion of landscape (PLand) comprised of canopy cover was underpredicted by NAIP
and overpredicted by Landsat-8 and Sentinel-2 across our three landscapes PLand was best predicted
by the Sentinel-2 data (10 m) which produced means most similar to the LiDAR data (1 m) on Powell
Plateau our remnant functional landscape with an intact natural disturbance regime (Table 2 Figure 3)
NAIP (1 m) while still underestimated effective1y best characterized PLand on Windmill Draw
a highly managed forest landscape experiencing both fire exclusion and extensive timber management
Landsat-8 (30 m) overestimated PLand on all three landscapes
Table 2 Proportion of landscape (PLand) comprised of canopy cover across each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
Mean values and standard error (SE) are shown
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80633
(0019)0619
(0017)0635
(0013)0648
(0011)0660
(0009)0584
(0000)
Sentinel-20568
(0011)0559
(0010)0569
(0006)0578
(0005)0577
(0004)0550
(0000)
NAIP0314
(0006)0319
(0005)0330
(0003)0332
(0003)0334
(0003)0329
(0000)
LiDAR0537
(0010)0535
(0007)0552
(0006)0560
(0005)0560
(0004)0543
(0000)
Tepeats Creek
Landsat-80743
(0013)0734
(0011)0742
(0009)0732
(0009)0737
(0008)0744
(0003)0748
(0001)
Sentinel-20824
(0011)0797
(0009)0807
(0008)0795
(0008)0796
(0007)0790
(0003)0797
(0000)
NAIP0439
(0007)0422
(0006)0427
(0005)0418
(0004)0417
(0004)0420
(0001)0418
(0000)
LiDAR0596
(0010)0571
(0008)0565
(0008)0560
(0007)0550
(0007)0546
(0002)0568
(0001)
Forests 2019 10 465 8 of 21
Table 2 Cont
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80835
(0010)0826
(0009)0832
(0007)0824
(0006)0838
(0005)0826
(0002)0828
(0001)0831
(0000)
Sentinel-20832
(0009)0819
(0009)0828
(0007)0816
(0007)0824
(0006)0813
(0002)0814
(0001)0809
(0001)
NAIP0356
(0008)0336
(0007)0347
(0006)0339
(0006)0341
(0005)0321
(0002)0321
(0001)0318
(0000)
LiDAR0492
(0010)0473
(0009)0484
(0008)0476
(0008)0480
(0007)0461
(0002)0467
(0001)0470
(0000)
Figure 3 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for the proportion of landscape (ha) under forested canopy cover for each landscape and data product
(Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets
with rescaled y-axis provided for clarity
The number of patches of canopy cover or patch density (PD) was not accurately predicted
by any of our spatial products across landscapes or spatial extents NAIP at 1 m resolution highly
over-predicted the patch density by 25 to 3 times across all three landscapes (Table 3 Figure 4)
The small pixel size can fragment patches that the LiDAR point cloud keeps aggregated thus resulting
in many more patches across the landscape at all extents Landsat (30 m) and Sentinel-2 (10 m) highly
underestimated PD at their native cell resolutions Mean Patch Area (MPA) the average area composed
of patches of canopy cover was highly overestimated (one to two orders of magnitude) by Landsat
and Sentinel-2 as compared to LiDAR (Table 4 Figure 5) NAIP underestimated mean patch area by
three to four times on all landscapes across all spatial extents
Forests 2019 10 465 9 of 21
Table 3 Patch density (PD) the average number of patches of canopy cover on each landscape
delineated by each data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Patch Density (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80214
(0015)0138
(0011)0090
(0006)0063
(0004)0061
(0004)0075
(0000)
Sentinel-21047
(0074)0883
(0053)0706
(0035)0597
(0028)0590
(0025)0673
(0002)
NAIP57969(1079)
56659(0695)
53808(0516)
53300(0499)
52735(0448)
50537(0025)
LiDAR15428(0661)
15170(0536)
13349(0377)
12601(0352)
12375(0302)
12950(0011)
Tepeats Creek
Landsat-80153
(0010)0085
(0007)0051
(0004)0041
(0003)0038
(0003)0019
(0001)0011
(0000)
Sentinel-20310
(0032)0259
(0023)0189
(0017)0207
(0015)0185
(0014)0182
(0005)0157
(0000)
NAIP41339(1283)
40980(1027)
39345(0835)
40110(0723)
39728(0626)
37624(0212)
34257(0041)
LiDAR15310(0750)
16167(0617)
16325(0616)
16551(0526)
16973(0487)
16740(0135)
15306(0022)
Windmill Draw
Landsat-80138
(0007)0056
(0004)0032
(0002)0030
(0003)0021
(0002)0013
(0001)0011
(0000)0008
(0000)
Sentinel-20205
(0018)0153
(0018)0099
(0009)0104
(0008)0081
(0006)0073
(0002)0072
(0001)0067
(0000)
NAIP52483(1502)
54286(1227)
52324(1362)
52001(1231)
52385(1146)
55683(0579)
54084(0352)
51462(0104)
LiDAR23018(0853)
23109(0691)
22289(0706)
21886(0663)
21978(0618)
22724(0238)
21838(0135)
21666(0040)
Figure 4 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch density (patches per ha) Patch density is the number of patches per landscape area under
forested canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1
m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 10 of 21
Table 4 Mean Patch Area (MPA) (ha) the average area composed of patches of canopy cover across
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Mean Patch Area (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-8 4647(0299)
9474(0776)
13988(1296)
19518(1840)
20520(2052)
7819(0035)
Sentinel-2 1094(0132)
1158(0129)
1096(0069)
1245(0067)
1206(0061)
0817(0002)
NAIP 0006(0000)
0006(0000)
0006(0000)
0006(0000)
0006(0000)
0007(0000)
LiDAR 0045(0003)
0042(0002)
0046(0002)
0048(0001)
0048(0001)
0042(0000)
Tepeats Creek
Landsat-8 6351(0264)
13949(0736)
25349(1542)
31540(2152)
34500(2566)
47600(2326)
71374(1401)
Sentinel-2 5420(0344)
7333(0700)
12646(1459)
8224(1062)
7709(0663)
4834(0187)
5082(0014)
NAIP 0012(0001)
0011(0000)
0012(0000)
0011(0000)
0011(0000)
0011(0000)
0012(0000)
LiDAR 0059(0006)
0045(0003)
0046(0004)
0041(0003)
0038(0002)
0033(0000)
0037(0000)
Windmill Draw
Landsat-8 7248(0255)
18257(0611)
33437(1340)
42230(2231)
58305(2789)
95256(6883)
85781(3900)
104836(0889)
Sentinel-2 6083(0302)
11371(0782)
17674(1437)
19018(2053)
20103(2224)
12738(0508)
11511(0152)
12085(0100)
NAIP 0008(0001)
0007(0000)
0008(0000)
0008(0000)
0007(0000)
0006(0000)
0006(0000)
0006(0000)
LiDAR 0035(0005)
0028(0004)
0030(0004)
0029(0003)
0028(0002)
0021(0000)
0022(0000)
0022(0000)
Figure 5 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for mean patch area (ha) (average size of patches) for each landscape and data product (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis
provided for clarity
Forests 2019 10 465 11 of 21
Canopy cover was highly aggregated an attribute that was detected by all four data products
across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately
on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8
best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and
Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and
across all extents especially on Powell Plateau
Figure 6 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are
to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8
30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and
standard error (SE) are shown
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-876761(1175)
76553(0986)
77397(0802)
78012(0712)
78950(0581)
74823(0021)
Sentinel-269008(0748)
68585(0636)
68873(0473)
69394(0406)
69600(0323)
66906(0009)
NAIP77402(0246)
77603(0191)
78206(0113)
78239(0122)
78429(0105)
78773(0003)
LiDAR91447(0146)
91467(0106)
91810(0074)
91921(0070)
91941(0063)
91810(0001)
Tepeats Creek
Landsat-882820(0625)
82027(0543)
81855(0490)
81684(0425)
81706(0426)
82238(0139)
82418(0047)
Sentinel-290755(0471)
89497(0390)
90100(0311)
89688(0269)
89613(0221)
89824(0094)
90086(0012)
NAIP81195(0239)
81281(0183)
81362(0150)
81272(0137)
81256(0121)
81784(0047)
81914(0013)
LiDAR91847(0181)
91468(0144)
91330(0146)
91286(0127)
91147(0118)
91083(0041)
91506(0013)
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 3 of 21
extent affect canopy cover patch characteristics We specifically asked 1) How do readily available
and repeatable remotely sensed productsmdashLandsat-8 Sentinel-2 the National Agriculture Imagery
Program and LiDARmdashthat are at different spatial grains compare in their ability to accurately predict
canopy cover and 2) how do forest canopy patch metrics vary across remotely sensed data products
and spatial extents
2 Materials and Methods
21 Study Area
We examined landscape patterns of canopy cover across three ponderosa pinegambel oak
(Pinus ponderosa var scopulorum EngelmQuercus gambelii Nutt) forests spanning a gradient of
intact un-managed ponderosa pine forest to a highly managed forest in northern Arizona USA
Our three landscapes were Powell Plateau and Tepeats Creek near the North Rim of the Grand Canyon
and Windmill DrawJacks Canyon located on the Mogollon Rim (Figure 1) Powell Plateau (~1800 ha
in size) represents a relict landscape of ponderosa pine and oak on limestone soils that has remained a
relatively intact functional landscape with a natural fire disturbance history into the 20th century [28]
Tepeats Creek (~2100 ha) represents a ponderosa pine and oak forest on limestone soils with fire
exclusion and timber management since the late 19th century and Windmill DrawJacks Canyon
(~2400 ha hereafter referred to as ldquoWindmill Drawrdquo) represents a ponderosa pine and oak forest on
basalt soils with fire exclusion and timber management since the late 19th century both typical of
current southwestern forest conditions [29]
Figure 1 Map of study landscapes in northern Arizona USA Powell Plateau and Tepeats Creek on the
North Rim of the Grand Canyon and Windmill DrawJacks Canyon on the Mogollon Rim
22 Remotely Sensed Data
We used remotely sensed data from four sources to examine patterns of canopy cover across
our three study landscapes We used Landsat-8 data (30 m cell resolution) Sentinel-2 data (10 m
cell resolution) orthographic aerial imagery from the National Agriculture Imagery Program (NAIP)
(1 m cell resolution) and aerial LiDAR data (1 m cell resolution) across our three landscapes In this
analysis we compared landscape metrics calculated from these four spatial products (ie different
Forests 2019 10 465 4 of 21
grainsmdashLandsat-8 Sentinel-2 NAIP and LiDAR) and then make comparisons across landscapes
The Landsat and Sentinel data are easily available to land management agencies and are released
relatively often while NAIP imagery is released every two to four years depending on location LiDAR
although more accurate is currently at a higher cost and flown by individual agencies or coalitions
of natural resource management stakeholders Currently it is not consistently collected across the
nation but the USGS 3D Elevation Program (3DEP) has set a goal to complete nationwide LiDAR data
coverage by 2023 [30] Our objective was to address how remotely sensed canopy cover patch metrics
vary over multiple spatial extents and how the grain (cell resolution) of normally used products affects
landscape metrics commonly used to assess restoration success and thus patterns detected
Airborne LiDAR data were used to create a digital elevation model (DEM) digital surface model
(DSM) and a canopy height model (CHM) for each site at 1 m spatial resolution The Airborne LiDAR
data were collected over two acquisition periods AugustndashSept 2012 for Powell and Tepeats Creek and
AugustndashSept 2013 for Windmill Draw The data were requisitioned by the USDA Forest Service and
acquired by Quantum Spatial using Leica ALS50 Phase II and ALS60 systems either dual mounted
or separately in a Cessna Caravan and a Partenavia fixed-wing aircraft respectively The sensor
was flown at an altitude of 900 m aboveground with ge50 side-lap (ge100 overlap) resulting in an
average volumetric point-density of ge8 pulses per m2 These data were then classified as ground or
non-ground by the vendor using TerraScan v12004 and v13008 which uses the Axelsson algorithm
to classify the DTM points [31] The horizontal and vertical positioning errors in the point cloud data
were le15 cm Resulting CHM pixels above 2 m were classified as either canopy or not canopy with no
effort undertaken to differentiate between deciduous and coniferous vegetation
Additionally we utilized National Agriculture Imagery Program (NAIP) imagery collected for
Arizona in 2015 This imagery is available free via Google Earth Engine (GEE) [32] and includes four
bands (red green blue and infrared) with a 1 m ground sample distance At a minimum the horizontal
accuracy of the imagery must be within 6 m and cloud cover must be less than 10 per quarter USGS
topographic quadrangle tile NAIP Imagery was loaded into R (httpswwwr-projectorg) using
the raster package (httpscranr-projectorgwebpackagesraster) and a ratio band was calculated
by dividing the near-infrared and red (bands 4 and 1 respectively) [33] The ratio band was merged
with the red green blue and near-infrared bands Test classifications indicated that 25 classes limited
confusion between tree canopy and forest openings and a 25-class unsupervised classification was
run using the RStoolbox package (httpscranr-projectorgwebpackagesRStoolbox) and then each
class was evaluated by overlaying with LiDAR CHM to verify canopy and not canopy classes We
then classified the resulting layer into a binary map of canopy and not canopy cover at 1 m spatial
resolution and georeferenced this layer to the LiDAR CHM We then generated 1000 random points
within each landscape and performed an accuracy assessment using the LiDAR classified canopy and
not canopy image as truth Resulting overall classification accuracies were 633 618 and 763 for
Powell Tepeats Creek and Windmill Draw areas respectively
Sentinel-2 is part of the Copernicus Earth Observation Program of the European Union We utilized
cloud-free (lt10) fall imagery (October 2015 for Windmill Draw and August 2016 for Powell plateau
and Tepeats Creek) from Sentinel-2 were used to characterize canopy cover across the study area
The satellite is equipped with a wide swath multi spectral instrument (MSI) which collects near-nadir
imagery in 13 spectral bands (coastal aerosol red green blue three red edge two near-infrared
water vapor and three shortwave infrared) ranging from 10 to 60 m resolution [34] For this
study we used the 10 m resolution visible and near-infrared bands and each Sentinel-2 image was
atmospherically corrected to bottom of atmosphere reflectance using the Sen2Cor algorithm provided
by the European Space Agency Sentinel Application Platform [35] Using the same approach as we
used with the NAIP imagery we performed a 25-class unsupervised classification with the resulting
image being reclassified into a binary map of canopy and not canopy cover at 10 m spatial resolution
To perform an accuracy assessment with this product we aggregated the LiDAR canopy and not canopy
image to 10 m using the raster package and reclassified pixels to canopy where ge50 (an approximate
Forests 2019 10 465 5 of 21
non-linear interpolation based on the FAO classification using 10 cover at 05 ha [36] Hansen et alrsquos
classification of cover using 25 at 30 m [37] and an assumed 90 cover at 1 m for LiDAR) of the
LiDAR canopy image pixels were classified as canopy This layer was then compared to the classified
Sentinel-2 canopy image using 1000 randomly generated points and resulting overall classification
accuracies were 724 568 and 631 for Powell Tepeats Creek and Windmill Draw respectively
Lastly Landsat-8 imagery was used to assess canopy cover at the coarsest grain 30 m spatial
resolution We obtained Landsat-8 operational land imagery from the fall (OctoberndashNovember) 2015
These images contain seven bands (five visible and near-infrared bands and two short-wave infrared)
processed to orthorectified surface reflectance by GEE using the Landsat surface reflectance code
(LaSRC) which makes use of the coastal aerosol band to perform aerosol inversion tests uses auxiliary
climate data from MODIS and uses a unique radiative transfer model We used a cloud cloud-shadow
and cirrus cloud mask included in the SR data using CFMASK [38] As with the other products
we performed a 25-class unsupervised classification with the resulting image being reclassified into
a binary map of canopy and not canopy cover at 30 m spatial resolution To perform an accuracy
assessment with this product we aggregated the LiDAR canopy and not canopy image to 30 m using
the raster package and reclassified pixels to canopy where ge25 [37] of the LiDAR canopy image
pixels were classified as canopy This layer was then compared to the classified Landsat-8 canopy
image using 1000 randomly generate points and resulting overall classification accuracies were 746
693 and 853 for Powell Tepeats Creek and Windmill Draw respectively
23 Use of LiDAR as Reference Data
Airborne LiDAR is a widely accepted method of collecting and generating precise georeferenced
spatial information about features of the earthrsquos surface shape and physical characteristics [39]
Recent advances have produced mapping technologies that allow scientists to examine environmental
features such as forests watersheds and ecosystem attributes with accuracy and precision [39]
LiDAR technology provides horizontal and vertical information at high spatial resolution and vertical
accuracies It has been used to accurately assess forest canopy metrics and other forest attributes
such as stand structure biomass and tree density [2122] LiDAR provides very detailed wall-to-wall
maps of forest canopy cover (via reclassification of the CHM to covernot cover binary maps) that
are problematic and laborious to produce with traditional field and photointerpretation methods
Furthermore validating the accuracy of LiDAR-derived canopy cover using highly uncertain field
observations should be avoided as the results of ocular and vertical sighting methods are prone to
sampling problems and are difficult to confirm [40] As a result of these uncertainties LiDAR-derived
canopy cover maps are increasingly being used as reference data for assessing accuracies of alternative
methods which typically show agreement with LiDAR-derived canopy cover estimates but are subject
to high levels of bias (underestimate low and to overestimate high canopy cover) and imprecision [41ndash43]
Given that LiDAR-derived estimates are continuous and high resolution making them useful in forest
monitoring and management we chose to use LiDAR-derived metrics as reference data and compare
the performance of the three other spatially derived products to LiDAR
For each landscape the four data products that vary in grain (Landsat-8 30 m Sentinel-2 10 m
NAIP 1 m and LiDAR 1 m) were compared over multiple spatial extents (Figure 2) For all four
data products each patch was delineated by contiguous pixels of canopy cover and we used the
eight neighbor rule for cell-based indices- meaning contiguous cells were defined as those that were
either directly or adjacently contiguous After classification of each data layer into canopynot canopy
we used landscape metrics to quantify key characteristics of forest canopy patches and to quantify forest
cover patterns and make comparisons among different landscapes A total of six patch metrics were
examined to explain the variability of patches across landscapes of different extent (Table 1) With our
simple binary classification the proportion of landscape (PLand) patch density (PD) and mean patch
area (MPA) are basic descriptors of the canopy distribution These characteristics inform the descriptors
Forests 2019 10 465 6 of 21
of spatial connectedness and distribution including the aggregation index (AI) patch cohesion index
(PCI) and landscape division index (LDI)
Figure 2 Remotely sensed data products and associated grain of each data source Landsat-8 (30 m)
Sentinel-2 (10 m) NAIP (1 m) and LiDAR (1 m) The top panel (a) shows the canopy cover of Windmill
Draw landscape with the blue areas representing the relative size of each extent (10ndash1000 ha) analyzed
The lower panel (b) shows size of the spatial grain (resolution) and level of information available for
each data product within an example extent
Table 1 Description of landscape metrics evaluated across grain and extent on three landscapes
in northern Arizona USA Metrics derived from FRAGSTATS (wwwumassedulandecoresearch
fragstatsfragstatshtml)
Metric
bull PLand (proportion of landscape) is the total proportion of the patchmdashcanopy covermdashacross each landscape
bull PD (patch density) is the number of patches of canopy cover per unit area hectares in this analysis
bull MPA (mean patch area) is the average area of patches (ha) composed of canopy cover across each landscape
bull AI (aggregation index) is the area of canopy cover on the landscape that has like adjacencies of canopyclumped into a compact patch and is weighted proportionally by its area in the landscape It is a landscapemetric of how aggregated canopy cover is at each extent
bull PCI (patch cohesion index) is a connectivity metric that measures the physical connectedness of thecorresponding patch type Patch cohesion increases as canopy cover becomes more clumped or aggregated inits distribution and more physically connected
bull LDI (landscape division index) is the probability that two randomly chosen pixels in the landscape are notin the same patch and is based on the cumulative patch area distribution The sum is across the proportionalarea of each patch of canopy cover
For each landscape and each remote sensing product we performed a landscape pattern analysis
of canopy cover using the SDM Tool package (httpsCRANR-projectorgpackage=SDMTools)
in R (R Studio httpswwwr-projectorg) SDM Tools calculates landscape metrics (similar to
FRAGSTATS [44]) and outputs values for each metric We sampled each landscape metric across five
to seven different spatial extents 10 25 50 75 100 300 600 1000 ha We performed 100 Markov
Chain Monte Carlo (MCMC) simulations to sample each landscape at these multiple extents Our three
Forests 2019 10 465 7 of 21
landscapes varied in size therefore all three landscapes did not get sampled at the largest extents
Powell Plateau was sampled 10 handash300 ha Tepeats Creek was sampled 10 handash600 ha and Windmill
Draw was sampled 10 handash1000 ha (Figure 2)
3 Results
General patterns of patch characteristics were observed across spatial extents and between the four
data products (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) that match our understanding of
expected changes of patches with scale and grain This study more specifically tested readily available
remotely sensed data products to assess their consistency and efficacy for quantifying key characteristics
of forest canopy and sensitivity to change due to forest management or diversity Here we report the
differences in data products (grain) and how the patterns change across spatial extents
31 Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics ofCanopy Cover
The proportion of landscape (PLand) comprised of canopy cover was underpredicted by NAIP
and overpredicted by Landsat-8 and Sentinel-2 across our three landscapes PLand was best predicted
by the Sentinel-2 data (10 m) which produced means most similar to the LiDAR data (1 m) on Powell
Plateau our remnant functional landscape with an intact natural disturbance regime (Table 2 Figure 3)
NAIP (1 m) while still underestimated effective1y best characterized PLand on Windmill Draw
a highly managed forest landscape experiencing both fire exclusion and extensive timber management
Landsat-8 (30 m) overestimated PLand on all three landscapes
Table 2 Proportion of landscape (PLand) comprised of canopy cover across each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
Mean values and standard error (SE) are shown
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80633
(0019)0619
(0017)0635
(0013)0648
(0011)0660
(0009)0584
(0000)
Sentinel-20568
(0011)0559
(0010)0569
(0006)0578
(0005)0577
(0004)0550
(0000)
NAIP0314
(0006)0319
(0005)0330
(0003)0332
(0003)0334
(0003)0329
(0000)
LiDAR0537
(0010)0535
(0007)0552
(0006)0560
(0005)0560
(0004)0543
(0000)
Tepeats Creek
Landsat-80743
(0013)0734
(0011)0742
(0009)0732
(0009)0737
(0008)0744
(0003)0748
(0001)
Sentinel-20824
(0011)0797
(0009)0807
(0008)0795
(0008)0796
(0007)0790
(0003)0797
(0000)
NAIP0439
(0007)0422
(0006)0427
(0005)0418
(0004)0417
(0004)0420
(0001)0418
(0000)
LiDAR0596
(0010)0571
(0008)0565
(0008)0560
(0007)0550
(0007)0546
(0002)0568
(0001)
Forests 2019 10 465 8 of 21
Table 2 Cont
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80835
(0010)0826
(0009)0832
(0007)0824
(0006)0838
(0005)0826
(0002)0828
(0001)0831
(0000)
Sentinel-20832
(0009)0819
(0009)0828
(0007)0816
(0007)0824
(0006)0813
(0002)0814
(0001)0809
(0001)
NAIP0356
(0008)0336
(0007)0347
(0006)0339
(0006)0341
(0005)0321
(0002)0321
(0001)0318
(0000)
LiDAR0492
(0010)0473
(0009)0484
(0008)0476
(0008)0480
(0007)0461
(0002)0467
(0001)0470
(0000)
Figure 3 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for the proportion of landscape (ha) under forested canopy cover for each landscape and data product
(Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets
with rescaled y-axis provided for clarity
The number of patches of canopy cover or patch density (PD) was not accurately predicted
by any of our spatial products across landscapes or spatial extents NAIP at 1 m resolution highly
over-predicted the patch density by 25 to 3 times across all three landscapes (Table 3 Figure 4)
The small pixel size can fragment patches that the LiDAR point cloud keeps aggregated thus resulting
in many more patches across the landscape at all extents Landsat (30 m) and Sentinel-2 (10 m) highly
underestimated PD at their native cell resolutions Mean Patch Area (MPA) the average area composed
of patches of canopy cover was highly overestimated (one to two orders of magnitude) by Landsat
and Sentinel-2 as compared to LiDAR (Table 4 Figure 5) NAIP underestimated mean patch area by
three to four times on all landscapes across all spatial extents
Forests 2019 10 465 9 of 21
Table 3 Patch density (PD) the average number of patches of canopy cover on each landscape
delineated by each data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Patch Density (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80214
(0015)0138
(0011)0090
(0006)0063
(0004)0061
(0004)0075
(0000)
Sentinel-21047
(0074)0883
(0053)0706
(0035)0597
(0028)0590
(0025)0673
(0002)
NAIP57969(1079)
56659(0695)
53808(0516)
53300(0499)
52735(0448)
50537(0025)
LiDAR15428(0661)
15170(0536)
13349(0377)
12601(0352)
12375(0302)
12950(0011)
Tepeats Creek
Landsat-80153
(0010)0085
(0007)0051
(0004)0041
(0003)0038
(0003)0019
(0001)0011
(0000)
Sentinel-20310
(0032)0259
(0023)0189
(0017)0207
(0015)0185
(0014)0182
(0005)0157
(0000)
NAIP41339(1283)
40980(1027)
39345(0835)
40110(0723)
39728(0626)
37624(0212)
34257(0041)
LiDAR15310(0750)
16167(0617)
16325(0616)
16551(0526)
16973(0487)
16740(0135)
15306(0022)
Windmill Draw
Landsat-80138
(0007)0056
(0004)0032
(0002)0030
(0003)0021
(0002)0013
(0001)0011
(0000)0008
(0000)
Sentinel-20205
(0018)0153
(0018)0099
(0009)0104
(0008)0081
(0006)0073
(0002)0072
(0001)0067
(0000)
NAIP52483(1502)
54286(1227)
52324(1362)
52001(1231)
52385(1146)
55683(0579)
54084(0352)
51462(0104)
LiDAR23018(0853)
23109(0691)
22289(0706)
21886(0663)
21978(0618)
22724(0238)
21838(0135)
21666(0040)
Figure 4 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch density (patches per ha) Patch density is the number of patches per landscape area under
forested canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1
m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 10 of 21
Table 4 Mean Patch Area (MPA) (ha) the average area composed of patches of canopy cover across
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Mean Patch Area (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-8 4647(0299)
9474(0776)
13988(1296)
19518(1840)
20520(2052)
7819(0035)
Sentinel-2 1094(0132)
1158(0129)
1096(0069)
1245(0067)
1206(0061)
0817(0002)
NAIP 0006(0000)
0006(0000)
0006(0000)
0006(0000)
0006(0000)
0007(0000)
LiDAR 0045(0003)
0042(0002)
0046(0002)
0048(0001)
0048(0001)
0042(0000)
Tepeats Creek
Landsat-8 6351(0264)
13949(0736)
25349(1542)
31540(2152)
34500(2566)
47600(2326)
71374(1401)
Sentinel-2 5420(0344)
7333(0700)
12646(1459)
8224(1062)
7709(0663)
4834(0187)
5082(0014)
NAIP 0012(0001)
0011(0000)
0012(0000)
0011(0000)
0011(0000)
0011(0000)
0012(0000)
LiDAR 0059(0006)
0045(0003)
0046(0004)
0041(0003)
0038(0002)
0033(0000)
0037(0000)
Windmill Draw
Landsat-8 7248(0255)
18257(0611)
33437(1340)
42230(2231)
58305(2789)
95256(6883)
85781(3900)
104836(0889)
Sentinel-2 6083(0302)
11371(0782)
17674(1437)
19018(2053)
20103(2224)
12738(0508)
11511(0152)
12085(0100)
NAIP 0008(0001)
0007(0000)
0008(0000)
0008(0000)
0007(0000)
0006(0000)
0006(0000)
0006(0000)
LiDAR 0035(0005)
0028(0004)
0030(0004)
0029(0003)
0028(0002)
0021(0000)
0022(0000)
0022(0000)
Figure 5 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for mean patch area (ha) (average size of patches) for each landscape and data product (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis
provided for clarity
Forests 2019 10 465 11 of 21
Canopy cover was highly aggregated an attribute that was detected by all four data products
across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately
on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8
best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and
Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and
across all extents especially on Powell Plateau
Figure 6 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are
to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8
30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and
standard error (SE) are shown
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-876761(1175)
76553(0986)
77397(0802)
78012(0712)
78950(0581)
74823(0021)
Sentinel-269008(0748)
68585(0636)
68873(0473)
69394(0406)
69600(0323)
66906(0009)
NAIP77402(0246)
77603(0191)
78206(0113)
78239(0122)
78429(0105)
78773(0003)
LiDAR91447(0146)
91467(0106)
91810(0074)
91921(0070)
91941(0063)
91810(0001)
Tepeats Creek
Landsat-882820(0625)
82027(0543)
81855(0490)
81684(0425)
81706(0426)
82238(0139)
82418(0047)
Sentinel-290755(0471)
89497(0390)
90100(0311)
89688(0269)
89613(0221)
89824(0094)
90086(0012)
NAIP81195(0239)
81281(0183)
81362(0150)
81272(0137)
81256(0121)
81784(0047)
81914(0013)
LiDAR91847(0181)
91468(0144)
91330(0146)
91286(0127)
91147(0118)
91083(0041)
91506(0013)
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 4 of 21
grainsmdashLandsat-8 Sentinel-2 NAIP and LiDAR) and then make comparisons across landscapes
The Landsat and Sentinel data are easily available to land management agencies and are released
relatively often while NAIP imagery is released every two to four years depending on location LiDAR
although more accurate is currently at a higher cost and flown by individual agencies or coalitions
of natural resource management stakeholders Currently it is not consistently collected across the
nation but the USGS 3D Elevation Program (3DEP) has set a goal to complete nationwide LiDAR data
coverage by 2023 [30] Our objective was to address how remotely sensed canopy cover patch metrics
vary over multiple spatial extents and how the grain (cell resolution) of normally used products affects
landscape metrics commonly used to assess restoration success and thus patterns detected
Airborne LiDAR data were used to create a digital elevation model (DEM) digital surface model
(DSM) and a canopy height model (CHM) for each site at 1 m spatial resolution The Airborne LiDAR
data were collected over two acquisition periods AugustndashSept 2012 for Powell and Tepeats Creek and
AugustndashSept 2013 for Windmill Draw The data were requisitioned by the USDA Forest Service and
acquired by Quantum Spatial using Leica ALS50 Phase II and ALS60 systems either dual mounted
or separately in a Cessna Caravan and a Partenavia fixed-wing aircraft respectively The sensor
was flown at an altitude of 900 m aboveground with ge50 side-lap (ge100 overlap) resulting in an
average volumetric point-density of ge8 pulses per m2 These data were then classified as ground or
non-ground by the vendor using TerraScan v12004 and v13008 which uses the Axelsson algorithm
to classify the DTM points [31] The horizontal and vertical positioning errors in the point cloud data
were le15 cm Resulting CHM pixels above 2 m were classified as either canopy or not canopy with no
effort undertaken to differentiate between deciduous and coniferous vegetation
Additionally we utilized National Agriculture Imagery Program (NAIP) imagery collected for
Arizona in 2015 This imagery is available free via Google Earth Engine (GEE) [32] and includes four
bands (red green blue and infrared) with a 1 m ground sample distance At a minimum the horizontal
accuracy of the imagery must be within 6 m and cloud cover must be less than 10 per quarter USGS
topographic quadrangle tile NAIP Imagery was loaded into R (httpswwwr-projectorg) using
the raster package (httpscranr-projectorgwebpackagesraster) and a ratio band was calculated
by dividing the near-infrared and red (bands 4 and 1 respectively) [33] The ratio band was merged
with the red green blue and near-infrared bands Test classifications indicated that 25 classes limited
confusion between tree canopy and forest openings and a 25-class unsupervised classification was
run using the RStoolbox package (httpscranr-projectorgwebpackagesRStoolbox) and then each
class was evaluated by overlaying with LiDAR CHM to verify canopy and not canopy classes We
then classified the resulting layer into a binary map of canopy and not canopy cover at 1 m spatial
resolution and georeferenced this layer to the LiDAR CHM We then generated 1000 random points
within each landscape and performed an accuracy assessment using the LiDAR classified canopy and
not canopy image as truth Resulting overall classification accuracies were 633 618 and 763 for
Powell Tepeats Creek and Windmill Draw areas respectively
Sentinel-2 is part of the Copernicus Earth Observation Program of the European Union We utilized
cloud-free (lt10) fall imagery (October 2015 for Windmill Draw and August 2016 for Powell plateau
and Tepeats Creek) from Sentinel-2 were used to characterize canopy cover across the study area
The satellite is equipped with a wide swath multi spectral instrument (MSI) which collects near-nadir
imagery in 13 spectral bands (coastal aerosol red green blue three red edge two near-infrared
water vapor and three shortwave infrared) ranging from 10 to 60 m resolution [34] For this
study we used the 10 m resolution visible and near-infrared bands and each Sentinel-2 image was
atmospherically corrected to bottom of atmosphere reflectance using the Sen2Cor algorithm provided
by the European Space Agency Sentinel Application Platform [35] Using the same approach as we
used with the NAIP imagery we performed a 25-class unsupervised classification with the resulting
image being reclassified into a binary map of canopy and not canopy cover at 10 m spatial resolution
To perform an accuracy assessment with this product we aggregated the LiDAR canopy and not canopy
image to 10 m using the raster package and reclassified pixels to canopy where ge50 (an approximate
Forests 2019 10 465 5 of 21
non-linear interpolation based on the FAO classification using 10 cover at 05 ha [36] Hansen et alrsquos
classification of cover using 25 at 30 m [37] and an assumed 90 cover at 1 m for LiDAR) of the
LiDAR canopy image pixels were classified as canopy This layer was then compared to the classified
Sentinel-2 canopy image using 1000 randomly generated points and resulting overall classification
accuracies were 724 568 and 631 for Powell Tepeats Creek and Windmill Draw respectively
Lastly Landsat-8 imagery was used to assess canopy cover at the coarsest grain 30 m spatial
resolution We obtained Landsat-8 operational land imagery from the fall (OctoberndashNovember) 2015
These images contain seven bands (five visible and near-infrared bands and two short-wave infrared)
processed to orthorectified surface reflectance by GEE using the Landsat surface reflectance code
(LaSRC) which makes use of the coastal aerosol band to perform aerosol inversion tests uses auxiliary
climate data from MODIS and uses a unique radiative transfer model We used a cloud cloud-shadow
and cirrus cloud mask included in the SR data using CFMASK [38] As with the other products
we performed a 25-class unsupervised classification with the resulting image being reclassified into
a binary map of canopy and not canopy cover at 30 m spatial resolution To perform an accuracy
assessment with this product we aggregated the LiDAR canopy and not canopy image to 30 m using
the raster package and reclassified pixels to canopy where ge25 [37] of the LiDAR canopy image
pixels were classified as canopy This layer was then compared to the classified Landsat-8 canopy
image using 1000 randomly generate points and resulting overall classification accuracies were 746
693 and 853 for Powell Tepeats Creek and Windmill Draw respectively
23 Use of LiDAR as Reference Data
Airborne LiDAR is a widely accepted method of collecting and generating precise georeferenced
spatial information about features of the earthrsquos surface shape and physical characteristics [39]
Recent advances have produced mapping technologies that allow scientists to examine environmental
features such as forests watersheds and ecosystem attributes with accuracy and precision [39]
LiDAR technology provides horizontal and vertical information at high spatial resolution and vertical
accuracies It has been used to accurately assess forest canopy metrics and other forest attributes
such as stand structure biomass and tree density [2122] LiDAR provides very detailed wall-to-wall
maps of forest canopy cover (via reclassification of the CHM to covernot cover binary maps) that
are problematic and laborious to produce with traditional field and photointerpretation methods
Furthermore validating the accuracy of LiDAR-derived canopy cover using highly uncertain field
observations should be avoided as the results of ocular and vertical sighting methods are prone to
sampling problems and are difficult to confirm [40] As a result of these uncertainties LiDAR-derived
canopy cover maps are increasingly being used as reference data for assessing accuracies of alternative
methods which typically show agreement with LiDAR-derived canopy cover estimates but are subject
to high levels of bias (underestimate low and to overestimate high canopy cover) and imprecision [41ndash43]
Given that LiDAR-derived estimates are continuous and high resolution making them useful in forest
monitoring and management we chose to use LiDAR-derived metrics as reference data and compare
the performance of the three other spatially derived products to LiDAR
For each landscape the four data products that vary in grain (Landsat-8 30 m Sentinel-2 10 m
NAIP 1 m and LiDAR 1 m) were compared over multiple spatial extents (Figure 2) For all four
data products each patch was delineated by contiguous pixels of canopy cover and we used the
eight neighbor rule for cell-based indices- meaning contiguous cells were defined as those that were
either directly or adjacently contiguous After classification of each data layer into canopynot canopy
we used landscape metrics to quantify key characteristics of forest canopy patches and to quantify forest
cover patterns and make comparisons among different landscapes A total of six patch metrics were
examined to explain the variability of patches across landscapes of different extent (Table 1) With our
simple binary classification the proportion of landscape (PLand) patch density (PD) and mean patch
area (MPA) are basic descriptors of the canopy distribution These characteristics inform the descriptors
Forests 2019 10 465 6 of 21
of spatial connectedness and distribution including the aggregation index (AI) patch cohesion index
(PCI) and landscape division index (LDI)
Figure 2 Remotely sensed data products and associated grain of each data source Landsat-8 (30 m)
Sentinel-2 (10 m) NAIP (1 m) and LiDAR (1 m) The top panel (a) shows the canopy cover of Windmill
Draw landscape with the blue areas representing the relative size of each extent (10ndash1000 ha) analyzed
The lower panel (b) shows size of the spatial grain (resolution) and level of information available for
each data product within an example extent
Table 1 Description of landscape metrics evaluated across grain and extent on three landscapes
in northern Arizona USA Metrics derived from FRAGSTATS (wwwumassedulandecoresearch
fragstatsfragstatshtml)
Metric
bull PLand (proportion of landscape) is the total proportion of the patchmdashcanopy covermdashacross each landscape
bull PD (patch density) is the number of patches of canopy cover per unit area hectares in this analysis
bull MPA (mean patch area) is the average area of patches (ha) composed of canopy cover across each landscape
bull AI (aggregation index) is the area of canopy cover on the landscape that has like adjacencies of canopyclumped into a compact patch and is weighted proportionally by its area in the landscape It is a landscapemetric of how aggregated canopy cover is at each extent
bull PCI (patch cohesion index) is a connectivity metric that measures the physical connectedness of thecorresponding patch type Patch cohesion increases as canopy cover becomes more clumped or aggregated inits distribution and more physically connected
bull LDI (landscape division index) is the probability that two randomly chosen pixels in the landscape are notin the same patch and is based on the cumulative patch area distribution The sum is across the proportionalarea of each patch of canopy cover
For each landscape and each remote sensing product we performed a landscape pattern analysis
of canopy cover using the SDM Tool package (httpsCRANR-projectorgpackage=SDMTools)
in R (R Studio httpswwwr-projectorg) SDM Tools calculates landscape metrics (similar to
FRAGSTATS [44]) and outputs values for each metric We sampled each landscape metric across five
to seven different spatial extents 10 25 50 75 100 300 600 1000 ha We performed 100 Markov
Chain Monte Carlo (MCMC) simulations to sample each landscape at these multiple extents Our three
Forests 2019 10 465 7 of 21
landscapes varied in size therefore all three landscapes did not get sampled at the largest extents
Powell Plateau was sampled 10 handash300 ha Tepeats Creek was sampled 10 handash600 ha and Windmill
Draw was sampled 10 handash1000 ha (Figure 2)
3 Results
General patterns of patch characteristics were observed across spatial extents and between the four
data products (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) that match our understanding of
expected changes of patches with scale and grain This study more specifically tested readily available
remotely sensed data products to assess their consistency and efficacy for quantifying key characteristics
of forest canopy and sensitivity to change due to forest management or diversity Here we report the
differences in data products (grain) and how the patterns change across spatial extents
31 Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics ofCanopy Cover
The proportion of landscape (PLand) comprised of canopy cover was underpredicted by NAIP
and overpredicted by Landsat-8 and Sentinel-2 across our three landscapes PLand was best predicted
by the Sentinel-2 data (10 m) which produced means most similar to the LiDAR data (1 m) on Powell
Plateau our remnant functional landscape with an intact natural disturbance regime (Table 2 Figure 3)
NAIP (1 m) while still underestimated effective1y best characterized PLand on Windmill Draw
a highly managed forest landscape experiencing both fire exclusion and extensive timber management
Landsat-8 (30 m) overestimated PLand on all three landscapes
Table 2 Proportion of landscape (PLand) comprised of canopy cover across each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
Mean values and standard error (SE) are shown
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80633
(0019)0619
(0017)0635
(0013)0648
(0011)0660
(0009)0584
(0000)
Sentinel-20568
(0011)0559
(0010)0569
(0006)0578
(0005)0577
(0004)0550
(0000)
NAIP0314
(0006)0319
(0005)0330
(0003)0332
(0003)0334
(0003)0329
(0000)
LiDAR0537
(0010)0535
(0007)0552
(0006)0560
(0005)0560
(0004)0543
(0000)
Tepeats Creek
Landsat-80743
(0013)0734
(0011)0742
(0009)0732
(0009)0737
(0008)0744
(0003)0748
(0001)
Sentinel-20824
(0011)0797
(0009)0807
(0008)0795
(0008)0796
(0007)0790
(0003)0797
(0000)
NAIP0439
(0007)0422
(0006)0427
(0005)0418
(0004)0417
(0004)0420
(0001)0418
(0000)
LiDAR0596
(0010)0571
(0008)0565
(0008)0560
(0007)0550
(0007)0546
(0002)0568
(0001)
Forests 2019 10 465 8 of 21
Table 2 Cont
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80835
(0010)0826
(0009)0832
(0007)0824
(0006)0838
(0005)0826
(0002)0828
(0001)0831
(0000)
Sentinel-20832
(0009)0819
(0009)0828
(0007)0816
(0007)0824
(0006)0813
(0002)0814
(0001)0809
(0001)
NAIP0356
(0008)0336
(0007)0347
(0006)0339
(0006)0341
(0005)0321
(0002)0321
(0001)0318
(0000)
LiDAR0492
(0010)0473
(0009)0484
(0008)0476
(0008)0480
(0007)0461
(0002)0467
(0001)0470
(0000)
Figure 3 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for the proportion of landscape (ha) under forested canopy cover for each landscape and data product
(Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets
with rescaled y-axis provided for clarity
The number of patches of canopy cover or patch density (PD) was not accurately predicted
by any of our spatial products across landscapes or spatial extents NAIP at 1 m resolution highly
over-predicted the patch density by 25 to 3 times across all three landscapes (Table 3 Figure 4)
The small pixel size can fragment patches that the LiDAR point cloud keeps aggregated thus resulting
in many more patches across the landscape at all extents Landsat (30 m) and Sentinel-2 (10 m) highly
underestimated PD at their native cell resolutions Mean Patch Area (MPA) the average area composed
of patches of canopy cover was highly overestimated (one to two orders of magnitude) by Landsat
and Sentinel-2 as compared to LiDAR (Table 4 Figure 5) NAIP underestimated mean patch area by
three to four times on all landscapes across all spatial extents
Forests 2019 10 465 9 of 21
Table 3 Patch density (PD) the average number of patches of canopy cover on each landscape
delineated by each data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Patch Density (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80214
(0015)0138
(0011)0090
(0006)0063
(0004)0061
(0004)0075
(0000)
Sentinel-21047
(0074)0883
(0053)0706
(0035)0597
(0028)0590
(0025)0673
(0002)
NAIP57969(1079)
56659(0695)
53808(0516)
53300(0499)
52735(0448)
50537(0025)
LiDAR15428(0661)
15170(0536)
13349(0377)
12601(0352)
12375(0302)
12950(0011)
Tepeats Creek
Landsat-80153
(0010)0085
(0007)0051
(0004)0041
(0003)0038
(0003)0019
(0001)0011
(0000)
Sentinel-20310
(0032)0259
(0023)0189
(0017)0207
(0015)0185
(0014)0182
(0005)0157
(0000)
NAIP41339(1283)
40980(1027)
39345(0835)
40110(0723)
39728(0626)
37624(0212)
34257(0041)
LiDAR15310(0750)
16167(0617)
16325(0616)
16551(0526)
16973(0487)
16740(0135)
15306(0022)
Windmill Draw
Landsat-80138
(0007)0056
(0004)0032
(0002)0030
(0003)0021
(0002)0013
(0001)0011
(0000)0008
(0000)
Sentinel-20205
(0018)0153
(0018)0099
(0009)0104
(0008)0081
(0006)0073
(0002)0072
(0001)0067
(0000)
NAIP52483(1502)
54286(1227)
52324(1362)
52001(1231)
52385(1146)
55683(0579)
54084(0352)
51462(0104)
LiDAR23018(0853)
23109(0691)
22289(0706)
21886(0663)
21978(0618)
22724(0238)
21838(0135)
21666(0040)
Figure 4 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch density (patches per ha) Patch density is the number of patches per landscape area under
forested canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1
m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 10 of 21
Table 4 Mean Patch Area (MPA) (ha) the average area composed of patches of canopy cover across
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Mean Patch Area (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-8 4647(0299)
9474(0776)
13988(1296)
19518(1840)
20520(2052)
7819(0035)
Sentinel-2 1094(0132)
1158(0129)
1096(0069)
1245(0067)
1206(0061)
0817(0002)
NAIP 0006(0000)
0006(0000)
0006(0000)
0006(0000)
0006(0000)
0007(0000)
LiDAR 0045(0003)
0042(0002)
0046(0002)
0048(0001)
0048(0001)
0042(0000)
Tepeats Creek
Landsat-8 6351(0264)
13949(0736)
25349(1542)
31540(2152)
34500(2566)
47600(2326)
71374(1401)
Sentinel-2 5420(0344)
7333(0700)
12646(1459)
8224(1062)
7709(0663)
4834(0187)
5082(0014)
NAIP 0012(0001)
0011(0000)
0012(0000)
0011(0000)
0011(0000)
0011(0000)
0012(0000)
LiDAR 0059(0006)
0045(0003)
0046(0004)
0041(0003)
0038(0002)
0033(0000)
0037(0000)
Windmill Draw
Landsat-8 7248(0255)
18257(0611)
33437(1340)
42230(2231)
58305(2789)
95256(6883)
85781(3900)
104836(0889)
Sentinel-2 6083(0302)
11371(0782)
17674(1437)
19018(2053)
20103(2224)
12738(0508)
11511(0152)
12085(0100)
NAIP 0008(0001)
0007(0000)
0008(0000)
0008(0000)
0007(0000)
0006(0000)
0006(0000)
0006(0000)
LiDAR 0035(0005)
0028(0004)
0030(0004)
0029(0003)
0028(0002)
0021(0000)
0022(0000)
0022(0000)
Figure 5 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for mean patch area (ha) (average size of patches) for each landscape and data product (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis
provided for clarity
Forests 2019 10 465 11 of 21
Canopy cover was highly aggregated an attribute that was detected by all four data products
across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately
on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8
best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and
Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and
across all extents especially on Powell Plateau
Figure 6 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are
to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8
30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and
standard error (SE) are shown
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-876761(1175)
76553(0986)
77397(0802)
78012(0712)
78950(0581)
74823(0021)
Sentinel-269008(0748)
68585(0636)
68873(0473)
69394(0406)
69600(0323)
66906(0009)
NAIP77402(0246)
77603(0191)
78206(0113)
78239(0122)
78429(0105)
78773(0003)
LiDAR91447(0146)
91467(0106)
91810(0074)
91921(0070)
91941(0063)
91810(0001)
Tepeats Creek
Landsat-882820(0625)
82027(0543)
81855(0490)
81684(0425)
81706(0426)
82238(0139)
82418(0047)
Sentinel-290755(0471)
89497(0390)
90100(0311)
89688(0269)
89613(0221)
89824(0094)
90086(0012)
NAIP81195(0239)
81281(0183)
81362(0150)
81272(0137)
81256(0121)
81784(0047)
81914(0013)
LiDAR91847(0181)
91468(0144)
91330(0146)
91286(0127)
91147(0118)
91083(0041)
91506(0013)
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 5 of 21
non-linear interpolation based on the FAO classification using 10 cover at 05 ha [36] Hansen et alrsquos
classification of cover using 25 at 30 m [37] and an assumed 90 cover at 1 m for LiDAR) of the
LiDAR canopy image pixels were classified as canopy This layer was then compared to the classified
Sentinel-2 canopy image using 1000 randomly generated points and resulting overall classification
accuracies were 724 568 and 631 for Powell Tepeats Creek and Windmill Draw respectively
Lastly Landsat-8 imagery was used to assess canopy cover at the coarsest grain 30 m spatial
resolution We obtained Landsat-8 operational land imagery from the fall (OctoberndashNovember) 2015
These images contain seven bands (five visible and near-infrared bands and two short-wave infrared)
processed to orthorectified surface reflectance by GEE using the Landsat surface reflectance code
(LaSRC) which makes use of the coastal aerosol band to perform aerosol inversion tests uses auxiliary
climate data from MODIS and uses a unique radiative transfer model We used a cloud cloud-shadow
and cirrus cloud mask included in the SR data using CFMASK [38] As with the other products
we performed a 25-class unsupervised classification with the resulting image being reclassified into
a binary map of canopy and not canopy cover at 30 m spatial resolution To perform an accuracy
assessment with this product we aggregated the LiDAR canopy and not canopy image to 30 m using
the raster package and reclassified pixels to canopy where ge25 [37] of the LiDAR canopy image
pixels were classified as canopy This layer was then compared to the classified Landsat-8 canopy
image using 1000 randomly generate points and resulting overall classification accuracies were 746
693 and 853 for Powell Tepeats Creek and Windmill Draw respectively
23 Use of LiDAR as Reference Data
Airborne LiDAR is a widely accepted method of collecting and generating precise georeferenced
spatial information about features of the earthrsquos surface shape and physical characteristics [39]
Recent advances have produced mapping technologies that allow scientists to examine environmental
features such as forests watersheds and ecosystem attributes with accuracy and precision [39]
LiDAR technology provides horizontal and vertical information at high spatial resolution and vertical
accuracies It has been used to accurately assess forest canopy metrics and other forest attributes
such as stand structure biomass and tree density [2122] LiDAR provides very detailed wall-to-wall
maps of forest canopy cover (via reclassification of the CHM to covernot cover binary maps) that
are problematic and laborious to produce with traditional field and photointerpretation methods
Furthermore validating the accuracy of LiDAR-derived canopy cover using highly uncertain field
observations should be avoided as the results of ocular and vertical sighting methods are prone to
sampling problems and are difficult to confirm [40] As a result of these uncertainties LiDAR-derived
canopy cover maps are increasingly being used as reference data for assessing accuracies of alternative
methods which typically show agreement with LiDAR-derived canopy cover estimates but are subject
to high levels of bias (underestimate low and to overestimate high canopy cover) and imprecision [41ndash43]
Given that LiDAR-derived estimates are continuous and high resolution making them useful in forest
monitoring and management we chose to use LiDAR-derived metrics as reference data and compare
the performance of the three other spatially derived products to LiDAR
For each landscape the four data products that vary in grain (Landsat-8 30 m Sentinel-2 10 m
NAIP 1 m and LiDAR 1 m) were compared over multiple spatial extents (Figure 2) For all four
data products each patch was delineated by contiguous pixels of canopy cover and we used the
eight neighbor rule for cell-based indices- meaning contiguous cells were defined as those that were
either directly or adjacently contiguous After classification of each data layer into canopynot canopy
we used landscape metrics to quantify key characteristics of forest canopy patches and to quantify forest
cover patterns and make comparisons among different landscapes A total of six patch metrics were
examined to explain the variability of patches across landscapes of different extent (Table 1) With our
simple binary classification the proportion of landscape (PLand) patch density (PD) and mean patch
area (MPA) are basic descriptors of the canopy distribution These characteristics inform the descriptors
Forests 2019 10 465 6 of 21
of spatial connectedness and distribution including the aggregation index (AI) patch cohesion index
(PCI) and landscape division index (LDI)
Figure 2 Remotely sensed data products and associated grain of each data source Landsat-8 (30 m)
Sentinel-2 (10 m) NAIP (1 m) and LiDAR (1 m) The top panel (a) shows the canopy cover of Windmill
Draw landscape with the blue areas representing the relative size of each extent (10ndash1000 ha) analyzed
The lower panel (b) shows size of the spatial grain (resolution) and level of information available for
each data product within an example extent
Table 1 Description of landscape metrics evaluated across grain and extent on three landscapes
in northern Arizona USA Metrics derived from FRAGSTATS (wwwumassedulandecoresearch
fragstatsfragstatshtml)
Metric
bull PLand (proportion of landscape) is the total proportion of the patchmdashcanopy covermdashacross each landscape
bull PD (patch density) is the number of patches of canopy cover per unit area hectares in this analysis
bull MPA (mean patch area) is the average area of patches (ha) composed of canopy cover across each landscape
bull AI (aggregation index) is the area of canopy cover on the landscape that has like adjacencies of canopyclumped into a compact patch and is weighted proportionally by its area in the landscape It is a landscapemetric of how aggregated canopy cover is at each extent
bull PCI (patch cohesion index) is a connectivity metric that measures the physical connectedness of thecorresponding patch type Patch cohesion increases as canopy cover becomes more clumped or aggregated inits distribution and more physically connected
bull LDI (landscape division index) is the probability that two randomly chosen pixels in the landscape are notin the same patch and is based on the cumulative patch area distribution The sum is across the proportionalarea of each patch of canopy cover
For each landscape and each remote sensing product we performed a landscape pattern analysis
of canopy cover using the SDM Tool package (httpsCRANR-projectorgpackage=SDMTools)
in R (R Studio httpswwwr-projectorg) SDM Tools calculates landscape metrics (similar to
FRAGSTATS [44]) and outputs values for each metric We sampled each landscape metric across five
to seven different spatial extents 10 25 50 75 100 300 600 1000 ha We performed 100 Markov
Chain Monte Carlo (MCMC) simulations to sample each landscape at these multiple extents Our three
Forests 2019 10 465 7 of 21
landscapes varied in size therefore all three landscapes did not get sampled at the largest extents
Powell Plateau was sampled 10 handash300 ha Tepeats Creek was sampled 10 handash600 ha and Windmill
Draw was sampled 10 handash1000 ha (Figure 2)
3 Results
General patterns of patch characteristics were observed across spatial extents and between the four
data products (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) that match our understanding of
expected changes of patches with scale and grain This study more specifically tested readily available
remotely sensed data products to assess their consistency and efficacy for quantifying key characteristics
of forest canopy and sensitivity to change due to forest management or diversity Here we report the
differences in data products (grain) and how the patterns change across spatial extents
31 Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics ofCanopy Cover
The proportion of landscape (PLand) comprised of canopy cover was underpredicted by NAIP
and overpredicted by Landsat-8 and Sentinel-2 across our three landscapes PLand was best predicted
by the Sentinel-2 data (10 m) which produced means most similar to the LiDAR data (1 m) on Powell
Plateau our remnant functional landscape with an intact natural disturbance regime (Table 2 Figure 3)
NAIP (1 m) while still underestimated effective1y best characterized PLand on Windmill Draw
a highly managed forest landscape experiencing both fire exclusion and extensive timber management
Landsat-8 (30 m) overestimated PLand on all three landscapes
Table 2 Proportion of landscape (PLand) comprised of canopy cover across each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
Mean values and standard error (SE) are shown
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80633
(0019)0619
(0017)0635
(0013)0648
(0011)0660
(0009)0584
(0000)
Sentinel-20568
(0011)0559
(0010)0569
(0006)0578
(0005)0577
(0004)0550
(0000)
NAIP0314
(0006)0319
(0005)0330
(0003)0332
(0003)0334
(0003)0329
(0000)
LiDAR0537
(0010)0535
(0007)0552
(0006)0560
(0005)0560
(0004)0543
(0000)
Tepeats Creek
Landsat-80743
(0013)0734
(0011)0742
(0009)0732
(0009)0737
(0008)0744
(0003)0748
(0001)
Sentinel-20824
(0011)0797
(0009)0807
(0008)0795
(0008)0796
(0007)0790
(0003)0797
(0000)
NAIP0439
(0007)0422
(0006)0427
(0005)0418
(0004)0417
(0004)0420
(0001)0418
(0000)
LiDAR0596
(0010)0571
(0008)0565
(0008)0560
(0007)0550
(0007)0546
(0002)0568
(0001)
Forests 2019 10 465 8 of 21
Table 2 Cont
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80835
(0010)0826
(0009)0832
(0007)0824
(0006)0838
(0005)0826
(0002)0828
(0001)0831
(0000)
Sentinel-20832
(0009)0819
(0009)0828
(0007)0816
(0007)0824
(0006)0813
(0002)0814
(0001)0809
(0001)
NAIP0356
(0008)0336
(0007)0347
(0006)0339
(0006)0341
(0005)0321
(0002)0321
(0001)0318
(0000)
LiDAR0492
(0010)0473
(0009)0484
(0008)0476
(0008)0480
(0007)0461
(0002)0467
(0001)0470
(0000)
Figure 3 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for the proportion of landscape (ha) under forested canopy cover for each landscape and data product
(Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets
with rescaled y-axis provided for clarity
The number of patches of canopy cover or patch density (PD) was not accurately predicted
by any of our spatial products across landscapes or spatial extents NAIP at 1 m resolution highly
over-predicted the patch density by 25 to 3 times across all three landscapes (Table 3 Figure 4)
The small pixel size can fragment patches that the LiDAR point cloud keeps aggregated thus resulting
in many more patches across the landscape at all extents Landsat (30 m) and Sentinel-2 (10 m) highly
underestimated PD at their native cell resolutions Mean Patch Area (MPA) the average area composed
of patches of canopy cover was highly overestimated (one to two orders of magnitude) by Landsat
and Sentinel-2 as compared to LiDAR (Table 4 Figure 5) NAIP underestimated mean patch area by
three to four times on all landscapes across all spatial extents
Forests 2019 10 465 9 of 21
Table 3 Patch density (PD) the average number of patches of canopy cover on each landscape
delineated by each data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Patch Density (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80214
(0015)0138
(0011)0090
(0006)0063
(0004)0061
(0004)0075
(0000)
Sentinel-21047
(0074)0883
(0053)0706
(0035)0597
(0028)0590
(0025)0673
(0002)
NAIP57969(1079)
56659(0695)
53808(0516)
53300(0499)
52735(0448)
50537(0025)
LiDAR15428(0661)
15170(0536)
13349(0377)
12601(0352)
12375(0302)
12950(0011)
Tepeats Creek
Landsat-80153
(0010)0085
(0007)0051
(0004)0041
(0003)0038
(0003)0019
(0001)0011
(0000)
Sentinel-20310
(0032)0259
(0023)0189
(0017)0207
(0015)0185
(0014)0182
(0005)0157
(0000)
NAIP41339(1283)
40980(1027)
39345(0835)
40110(0723)
39728(0626)
37624(0212)
34257(0041)
LiDAR15310(0750)
16167(0617)
16325(0616)
16551(0526)
16973(0487)
16740(0135)
15306(0022)
Windmill Draw
Landsat-80138
(0007)0056
(0004)0032
(0002)0030
(0003)0021
(0002)0013
(0001)0011
(0000)0008
(0000)
Sentinel-20205
(0018)0153
(0018)0099
(0009)0104
(0008)0081
(0006)0073
(0002)0072
(0001)0067
(0000)
NAIP52483(1502)
54286(1227)
52324(1362)
52001(1231)
52385(1146)
55683(0579)
54084(0352)
51462(0104)
LiDAR23018(0853)
23109(0691)
22289(0706)
21886(0663)
21978(0618)
22724(0238)
21838(0135)
21666(0040)
Figure 4 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch density (patches per ha) Patch density is the number of patches per landscape area under
forested canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1
m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 10 of 21
Table 4 Mean Patch Area (MPA) (ha) the average area composed of patches of canopy cover across
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Mean Patch Area (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-8 4647(0299)
9474(0776)
13988(1296)
19518(1840)
20520(2052)
7819(0035)
Sentinel-2 1094(0132)
1158(0129)
1096(0069)
1245(0067)
1206(0061)
0817(0002)
NAIP 0006(0000)
0006(0000)
0006(0000)
0006(0000)
0006(0000)
0007(0000)
LiDAR 0045(0003)
0042(0002)
0046(0002)
0048(0001)
0048(0001)
0042(0000)
Tepeats Creek
Landsat-8 6351(0264)
13949(0736)
25349(1542)
31540(2152)
34500(2566)
47600(2326)
71374(1401)
Sentinel-2 5420(0344)
7333(0700)
12646(1459)
8224(1062)
7709(0663)
4834(0187)
5082(0014)
NAIP 0012(0001)
0011(0000)
0012(0000)
0011(0000)
0011(0000)
0011(0000)
0012(0000)
LiDAR 0059(0006)
0045(0003)
0046(0004)
0041(0003)
0038(0002)
0033(0000)
0037(0000)
Windmill Draw
Landsat-8 7248(0255)
18257(0611)
33437(1340)
42230(2231)
58305(2789)
95256(6883)
85781(3900)
104836(0889)
Sentinel-2 6083(0302)
11371(0782)
17674(1437)
19018(2053)
20103(2224)
12738(0508)
11511(0152)
12085(0100)
NAIP 0008(0001)
0007(0000)
0008(0000)
0008(0000)
0007(0000)
0006(0000)
0006(0000)
0006(0000)
LiDAR 0035(0005)
0028(0004)
0030(0004)
0029(0003)
0028(0002)
0021(0000)
0022(0000)
0022(0000)
Figure 5 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for mean patch area (ha) (average size of patches) for each landscape and data product (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis
provided for clarity
Forests 2019 10 465 11 of 21
Canopy cover was highly aggregated an attribute that was detected by all four data products
across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately
on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8
best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and
Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and
across all extents especially on Powell Plateau
Figure 6 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are
to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8
30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and
standard error (SE) are shown
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-876761(1175)
76553(0986)
77397(0802)
78012(0712)
78950(0581)
74823(0021)
Sentinel-269008(0748)
68585(0636)
68873(0473)
69394(0406)
69600(0323)
66906(0009)
NAIP77402(0246)
77603(0191)
78206(0113)
78239(0122)
78429(0105)
78773(0003)
LiDAR91447(0146)
91467(0106)
91810(0074)
91921(0070)
91941(0063)
91810(0001)
Tepeats Creek
Landsat-882820(0625)
82027(0543)
81855(0490)
81684(0425)
81706(0426)
82238(0139)
82418(0047)
Sentinel-290755(0471)
89497(0390)
90100(0311)
89688(0269)
89613(0221)
89824(0094)
90086(0012)
NAIP81195(0239)
81281(0183)
81362(0150)
81272(0137)
81256(0121)
81784(0047)
81914(0013)
LiDAR91847(0181)
91468(0144)
91330(0146)
91286(0127)
91147(0118)
91083(0041)
91506(0013)
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 6 of 21
of spatial connectedness and distribution including the aggregation index (AI) patch cohesion index
(PCI) and landscape division index (LDI)
Figure 2 Remotely sensed data products and associated grain of each data source Landsat-8 (30 m)
Sentinel-2 (10 m) NAIP (1 m) and LiDAR (1 m) The top panel (a) shows the canopy cover of Windmill
Draw landscape with the blue areas representing the relative size of each extent (10ndash1000 ha) analyzed
The lower panel (b) shows size of the spatial grain (resolution) and level of information available for
each data product within an example extent
Table 1 Description of landscape metrics evaluated across grain and extent on three landscapes
in northern Arizona USA Metrics derived from FRAGSTATS (wwwumassedulandecoresearch
fragstatsfragstatshtml)
Metric
bull PLand (proportion of landscape) is the total proportion of the patchmdashcanopy covermdashacross each landscape
bull PD (patch density) is the number of patches of canopy cover per unit area hectares in this analysis
bull MPA (mean patch area) is the average area of patches (ha) composed of canopy cover across each landscape
bull AI (aggregation index) is the area of canopy cover on the landscape that has like adjacencies of canopyclumped into a compact patch and is weighted proportionally by its area in the landscape It is a landscapemetric of how aggregated canopy cover is at each extent
bull PCI (patch cohesion index) is a connectivity metric that measures the physical connectedness of thecorresponding patch type Patch cohesion increases as canopy cover becomes more clumped or aggregated inits distribution and more physically connected
bull LDI (landscape division index) is the probability that two randomly chosen pixels in the landscape are notin the same patch and is based on the cumulative patch area distribution The sum is across the proportionalarea of each patch of canopy cover
For each landscape and each remote sensing product we performed a landscape pattern analysis
of canopy cover using the SDM Tool package (httpsCRANR-projectorgpackage=SDMTools)
in R (R Studio httpswwwr-projectorg) SDM Tools calculates landscape metrics (similar to
FRAGSTATS [44]) and outputs values for each metric We sampled each landscape metric across five
to seven different spatial extents 10 25 50 75 100 300 600 1000 ha We performed 100 Markov
Chain Monte Carlo (MCMC) simulations to sample each landscape at these multiple extents Our three
Forests 2019 10 465 7 of 21
landscapes varied in size therefore all three landscapes did not get sampled at the largest extents
Powell Plateau was sampled 10 handash300 ha Tepeats Creek was sampled 10 handash600 ha and Windmill
Draw was sampled 10 handash1000 ha (Figure 2)
3 Results
General patterns of patch characteristics were observed across spatial extents and between the four
data products (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) that match our understanding of
expected changes of patches with scale and grain This study more specifically tested readily available
remotely sensed data products to assess their consistency and efficacy for quantifying key characteristics
of forest canopy and sensitivity to change due to forest management or diversity Here we report the
differences in data products (grain) and how the patterns change across spatial extents
31 Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics ofCanopy Cover
The proportion of landscape (PLand) comprised of canopy cover was underpredicted by NAIP
and overpredicted by Landsat-8 and Sentinel-2 across our three landscapes PLand was best predicted
by the Sentinel-2 data (10 m) which produced means most similar to the LiDAR data (1 m) on Powell
Plateau our remnant functional landscape with an intact natural disturbance regime (Table 2 Figure 3)
NAIP (1 m) while still underestimated effective1y best characterized PLand on Windmill Draw
a highly managed forest landscape experiencing both fire exclusion and extensive timber management
Landsat-8 (30 m) overestimated PLand on all three landscapes
Table 2 Proportion of landscape (PLand) comprised of canopy cover across each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
Mean values and standard error (SE) are shown
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80633
(0019)0619
(0017)0635
(0013)0648
(0011)0660
(0009)0584
(0000)
Sentinel-20568
(0011)0559
(0010)0569
(0006)0578
(0005)0577
(0004)0550
(0000)
NAIP0314
(0006)0319
(0005)0330
(0003)0332
(0003)0334
(0003)0329
(0000)
LiDAR0537
(0010)0535
(0007)0552
(0006)0560
(0005)0560
(0004)0543
(0000)
Tepeats Creek
Landsat-80743
(0013)0734
(0011)0742
(0009)0732
(0009)0737
(0008)0744
(0003)0748
(0001)
Sentinel-20824
(0011)0797
(0009)0807
(0008)0795
(0008)0796
(0007)0790
(0003)0797
(0000)
NAIP0439
(0007)0422
(0006)0427
(0005)0418
(0004)0417
(0004)0420
(0001)0418
(0000)
LiDAR0596
(0010)0571
(0008)0565
(0008)0560
(0007)0550
(0007)0546
(0002)0568
(0001)
Forests 2019 10 465 8 of 21
Table 2 Cont
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80835
(0010)0826
(0009)0832
(0007)0824
(0006)0838
(0005)0826
(0002)0828
(0001)0831
(0000)
Sentinel-20832
(0009)0819
(0009)0828
(0007)0816
(0007)0824
(0006)0813
(0002)0814
(0001)0809
(0001)
NAIP0356
(0008)0336
(0007)0347
(0006)0339
(0006)0341
(0005)0321
(0002)0321
(0001)0318
(0000)
LiDAR0492
(0010)0473
(0009)0484
(0008)0476
(0008)0480
(0007)0461
(0002)0467
(0001)0470
(0000)
Figure 3 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for the proportion of landscape (ha) under forested canopy cover for each landscape and data product
(Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets
with rescaled y-axis provided for clarity
The number of patches of canopy cover or patch density (PD) was not accurately predicted
by any of our spatial products across landscapes or spatial extents NAIP at 1 m resolution highly
over-predicted the patch density by 25 to 3 times across all three landscapes (Table 3 Figure 4)
The small pixel size can fragment patches that the LiDAR point cloud keeps aggregated thus resulting
in many more patches across the landscape at all extents Landsat (30 m) and Sentinel-2 (10 m) highly
underestimated PD at their native cell resolutions Mean Patch Area (MPA) the average area composed
of patches of canopy cover was highly overestimated (one to two orders of magnitude) by Landsat
and Sentinel-2 as compared to LiDAR (Table 4 Figure 5) NAIP underestimated mean patch area by
three to four times on all landscapes across all spatial extents
Forests 2019 10 465 9 of 21
Table 3 Patch density (PD) the average number of patches of canopy cover on each landscape
delineated by each data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Patch Density (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80214
(0015)0138
(0011)0090
(0006)0063
(0004)0061
(0004)0075
(0000)
Sentinel-21047
(0074)0883
(0053)0706
(0035)0597
(0028)0590
(0025)0673
(0002)
NAIP57969(1079)
56659(0695)
53808(0516)
53300(0499)
52735(0448)
50537(0025)
LiDAR15428(0661)
15170(0536)
13349(0377)
12601(0352)
12375(0302)
12950(0011)
Tepeats Creek
Landsat-80153
(0010)0085
(0007)0051
(0004)0041
(0003)0038
(0003)0019
(0001)0011
(0000)
Sentinel-20310
(0032)0259
(0023)0189
(0017)0207
(0015)0185
(0014)0182
(0005)0157
(0000)
NAIP41339(1283)
40980(1027)
39345(0835)
40110(0723)
39728(0626)
37624(0212)
34257(0041)
LiDAR15310(0750)
16167(0617)
16325(0616)
16551(0526)
16973(0487)
16740(0135)
15306(0022)
Windmill Draw
Landsat-80138
(0007)0056
(0004)0032
(0002)0030
(0003)0021
(0002)0013
(0001)0011
(0000)0008
(0000)
Sentinel-20205
(0018)0153
(0018)0099
(0009)0104
(0008)0081
(0006)0073
(0002)0072
(0001)0067
(0000)
NAIP52483(1502)
54286(1227)
52324(1362)
52001(1231)
52385(1146)
55683(0579)
54084(0352)
51462(0104)
LiDAR23018(0853)
23109(0691)
22289(0706)
21886(0663)
21978(0618)
22724(0238)
21838(0135)
21666(0040)
Figure 4 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch density (patches per ha) Patch density is the number of patches per landscape area under
forested canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1
m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 10 of 21
Table 4 Mean Patch Area (MPA) (ha) the average area composed of patches of canopy cover across
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Mean Patch Area (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-8 4647(0299)
9474(0776)
13988(1296)
19518(1840)
20520(2052)
7819(0035)
Sentinel-2 1094(0132)
1158(0129)
1096(0069)
1245(0067)
1206(0061)
0817(0002)
NAIP 0006(0000)
0006(0000)
0006(0000)
0006(0000)
0006(0000)
0007(0000)
LiDAR 0045(0003)
0042(0002)
0046(0002)
0048(0001)
0048(0001)
0042(0000)
Tepeats Creek
Landsat-8 6351(0264)
13949(0736)
25349(1542)
31540(2152)
34500(2566)
47600(2326)
71374(1401)
Sentinel-2 5420(0344)
7333(0700)
12646(1459)
8224(1062)
7709(0663)
4834(0187)
5082(0014)
NAIP 0012(0001)
0011(0000)
0012(0000)
0011(0000)
0011(0000)
0011(0000)
0012(0000)
LiDAR 0059(0006)
0045(0003)
0046(0004)
0041(0003)
0038(0002)
0033(0000)
0037(0000)
Windmill Draw
Landsat-8 7248(0255)
18257(0611)
33437(1340)
42230(2231)
58305(2789)
95256(6883)
85781(3900)
104836(0889)
Sentinel-2 6083(0302)
11371(0782)
17674(1437)
19018(2053)
20103(2224)
12738(0508)
11511(0152)
12085(0100)
NAIP 0008(0001)
0007(0000)
0008(0000)
0008(0000)
0007(0000)
0006(0000)
0006(0000)
0006(0000)
LiDAR 0035(0005)
0028(0004)
0030(0004)
0029(0003)
0028(0002)
0021(0000)
0022(0000)
0022(0000)
Figure 5 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for mean patch area (ha) (average size of patches) for each landscape and data product (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis
provided for clarity
Forests 2019 10 465 11 of 21
Canopy cover was highly aggregated an attribute that was detected by all four data products
across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately
on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8
best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and
Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and
across all extents especially on Powell Plateau
Figure 6 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are
to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8
30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and
standard error (SE) are shown
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-876761(1175)
76553(0986)
77397(0802)
78012(0712)
78950(0581)
74823(0021)
Sentinel-269008(0748)
68585(0636)
68873(0473)
69394(0406)
69600(0323)
66906(0009)
NAIP77402(0246)
77603(0191)
78206(0113)
78239(0122)
78429(0105)
78773(0003)
LiDAR91447(0146)
91467(0106)
91810(0074)
91921(0070)
91941(0063)
91810(0001)
Tepeats Creek
Landsat-882820(0625)
82027(0543)
81855(0490)
81684(0425)
81706(0426)
82238(0139)
82418(0047)
Sentinel-290755(0471)
89497(0390)
90100(0311)
89688(0269)
89613(0221)
89824(0094)
90086(0012)
NAIP81195(0239)
81281(0183)
81362(0150)
81272(0137)
81256(0121)
81784(0047)
81914(0013)
LiDAR91847(0181)
91468(0144)
91330(0146)
91286(0127)
91147(0118)
91083(0041)
91506(0013)
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 7 of 21
landscapes varied in size therefore all three landscapes did not get sampled at the largest extents
Powell Plateau was sampled 10 handash300 ha Tepeats Creek was sampled 10 handash600 ha and Windmill
Draw was sampled 10 handash1000 ha (Figure 2)
3 Results
General patterns of patch characteristics were observed across spatial extents and between the four
data products (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) that match our understanding of
expected changes of patches with scale and grain This study more specifically tested readily available
remotely sensed data products to assess their consistency and efficacy for quantifying key characteristics
of forest canopy and sensitivity to change due to forest management or diversity Here we report the
differences in data products (grain) and how the patterns change across spatial extents
31 Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics ofCanopy Cover
The proportion of landscape (PLand) comprised of canopy cover was underpredicted by NAIP
and overpredicted by Landsat-8 and Sentinel-2 across our three landscapes PLand was best predicted
by the Sentinel-2 data (10 m) which produced means most similar to the LiDAR data (1 m) on Powell
Plateau our remnant functional landscape with an intact natural disturbance regime (Table 2 Figure 3)
NAIP (1 m) while still underestimated effective1y best characterized PLand on Windmill Draw
a highly managed forest landscape experiencing both fire exclusion and extensive timber management
Landsat-8 (30 m) overestimated PLand on all three landscapes
Table 2 Proportion of landscape (PLand) comprised of canopy cover across each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
Mean values and standard error (SE) are shown
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80633
(0019)0619
(0017)0635
(0013)0648
(0011)0660
(0009)0584
(0000)
Sentinel-20568
(0011)0559
(0010)0569
(0006)0578
(0005)0577
(0004)0550
(0000)
NAIP0314
(0006)0319
(0005)0330
(0003)0332
(0003)0334
(0003)0329
(0000)
LiDAR0537
(0010)0535
(0007)0552
(0006)0560
(0005)0560
(0004)0543
(0000)
Tepeats Creek
Landsat-80743
(0013)0734
(0011)0742
(0009)0732
(0009)0737
(0008)0744
(0003)0748
(0001)
Sentinel-20824
(0011)0797
(0009)0807
(0008)0795
(0008)0796
(0007)0790
(0003)0797
(0000)
NAIP0439
(0007)0422
(0006)0427
(0005)0418
(0004)0417
(0004)0420
(0001)0418
(0000)
LiDAR0596
(0010)0571
(0008)0565
(0008)0560
(0007)0550
(0007)0546
(0002)0568
(0001)
Forests 2019 10 465 8 of 21
Table 2 Cont
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80835
(0010)0826
(0009)0832
(0007)0824
(0006)0838
(0005)0826
(0002)0828
(0001)0831
(0000)
Sentinel-20832
(0009)0819
(0009)0828
(0007)0816
(0007)0824
(0006)0813
(0002)0814
(0001)0809
(0001)
NAIP0356
(0008)0336
(0007)0347
(0006)0339
(0006)0341
(0005)0321
(0002)0321
(0001)0318
(0000)
LiDAR0492
(0010)0473
(0009)0484
(0008)0476
(0008)0480
(0007)0461
(0002)0467
(0001)0470
(0000)
Figure 3 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for the proportion of landscape (ha) under forested canopy cover for each landscape and data product
(Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets
with rescaled y-axis provided for clarity
The number of patches of canopy cover or patch density (PD) was not accurately predicted
by any of our spatial products across landscapes or spatial extents NAIP at 1 m resolution highly
over-predicted the patch density by 25 to 3 times across all three landscapes (Table 3 Figure 4)
The small pixel size can fragment patches that the LiDAR point cloud keeps aggregated thus resulting
in many more patches across the landscape at all extents Landsat (30 m) and Sentinel-2 (10 m) highly
underestimated PD at their native cell resolutions Mean Patch Area (MPA) the average area composed
of patches of canopy cover was highly overestimated (one to two orders of magnitude) by Landsat
and Sentinel-2 as compared to LiDAR (Table 4 Figure 5) NAIP underestimated mean patch area by
three to four times on all landscapes across all spatial extents
Forests 2019 10 465 9 of 21
Table 3 Patch density (PD) the average number of patches of canopy cover on each landscape
delineated by each data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Patch Density (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80214
(0015)0138
(0011)0090
(0006)0063
(0004)0061
(0004)0075
(0000)
Sentinel-21047
(0074)0883
(0053)0706
(0035)0597
(0028)0590
(0025)0673
(0002)
NAIP57969(1079)
56659(0695)
53808(0516)
53300(0499)
52735(0448)
50537(0025)
LiDAR15428(0661)
15170(0536)
13349(0377)
12601(0352)
12375(0302)
12950(0011)
Tepeats Creek
Landsat-80153
(0010)0085
(0007)0051
(0004)0041
(0003)0038
(0003)0019
(0001)0011
(0000)
Sentinel-20310
(0032)0259
(0023)0189
(0017)0207
(0015)0185
(0014)0182
(0005)0157
(0000)
NAIP41339(1283)
40980(1027)
39345(0835)
40110(0723)
39728(0626)
37624(0212)
34257(0041)
LiDAR15310(0750)
16167(0617)
16325(0616)
16551(0526)
16973(0487)
16740(0135)
15306(0022)
Windmill Draw
Landsat-80138
(0007)0056
(0004)0032
(0002)0030
(0003)0021
(0002)0013
(0001)0011
(0000)0008
(0000)
Sentinel-20205
(0018)0153
(0018)0099
(0009)0104
(0008)0081
(0006)0073
(0002)0072
(0001)0067
(0000)
NAIP52483(1502)
54286(1227)
52324(1362)
52001(1231)
52385(1146)
55683(0579)
54084(0352)
51462(0104)
LiDAR23018(0853)
23109(0691)
22289(0706)
21886(0663)
21978(0618)
22724(0238)
21838(0135)
21666(0040)
Figure 4 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch density (patches per ha) Patch density is the number of patches per landscape area under
forested canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1
m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 10 of 21
Table 4 Mean Patch Area (MPA) (ha) the average area composed of patches of canopy cover across
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Mean Patch Area (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-8 4647(0299)
9474(0776)
13988(1296)
19518(1840)
20520(2052)
7819(0035)
Sentinel-2 1094(0132)
1158(0129)
1096(0069)
1245(0067)
1206(0061)
0817(0002)
NAIP 0006(0000)
0006(0000)
0006(0000)
0006(0000)
0006(0000)
0007(0000)
LiDAR 0045(0003)
0042(0002)
0046(0002)
0048(0001)
0048(0001)
0042(0000)
Tepeats Creek
Landsat-8 6351(0264)
13949(0736)
25349(1542)
31540(2152)
34500(2566)
47600(2326)
71374(1401)
Sentinel-2 5420(0344)
7333(0700)
12646(1459)
8224(1062)
7709(0663)
4834(0187)
5082(0014)
NAIP 0012(0001)
0011(0000)
0012(0000)
0011(0000)
0011(0000)
0011(0000)
0012(0000)
LiDAR 0059(0006)
0045(0003)
0046(0004)
0041(0003)
0038(0002)
0033(0000)
0037(0000)
Windmill Draw
Landsat-8 7248(0255)
18257(0611)
33437(1340)
42230(2231)
58305(2789)
95256(6883)
85781(3900)
104836(0889)
Sentinel-2 6083(0302)
11371(0782)
17674(1437)
19018(2053)
20103(2224)
12738(0508)
11511(0152)
12085(0100)
NAIP 0008(0001)
0007(0000)
0008(0000)
0008(0000)
0007(0000)
0006(0000)
0006(0000)
0006(0000)
LiDAR 0035(0005)
0028(0004)
0030(0004)
0029(0003)
0028(0002)
0021(0000)
0022(0000)
0022(0000)
Figure 5 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for mean patch area (ha) (average size of patches) for each landscape and data product (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis
provided for clarity
Forests 2019 10 465 11 of 21
Canopy cover was highly aggregated an attribute that was detected by all four data products
across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately
on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8
best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and
Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and
across all extents especially on Powell Plateau
Figure 6 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are
to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8
30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and
standard error (SE) are shown
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-876761(1175)
76553(0986)
77397(0802)
78012(0712)
78950(0581)
74823(0021)
Sentinel-269008(0748)
68585(0636)
68873(0473)
69394(0406)
69600(0323)
66906(0009)
NAIP77402(0246)
77603(0191)
78206(0113)
78239(0122)
78429(0105)
78773(0003)
LiDAR91447(0146)
91467(0106)
91810(0074)
91921(0070)
91941(0063)
91810(0001)
Tepeats Creek
Landsat-882820(0625)
82027(0543)
81855(0490)
81684(0425)
81706(0426)
82238(0139)
82418(0047)
Sentinel-290755(0471)
89497(0390)
90100(0311)
89688(0269)
89613(0221)
89824(0094)
90086(0012)
NAIP81195(0239)
81281(0183)
81362(0150)
81272(0137)
81256(0121)
81784(0047)
81914(0013)
LiDAR91847(0181)
91468(0144)
91330(0146)
91286(0127)
91147(0118)
91083(0041)
91506(0013)
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 8 of 21
Table 2 Cont
Proportion of Landscape () Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80835
(0010)0826
(0009)0832
(0007)0824
(0006)0838
(0005)0826
(0002)0828
(0001)0831
(0000)
Sentinel-20832
(0009)0819
(0009)0828
(0007)0816
(0007)0824
(0006)0813
(0002)0814
(0001)0809
(0001)
NAIP0356
(0008)0336
(0007)0347
(0006)0339
(0006)0341
(0005)0321
(0002)0321
(0001)0318
(0000)
LiDAR0492
(0010)0473
(0009)0484
(0008)0476
(0008)0480
(0007)0461
(0002)0467
(0001)0470
(0000)
Figure 3 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for the proportion of landscape (ha) under forested canopy cover for each landscape and data product
(Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets
with rescaled y-axis provided for clarity
The number of patches of canopy cover or patch density (PD) was not accurately predicted
by any of our spatial products across landscapes or spatial extents NAIP at 1 m resolution highly
over-predicted the patch density by 25 to 3 times across all three landscapes (Table 3 Figure 4)
The small pixel size can fragment patches that the LiDAR point cloud keeps aggregated thus resulting
in many more patches across the landscape at all extents Landsat (30 m) and Sentinel-2 (10 m) highly
underestimated PD at their native cell resolutions Mean Patch Area (MPA) the average area composed
of patches of canopy cover was highly overestimated (one to two orders of magnitude) by Landsat
and Sentinel-2 as compared to LiDAR (Table 4 Figure 5) NAIP underestimated mean patch area by
three to four times on all landscapes across all spatial extents
Forests 2019 10 465 9 of 21
Table 3 Patch density (PD) the average number of patches of canopy cover on each landscape
delineated by each data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Patch Density (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80214
(0015)0138
(0011)0090
(0006)0063
(0004)0061
(0004)0075
(0000)
Sentinel-21047
(0074)0883
(0053)0706
(0035)0597
(0028)0590
(0025)0673
(0002)
NAIP57969(1079)
56659(0695)
53808(0516)
53300(0499)
52735(0448)
50537(0025)
LiDAR15428(0661)
15170(0536)
13349(0377)
12601(0352)
12375(0302)
12950(0011)
Tepeats Creek
Landsat-80153
(0010)0085
(0007)0051
(0004)0041
(0003)0038
(0003)0019
(0001)0011
(0000)
Sentinel-20310
(0032)0259
(0023)0189
(0017)0207
(0015)0185
(0014)0182
(0005)0157
(0000)
NAIP41339(1283)
40980(1027)
39345(0835)
40110(0723)
39728(0626)
37624(0212)
34257(0041)
LiDAR15310(0750)
16167(0617)
16325(0616)
16551(0526)
16973(0487)
16740(0135)
15306(0022)
Windmill Draw
Landsat-80138
(0007)0056
(0004)0032
(0002)0030
(0003)0021
(0002)0013
(0001)0011
(0000)0008
(0000)
Sentinel-20205
(0018)0153
(0018)0099
(0009)0104
(0008)0081
(0006)0073
(0002)0072
(0001)0067
(0000)
NAIP52483(1502)
54286(1227)
52324(1362)
52001(1231)
52385(1146)
55683(0579)
54084(0352)
51462(0104)
LiDAR23018(0853)
23109(0691)
22289(0706)
21886(0663)
21978(0618)
22724(0238)
21838(0135)
21666(0040)
Figure 4 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch density (patches per ha) Patch density is the number of patches per landscape area under
forested canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1
m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 10 of 21
Table 4 Mean Patch Area (MPA) (ha) the average area composed of patches of canopy cover across
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Mean Patch Area (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-8 4647(0299)
9474(0776)
13988(1296)
19518(1840)
20520(2052)
7819(0035)
Sentinel-2 1094(0132)
1158(0129)
1096(0069)
1245(0067)
1206(0061)
0817(0002)
NAIP 0006(0000)
0006(0000)
0006(0000)
0006(0000)
0006(0000)
0007(0000)
LiDAR 0045(0003)
0042(0002)
0046(0002)
0048(0001)
0048(0001)
0042(0000)
Tepeats Creek
Landsat-8 6351(0264)
13949(0736)
25349(1542)
31540(2152)
34500(2566)
47600(2326)
71374(1401)
Sentinel-2 5420(0344)
7333(0700)
12646(1459)
8224(1062)
7709(0663)
4834(0187)
5082(0014)
NAIP 0012(0001)
0011(0000)
0012(0000)
0011(0000)
0011(0000)
0011(0000)
0012(0000)
LiDAR 0059(0006)
0045(0003)
0046(0004)
0041(0003)
0038(0002)
0033(0000)
0037(0000)
Windmill Draw
Landsat-8 7248(0255)
18257(0611)
33437(1340)
42230(2231)
58305(2789)
95256(6883)
85781(3900)
104836(0889)
Sentinel-2 6083(0302)
11371(0782)
17674(1437)
19018(2053)
20103(2224)
12738(0508)
11511(0152)
12085(0100)
NAIP 0008(0001)
0007(0000)
0008(0000)
0008(0000)
0007(0000)
0006(0000)
0006(0000)
0006(0000)
LiDAR 0035(0005)
0028(0004)
0030(0004)
0029(0003)
0028(0002)
0021(0000)
0022(0000)
0022(0000)
Figure 5 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for mean patch area (ha) (average size of patches) for each landscape and data product (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis
provided for clarity
Forests 2019 10 465 11 of 21
Canopy cover was highly aggregated an attribute that was detected by all four data products
across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately
on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8
best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and
Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and
across all extents especially on Powell Plateau
Figure 6 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are
to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8
30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and
standard error (SE) are shown
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-876761(1175)
76553(0986)
77397(0802)
78012(0712)
78950(0581)
74823(0021)
Sentinel-269008(0748)
68585(0636)
68873(0473)
69394(0406)
69600(0323)
66906(0009)
NAIP77402(0246)
77603(0191)
78206(0113)
78239(0122)
78429(0105)
78773(0003)
LiDAR91447(0146)
91467(0106)
91810(0074)
91921(0070)
91941(0063)
91810(0001)
Tepeats Creek
Landsat-882820(0625)
82027(0543)
81855(0490)
81684(0425)
81706(0426)
82238(0139)
82418(0047)
Sentinel-290755(0471)
89497(0390)
90100(0311)
89688(0269)
89613(0221)
89824(0094)
90086(0012)
NAIP81195(0239)
81281(0183)
81362(0150)
81272(0137)
81256(0121)
81784(0047)
81914(0013)
LiDAR91847(0181)
91468(0144)
91330(0146)
91286(0127)
91147(0118)
91083(0041)
91506(0013)
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 9 of 21
Table 3 Patch density (PD) the average number of patches of canopy cover on each landscape
delineated by each data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Patch Density (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80214
(0015)0138
(0011)0090
(0006)0063
(0004)0061
(0004)0075
(0000)
Sentinel-21047
(0074)0883
(0053)0706
(0035)0597
(0028)0590
(0025)0673
(0002)
NAIP57969(1079)
56659(0695)
53808(0516)
53300(0499)
52735(0448)
50537(0025)
LiDAR15428(0661)
15170(0536)
13349(0377)
12601(0352)
12375(0302)
12950(0011)
Tepeats Creek
Landsat-80153
(0010)0085
(0007)0051
(0004)0041
(0003)0038
(0003)0019
(0001)0011
(0000)
Sentinel-20310
(0032)0259
(0023)0189
(0017)0207
(0015)0185
(0014)0182
(0005)0157
(0000)
NAIP41339(1283)
40980(1027)
39345(0835)
40110(0723)
39728(0626)
37624(0212)
34257(0041)
LiDAR15310(0750)
16167(0617)
16325(0616)
16551(0526)
16973(0487)
16740(0135)
15306(0022)
Windmill Draw
Landsat-80138
(0007)0056
(0004)0032
(0002)0030
(0003)0021
(0002)0013
(0001)0011
(0000)0008
(0000)
Sentinel-20205
(0018)0153
(0018)0099
(0009)0104
(0008)0081
(0006)0073
(0002)0072
(0001)0067
(0000)
NAIP52483(1502)
54286(1227)
52324(1362)
52001(1231)
52385(1146)
55683(0579)
54084(0352)
51462(0104)
LiDAR23018(0853)
23109(0691)
22289(0706)
21886(0663)
21978(0618)
22724(0238)
21838(0135)
21666(0040)
Figure 4 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch density (patches per ha) Patch density is the number of patches per landscape area under
forested canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1
m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 10 of 21
Table 4 Mean Patch Area (MPA) (ha) the average area composed of patches of canopy cover across
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Mean Patch Area (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-8 4647(0299)
9474(0776)
13988(1296)
19518(1840)
20520(2052)
7819(0035)
Sentinel-2 1094(0132)
1158(0129)
1096(0069)
1245(0067)
1206(0061)
0817(0002)
NAIP 0006(0000)
0006(0000)
0006(0000)
0006(0000)
0006(0000)
0007(0000)
LiDAR 0045(0003)
0042(0002)
0046(0002)
0048(0001)
0048(0001)
0042(0000)
Tepeats Creek
Landsat-8 6351(0264)
13949(0736)
25349(1542)
31540(2152)
34500(2566)
47600(2326)
71374(1401)
Sentinel-2 5420(0344)
7333(0700)
12646(1459)
8224(1062)
7709(0663)
4834(0187)
5082(0014)
NAIP 0012(0001)
0011(0000)
0012(0000)
0011(0000)
0011(0000)
0011(0000)
0012(0000)
LiDAR 0059(0006)
0045(0003)
0046(0004)
0041(0003)
0038(0002)
0033(0000)
0037(0000)
Windmill Draw
Landsat-8 7248(0255)
18257(0611)
33437(1340)
42230(2231)
58305(2789)
95256(6883)
85781(3900)
104836(0889)
Sentinel-2 6083(0302)
11371(0782)
17674(1437)
19018(2053)
20103(2224)
12738(0508)
11511(0152)
12085(0100)
NAIP 0008(0001)
0007(0000)
0008(0000)
0008(0000)
0007(0000)
0006(0000)
0006(0000)
0006(0000)
LiDAR 0035(0005)
0028(0004)
0030(0004)
0029(0003)
0028(0002)
0021(0000)
0022(0000)
0022(0000)
Figure 5 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for mean patch area (ha) (average size of patches) for each landscape and data product (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis
provided for clarity
Forests 2019 10 465 11 of 21
Canopy cover was highly aggregated an attribute that was detected by all four data products
across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately
on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8
best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and
Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and
across all extents especially on Powell Plateau
Figure 6 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are
to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8
30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and
standard error (SE) are shown
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-876761(1175)
76553(0986)
77397(0802)
78012(0712)
78950(0581)
74823(0021)
Sentinel-269008(0748)
68585(0636)
68873(0473)
69394(0406)
69600(0323)
66906(0009)
NAIP77402(0246)
77603(0191)
78206(0113)
78239(0122)
78429(0105)
78773(0003)
LiDAR91447(0146)
91467(0106)
91810(0074)
91921(0070)
91941(0063)
91810(0001)
Tepeats Creek
Landsat-882820(0625)
82027(0543)
81855(0490)
81684(0425)
81706(0426)
82238(0139)
82418(0047)
Sentinel-290755(0471)
89497(0390)
90100(0311)
89688(0269)
89613(0221)
89824(0094)
90086(0012)
NAIP81195(0239)
81281(0183)
81362(0150)
81272(0137)
81256(0121)
81784(0047)
81914(0013)
LiDAR91847(0181)
91468(0144)
91330(0146)
91286(0127)
91147(0118)
91083(0041)
91506(0013)
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 10 of 21
Table 4 Mean Patch Area (MPA) (ha) the average area composed of patches of canopy cover across
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
multiple spatial extents Mean values and standard error (SE) are shown
Mean Patch Area (ha) Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-8 4647(0299)
9474(0776)
13988(1296)
19518(1840)
20520(2052)
7819(0035)
Sentinel-2 1094(0132)
1158(0129)
1096(0069)
1245(0067)
1206(0061)
0817(0002)
NAIP 0006(0000)
0006(0000)
0006(0000)
0006(0000)
0006(0000)
0007(0000)
LiDAR 0045(0003)
0042(0002)
0046(0002)
0048(0001)
0048(0001)
0042(0000)
Tepeats Creek
Landsat-8 6351(0264)
13949(0736)
25349(1542)
31540(2152)
34500(2566)
47600(2326)
71374(1401)
Sentinel-2 5420(0344)
7333(0700)
12646(1459)
8224(1062)
7709(0663)
4834(0187)
5082(0014)
NAIP 0012(0001)
0011(0000)
0012(0000)
0011(0000)
0011(0000)
0011(0000)
0012(0000)
LiDAR 0059(0006)
0045(0003)
0046(0004)
0041(0003)
0038(0002)
0033(0000)
0037(0000)
Windmill Draw
Landsat-8 7248(0255)
18257(0611)
33437(1340)
42230(2231)
58305(2789)
95256(6883)
85781(3900)
104836(0889)
Sentinel-2 6083(0302)
11371(0782)
17674(1437)
19018(2053)
20103(2224)
12738(0508)
11511(0152)
12085(0100)
NAIP 0008(0001)
0007(0000)
0008(0000)
0008(0000)
0007(0000)
0006(0000)
0006(0000)
0006(0000)
LiDAR 0035(0005)
0028(0004)
0030(0004)
0029(0003)
0028(0002)
0021(0000)
0022(0000)
0022(0000)
Figure 5 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for mean patch area (ha) (average size of patches) for each landscape and data product (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Insets with rescaled y-axis
provided for clarity
Forests 2019 10 465 11 of 21
Canopy cover was highly aggregated an attribute that was detected by all four data products
across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately
on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8
best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and
Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and
across all extents especially on Powell Plateau
Figure 6 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are
to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8
30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and
standard error (SE) are shown
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-876761(1175)
76553(0986)
77397(0802)
78012(0712)
78950(0581)
74823(0021)
Sentinel-269008(0748)
68585(0636)
68873(0473)
69394(0406)
69600(0323)
66906(0009)
NAIP77402(0246)
77603(0191)
78206(0113)
78239(0122)
78429(0105)
78773(0003)
LiDAR91447(0146)
91467(0106)
91810(0074)
91921(0070)
91941(0063)
91810(0001)
Tepeats Creek
Landsat-882820(0625)
82027(0543)
81855(0490)
81684(0425)
81706(0426)
82238(0139)
82418(0047)
Sentinel-290755(0471)
89497(0390)
90100(0311)
89688(0269)
89613(0221)
89824(0094)
90086(0012)
NAIP81195(0239)
81281(0183)
81362(0150)
81272(0137)
81256(0121)
81784(0047)
81914(0013)
LiDAR91847(0181)
91468(0144)
91330(0146)
91286(0127)
91147(0118)
91083(0041)
91506(0013)
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 11 of 21
Canopy cover was highly aggregated an attribute that was detected by all four data products
across all spatial extents sampled (Figure 6) Sentinel-2 was able to detect aggregation more accurately
on Tepeats Creek and Windmill Draw but under-predicted aggregation on Powell Plateau Landsat-8
best predicted aggregation on Windmill Draw and under-predicted aggregation on Tepeats Creek and
Powell Plateau (Table 5 Figure 6) NAIP under-estimated aggregation on all three landscapes and
across all extents especially on Powell Plateau
Figure 6 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for aggregation index (0ndash100) Aggregation index indicates how adjacent patches of canopy cover are
to each other for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Table 5 Aggregation index (AI) () of canopy cover across each landscape and data product (Landsat-8
30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents Mean values and
standard error (SE) are shown
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-876761(1175)
76553(0986)
77397(0802)
78012(0712)
78950(0581)
74823(0021)
Sentinel-269008(0748)
68585(0636)
68873(0473)
69394(0406)
69600(0323)
66906(0009)
NAIP77402(0246)
77603(0191)
78206(0113)
78239(0122)
78429(0105)
78773(0003)
LiDAR91447(0146)
91467(0106)
91810(0074)
91921(0070)
91941(0063)
91810(0001)
Tepeats Creek
Landsat-882820(0625)
82027(0543)
81855(0490)
81684(0425)
81706(0426)
82238(0139)
82418(0047)
Sentinel-290755(0471)
89497(0390)
90100(0311)
89688(0269)
89613(0221)
89824(0094)
90086(0012)
NAIP81195(0239)
81281(0183)
81362(0150)
81272(0137)
81256(0121)
81784(0047)
81914(0013)
LiDAR91847(0181)
91468(0144)
91330(0146)
91286(0127)
91147(0118)
91083(0041)
91506(0013)
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 12 of 21
Table 5 Cont
Aggregation Index Extent (ha)
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-887235(0775)
86813(0595)
86940(0466)
86730(0364)
87511(0305)
86643(0168)
86639(0073)
86754(0010)
Sentinel-289880(0514)
89913(0344)
90184(0312)
89781(0323)
89827(0298)
89461(0091)
89408(0061)
88922(0032)
NAIP79129(0287)
78427(0252)
78715(0271)
78745(0256)
78549(0231)
77921(0159)
78242(0102)
78734(0020)
LiDAR88984(0192)
88656(0170)
88852(0175)
88824(0163)
88834(0151)
88463(0057)
88651(0035)
88751(0008)
All three landscapes had a high patch cohesion index and high physical patch connectivity of
canopy cover across all four data grains and across all spatial extents (Figure 7 Table 6) Patch cohesion
is sensitive to aggregation of canopy cover and increases as canopy cover becomes more clumped or
aggregated in its distribution NAIP and Sentinel-2 most accurately predicted patch cohesion on all
three landscapes across all extents while Landsat-8 consistently and increasingly under-predicted
patch cohesion at extents 50 ha and smaller (Table 6)
Figure 7 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for patch cohesion index () Patch cohesion index describes the physical connectedness of patches of
canopy cover for each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR
1 m) and across multiple spatial extents Insets with rescaled y-axis provided for clarity
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 13 of 21
Table 6 Patch cohesion (PC) index measuring the physical connectedness of patches of canopy cover for
each landscape and data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across
spatial extents Mean values and standard error (SE) are shown
Patch Cohesion Index () Extent (ha)
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-87733
(0066)8496
(0036)8998
(0014)9205
(0008)9318
(0005)9560
(0000)
Sentinel-29183
(0020)9476
(0012)9654
(0004)9723
(0002)9760
(0001)9855
(0000)
NAIP9539
(0015)9624
(0013)9686
(0008)9686
(0006)9705
(0004)9733
(0000)
LiDAR9859
(0007)9914
(0003)9947
(0001)9957
(0001)9963
(0001)9979
(0000)
Tepeats Creek
Landsat-87970
(0024)8700
(0012)9063
(0010)9229
(0009)9331
(0007)9627
(0001)9731
(0000)
Sentinel-29327
(0008)9569
(0004)9697
(0002)9749
(0002)9785
(0001)9877
(0000)9911
(0000)
NAIP9758
(0013)9793
(0011)9835
(0009)9844
(0008)9850
(0008)9913
(0003)9947
(0000)
LiDAR9887
(0006)9919
(0004)9936
(0003)9947
(0002)9949
(0002)9971
(0000)9981
(0000)
Windmill Draw
Landsat-88070
(0015)8767
(0006)9126
(0003)9283
(0002)9378
(0001)9638
(0000)9743
(0000)9798
(0000)
Sentinel-29333
(0005)9571
(0005)9698
(0002)9752
(0002)9784
(0002)9876
(0000)9912
(0000)9932
(0000)
NAIP9476
(0021)9471
(0019)9551
(0020)9538
(0020)9561
(0019)9518
(0012)9554
(0009)9627
(0002)
LiDAR9759
(0012)9793
(0010)9839
(0008)9843
(0009)9858
(0008)9876
(0004)9906
(0002)9921
(0000)
Landscape division index was best predicted by NAIP especially on Powell Plateau and Windmill
Draw but poorly captured within-extent variability (Figure 8 Table 7) Landsat-8 and Sentinel-2 highly
under-predicted LDI by two to four times across all landscapes and extents NAIP over-estimated LDI
on Powell Plateau but performed well at larger spatial extents 300 ha and 600 and 1000 ha on Tepeats
Creek and Windmill Draw (Table 7)
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 14 of 21
Figure 8 Violin plots (colored by extent and depicting probability density of the data at different values
smoothed by a kernel density estimator) box-and-whisker plots (black and white) and mean (red dot)
for landscape division index () Landscape division index is presented for each landscape and data
product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial extents
and is defined as the probability that two animals placed in different areas somewhere in the extent
might find one another Insets with rescaled y-axis provided for clarity
Table 7 Landscape division index (LDI) of canopy cover across all three landscapes across for each
data product (Landsat-8 30 m Sentinel-2 10 m NAIP 1 m LiDAR 1 m) and across multiple spatial
extents Mean values and standard error (SE) are shown
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Powell Plateau
Landsat-80585
(0024)0628
(0025)0615
(0020)0594
(0016)0581
(0014)0741
(0001)
Sentinel-20708
(0015)0726
(0014)0708
(0010)0692
(0008)0695
(0006)0745
(0000)
NAIP0996
(0000)0997
(0000)0998
(0000)0999
(0000)0999
(0000)1000
(0000)
LiDAR0869
(0012)0879
(0010)0867
(0007)0864
(0006)0859
(0006)0881
(0000)
Tepeats Creek
Landsat-80446
(0021)0468
(0018)0474
(0018)0489
(0017)0486
(0016)0453
(0006)0444
(0001)
Sentinel-20326
(0020)0382
(0017)0367
(0016)0394
(0016)0382
(0013)0385
(0005)0374
(0000)
NAIP0959
(0005)0977
(0003)0980
(0003)0986
(0002)0988
(0002)0994
(0000)0994
(0000)
LiDAR0775
(0017)0839
(0013)0852
(0015)0869
(0013)0893
(0012)0939
(0004)0936
(0001)
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 15 of 21
Table 7 Cont
Landscape Division Index Extent
10 25 50 75 100 300 600 1000
Windmill Draw
Landsat-80305
(0018)0319
(0015)0310
(0012)0325
(0010)0301
(0008)0322
(0004)0318
(0001)0312
(0000)
Sentinel-20319
(0018)0344
(0017)0337
(0015)0358
(0016)0353
(0015)0366
(0006)0366
(0005)0354
(0001)
NAIP0983
(0005)0992
(0003)0990
(0003)0994
(0002)0995
(0002)1000
(0000)1000
(0000)1000
(0000)
LiDAR0925
(0014)0957
(0011)0952
(0011)0964
(0009)0965
(0009)0997
(0000)0998
(0000)0998
(0000)
32 Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
We examined how patch metrics varied across the landscapes and extents for each data product
PLand varied across landscapes and extent for all four spatial products In addition to the median and
mean values the ability to capture the variability on the landscape is important to both land managers
and stakeholders For this metric there was little observed variability at extents larger than 300 ha
and was best observed using extents smaller than 75 ha or 100 ha (Table 2 Figure 3) The values of
the PLand varied across landscapes However not consistently across remotely sensed products and
spatial grain The LiDAR data product showed similar canopy cover across Tepeats Creek and Powell
Plateau with lower values at Windmill Draw Conversely the Landsat-8 and Sentinel-2 data products
both had the highest canopy cover at Windmill Draw NAIP at 1 m resolution underestimated PLand
at Powell Plateau across all spatial extents
We standardized patch density (PD) by area in hectares (Figure 4) There were consistent patterns
of high overestimation of PD by NAIP and high underestimation of PD by Sentinel-2 and Landsat-8
compared to LiDAR On Powell Plateau at 100 ha extent LiDAR predicted 124 patches while NAIP
predicted 527 patches Landsat-8 predicted 006 patches and Sentinel-2 predicted 059 patches (Table 3)
Patch density of both single trees or groups of trees are categorized as a patch and in these forests
canopy patches on the landscape were dominated by many small patches This trend was steady
across all spatial extents The metric was amplified by a higher number of small trees and density
decreased by the area sampled For both LiDAR and NAIP patch density was not sensitive to the
extent measured However using Landsat-8 data (30 m grain) and Sentinel-2 data (10 m grain) patch
density decreased with spatial extent by nearly an order of magnitude from smallest (10 ha) to largest
(300 ha) This correlates with the inability of these spatial products at those spatial grains to detect
small patches Landsat-8-derived patch density at a 10-ha extent was limited by this extent and showed
higher patch density
Patch density (PD) and mean patch area (MPA) are closely related and remotely sensed products
with high PD will have lower MPA For example On Tepeats Creek at 50 ha extent LiDAR predicted
the mean patch area to be 0046 ha in size NAIP predicted it to be 0006 ha in size Sentinel-2 predicted
1096 and Landsat-8 predicted 139 ha in size (Table 4 Figure 5) The effect of extent was similar to the
patterns in PD Landsat-8 and Sentinel-2 data had an increasing MPA with an increased spatial extent
on all three landscapes which was expected due to the larger cell resolution that samples the data
NAIP and LiDAR showed little sensitivity to spatial extents (Table 4)
Aggregation (AI) was high on all three landscapes (Figure 6 Table 5) and varied little across
all landscapes as estimated using LiDAR (ie reported standard errors ranged from 0001 to 0192)
The NAIP had consistently lower AI values than the other products on Tepeats and Windmill Draw
areas This was directly related to the native cell resolution of NAIP at 1 m the high PD and low
MPA Landscape Aggregation was well estimated using the Landsat-8 data This was due to the larger
cell resolution (30 m) which tended to form more contiguous patches NAIP at 1 m cell resolution
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 16 of 21
detected more discrete single patches that are not aggregated At the landscape-scale NAIP at 1 m cell
resolution under-predicted aggregation All products were consistent across extents showing little
sensitivity to extent
Patch Cohesion (PC) increased as canopy cover became more clumped or aggregated in its
distribution and therefore more physically connected All three landscapes had a high patch cohesion
consistent with the aggregation index and supporting the observation that canopy cover was highly
connected This pattern was found across all products and landscapes (Figure 7 Table 6) Although
NAIP had much different values than LiDAR on patch description metrics (PLand PD MPA) for patch
cohesion it was more similar to LiDAR on all three landscapes and across all extents Sentinel-2
performed well at estimating patch cohesion across all spatial extents (Table 6) Landsat-8 consistently
under-predicted patch cohesion at smaller extents (10ndash25 ha) and was more accurate at predicting
patch cohesion at ge100 ha (Table 6) Patch cohesion was sensitive to the aggregation of canopy cover
and increased as canopy cover becomes more clustered or aggregated in its distribution
Landscape division index (LDI) is the probability that two randomly chosen pixels in the landscape
are not in the same patch and is based on the cumulative patch area distribution NAIP had a high
probability of landscape division across all spatial extents as it tended to break up large patches
into many smaller patches yet it was accurate on Windmill Draw (Table 7) Landsat-8 had a lower
probability of LDI because it measured canopy cover across the landscape as a smaller number of
larger sized patches The four data products displayed variability of patterns across spatial extents
due to their native cell resolutions NAIP showed consistent unimodal distributions of LDI at smaller
extents LiDAR only showed this at the Windmill Draw landscape
4 Discussion
This analysis provided insights into remotely sensed data products their native cell resolutions
(grain) and how metrics can describe patch and landscape characteristics of canopy cover We examined
six forest canopy patch metrics and described their differences across data products (Landsat-8 30 m
Sentinel-2 10 m NAIP 1 m and LiDAR 1 m) spatial extent and among study landscapes LiDAR
was used as a reference for evaluating landscapes as well as for assessing the accuracy of the other
products In the United States little resources are available for ground-based assessment and remote
sensing techniques are increasingly explored for usability with minimal ground-truthing Our accuracy
assessment on each remotely sensed data product put us within the acceptable range of accuracy for
spatial products [45]
Spatial heterogeneity directly affects ecosystem patterns and processes but can be difficult
to measure in a consistent way to inform federal land management priorities Heterogeneity is
characterized by a focal patch type interacting with the mosaic of surrounding patches or patch
types across an area of interest [46] and can influence a variety of ecological responses including
animal movement species interactions fire spread and behavior and overall ecosystem function [47]
For land managers landscape heterogeneity is important to support multiple seral stages as habitat
for wildlife species biodiversity ecosystem function and can aid in characterizing fuels and wildfire
spread [48ndash51] We chose a variety of metrics to quantify landscape pattern attributes and assess
sensitivity to management practices found in western frequent-fire forests
Patch attributes such as mean patch density (PD) that depend on discriminating smaller patches
are highly underestimated by coarser grained products (Landsat-8 and Sentinel-2) The grain of both
Landsat-8 and Sentinel-2 is too large to detect individual small patches Small patches are lumped
together resulting in fewer but larger patches The proportion of landscape of canopy cover (PLand)
can be a highly variable metric especially at smaller extents (10ndash25 ha) but is more stable at larger
extents (ge300 ha) On all three landscapes fine-scale heterogeneity becomes harder to detect by each
data product at scales 300 ha and larger On our highly managed landscape Windmill Draw there is
less fine-scale landscape variability of canopy cover Landsat-8 (30 m grain) and Sentinel-2 (10 m grain)
over-predicted proportion of canopy cover because each pixel size is larger so when the pixel moves
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 17 of 21
across the landscape at various spatial extents it often finds canopy cover in the pixel and classifies it
as cover (See Figure 2) On the other hand NAIP under-predicted PLand across all landscapes and
spatial extents because the 1 m data product cell resolution was too small to accurately estimate the
true pattern of PLand across the landscapes
Canopy cover is a difficult metric to use solely to assess forest conditions and forest restoration
and fuels reduction projects [52ndash54] However canopy cover has been incorporated into southwestern
US forest service directives (USDA FS Region 3) including forest planning as a surrogate for wildlife
habitat Canopy patterns measured at smaller scales do not necessarily scale up appropriately and the
processes identified at small scales are not always the drivers at larger spatial scales When this happens
incorrect inferences can be made for the phenomena or species of interest and incorrect management
actions may be chosen Canopy cover should be selected at the appropriate scale for the analysis
or phenomena of interest including appropriate grain (cell resolution) and spatial extent Canopy
cover-informed spatial metrics combined with additional forest structural information including
tree size (height and diameter) age species composition and tree densities are all important factors
in determining if management actions have restored forest structure to within the natural range
of variability [55ndash57] Used together these variables are more informative for quantifying wildlife
habitat potential fire behavior landscape connectivity and forest restoration success and resilience to
disturbances [5157ndash59]
Heterogeneity in frequent-fire ponderosa pine forests has been captured at fine scales [60]
To capture this remotely LiDAR is a very robust data source that can accurately produce models of
canopy cover and openings due to the inherent quality of the data NAIP has a very small (le1 m)
cell resolution and the user has to classify shadows bare ground shrubs and more therefore there
are inherent classification errors at the 1 m Re-scaling NAIP data to a minimum cell resolution of
25 m could help reduce some classification error and increase the accuracy of a binary canopynot
canopy classification From a management perspective and in a landscape ecology context forest
heterogeneity is often characterized on large landscapes (1000primesndash10000primes of hectares in size) where
patterns of large-scale heterogeneity are often detected Southwestern US forests that evolved with
frequent fire have fine-scale heterogeneity [60] which is often harder to describe in a binary landscape
As the USDA Forest Service is tasked to meet urgent goals of managing for forest resiliency to future
disturbances metrics of fine-scale heterogeneity and the methods to summarize these metrics across
large scales will be useful to assess treatment success Using other information in tandem with canopy
cover such as tree size and age classes structural composition and species composition will be more
informative and helpful in elucidating landscape patterns and processes than using canopy cover
alone Although analyses should include structural and compositional variables remotely sensed data
acquisitions may be faster and better assessed to quickly inform adaptive management
Limitations of This Study
In this study we calculated landscape metrics on a single class (canopy cover) and therefore can
only make inferences on canopy cover and canopy cover-related landscape metrics We did not describe
spatial patterns of patches and openings nor make comparisons among classes Additionally our three
landscapes varied in size and therefore landscape metrics were calculated on different maximum
extents Differences in data product and grain (cell resolution) caused differences in how landscape
patterns of canopy cover were calculated and were more evident than differences due to spatial extent
Accuracy of unsupervised classification of our data products ranged from 56 to 85 and while
acceptable by most classification standards this is still an important source of uncertainty The LiDAR
data used in this analysis is the best estimate of canopy cover in our study area and interpretations of
results should be made accordingly
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 18 of 21
5 Conclusions
Spatial products at different resolutions performed differently across patch metrics and across our
three landscapes Overall Sentinel-2 data at 10 m and NAIP at 1 m resolution most often successfully
characterized patch metrics in our analysis compared to LiDAR Landsat-8 varied in its ability to
accurately characterize patch metrics due to its coarse cell resolution of 30 m while NAIP at 1 m
resolution often underestimated canopy cover patterns Metrics that describe spatial connectedness
and distribution of patches such as aggregation index and patch cohesion index were well described
by all three spatial data products LiDAR data provide the most accurate and high-resolution data
that can be used to characterize forest structure tree spatial patterns and canopy metrics on our three
landscapes in northern Arizona USA Analyses may benefit from using more than one spatial data
product at different grains and at multiple spatial extents to describe forest heterogeneity and structural
characteristics within a landscape
Author Contributions Conceptualization TNW AJSM AEMW methodology TNW AJSM AEMWsoftware AJSM validation TNW and AJSM formal analysis TNW and AJSM investigation TNWand AJSM data curation AJSM writingmdashoriginal draft preparation TNW AJSM and AEMWwritingmdashreview amp editing TNW and AJSM visualization AJSM
Funding This report was funded by a grant from the US Forest Service NAU is an equal opportunity providerThe APC was funded by the Ecological Restoration Institute
Acknowledgments We would like to thank the Ecological Restoration Institute for funding the Kaibab andCoconino National Forests and the Southwestern Regional Office for access to the LiDAR data
Conflicts of Interest The authors declare no conflict of interest
References
1 USDA Forest Service National Forest System Land Management Planning Available online https
wwwfsusdagovplanningrule (accessed on 4 February 2019)
2 USDA Forest Service Forest Service Handbook USDA Forest Service Washington DC USA 2015
3 Calkin DE Thompson MP Finney MA Negative consequences of positive feedbacks in US wildfire
management For Ecosyst 2015 2 1418 [CrossRef]
4 Westerling AL Hidalgo HG Cayan DR Swetnam TW Warming and Earlier Spring Increase Western
US Forest Wildfire Activity Science 2006 313 940ndash943 [CrossRef]
5 Westerling AL Increasing western US forest wildfire activity Sensitivity to changes in the timing of spring
Philos Trans Soc B Boil Sci 2016 371 20150178 [CrossRef] [PubMed]
6 GAO Forest Restoration Adjusting Agenciesrsquo Information-Sharing Strategies Could Benefit Landscape-Scale Projects
GAO-15-398 United States Government Accountability Office Washington DC USA 2015
7 USDA Forest Service Increasing the Pace of Restoration and Job Creation on Our National Forests USFS Report
United States Department of Agriculture Forest Service Washington DC USA 2012
8 United States Department of Agriculture Forest Service (USDA FS) From Accelerating Restoration to Creating
and Maintaining Resilient Landscapes and Communities across the Nation Update on Progress from 2012 FS-1069
United States Department of Agriculture Forest Service Washington DC USA 2015
9 Collaborative Forest Landscape Restoration Program Available online httpswwwfsfedusrestoration
documentscflrptitleIVpdf (accessed on 4 February 2019)
10 Turner MG Gardner RH OrsquoNeill RV Landscape Ecology in Theory and Practice Pattern and Process
Springer New York NY USA 2001
11 Abella SR Denton CW Spatial variation in reference conditions Historical tree density and pattern on a
Pinus ponderosa landscape Can J 2009 39 2391ndash2403 [CrossRef]
12 Dickinson Y Pelz K Giles E Howie J Have we been successful Monitoring horizontal forest complexity
for forest restoration projects Restor Ecol 2015 24 8ndash17 [CrossRef]
13 Chapin FS Matson PA Vitousek PM Iii FSC Landscape Heterogeneity and Ecosystem Dynamics
In Principles of Terrestrial Ecosystem Ecology Springer Nature Basingstoke UK 2011 pp 369ndash397
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 19 of 21
14 Farina A Principles and Methods in Landscape Ecology Toward a Science of Landscape Springer Dordrecht
The Netherlands 2006
15 Wiens JA Spatial Scaling in Ecology Funct Eco 1989 3 385ndash397 [CrossRef]
16 Forman RTT Godron M Landscape Ecology John Wiley amp Sons New York NY USA 1986 p 620
17 Turner MG Dale VH Gardner RH Predicting across scales Theory development and testing Landsc
Ecol 1989 3 245ndash252 [CrossRef]
18 Gustafson EJ Lytle DE Swaty R Loehl C Simulating the cumulative effects of multiple forest
management strategies on landscape measures of forest sustainability Landsc Eco 2007 22 141ndash156
[CrossRef]
19 Spies TA Ripple WJ Bradshaw GA Dynamics and Pattern of a Managed Coniferous Forest Landscape
in Oregon Ecol Appl 1994 4 555ndash568 [CrossRef]
20 Wu H Li Z-L Scale Issues in Remote Sensing A Review on Analysis Processing and Modeling Sensors
2009 9 1768ndash1793 [CrossRef] [PubMed]
21 Means JE Acker SA Fitt BJ Renslow M Emerson L Hendrix CJ Predicting forest stand characteristics
with airborne scanning lidar Photogram Eng Remote Sens 2000 66 1367ndash1371
22 Lefsky MA Hudak AT Cohen WB Acker S Geographic variability in lidar predictions of forest stand
structure in the Pacific Northwest Remote Sens Environ 2005 95 532ndash548 [CrossRef]
23 Kane VR Lutz JA Roberts SL Smith DF McGaughey RJ Povak NA Brooks ML Landscape-scale
effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park Ecol Manag
2013 287 17ndash31 [CrossRef]
24 Stephens SL Lydersen JM Collins BM Fry DL Meyer MD Historical and current landscape-scale
ponderosa pine and mixed conifer forest structure in the Southern Sierra Nevada Ecosphere 2015 6 1ndash63
[CrossRef]
25 A Jeronimo SM Kane VR Churchill DJ McGaughey RJ Franklin JF Applying LiDAR Individual Tree
Detection to Management of Structurally Diverse Forest Landscapes J For 2018 116 336ndash346 [CrossRef]
26 Churchill DJ Larson AJ Dahlgreen MC Franklin JF Hessburg PF Lutz JA Restoring forest
resilience From reference spatial patterns to silvicultural prescriptions and monitoring Ecol Manag 2013
291 442ndash457 [CrossRef]
27 Wiggins HL Nelson CR Larson AJ Safford HD Using LiDAR to develop high-resolution reference
models of forest structure and spatial pattern Ecol Manag 2019 434 318ndash330 [CrossRef]
28 Fuleacute PZ Heinlein TA Covington WW Moore MM Assessing fire regimes on Grand Canyon landscapes
with fire-scar and fire-record data Int J Wildland Fire 2004 12 129ndash145 [CrossRef]
29 Covington WW Moore MM Moore MM Southwestern ponderosa pine forest structure Changes since
Euro-American settlement J For 1994 92 39ndash47
30 Sugarbaker LJ Constance EW Heidemann HK Jason AL Lukas V Saghy DL Stoker JM The 3D
Elevation Program Initiative A Call for Action US Geological Survey Circular Reston VA USA 2014 p 35
31 Axelsson P DEM Generation from Laser Scanner Data Using TIN Models The International Archives of the
Photogrammetry and Remote Sensing Amsterdam The Netherlands 2000 pp 110ndash117
32 Gorelick N Hancher M Dixon M Ilyushchenko S Thau D Moore R Google Earth Engine
Planetary-scale geospatial analysis for everyone Remote Sens Environ 2017 202 18ndash27 [CrossRef]
33 Pelz KA Dickinson YL Monitoring Forest Cover Spatial Patterns with Aerial Imagery A Tutorial
Colorado Forest Restoration Institute Colorado State University Fort Collins CO USA 2014 p 43
34 European Space Agency (ESA) Available online wwwesaintESA (accessed on 4 February 2019)
35 Muumlller-Wilm U Sen2Cor 255mdashSoftware Release Note Available online httpsstepesaintmainnew-
release-of-sen2cor-2-5-5-is-available (accessed on 4 February 2019)
36 FAO FRA2000 on Definitions of Forest and Forest Change FRA Working Paper 33 Available online
httpwwwfaoorg3ad665ead665e00htm (accessed on 4 February 2019)
37 Hansen MC Stehman SV Potapov PV Quantification of global gross forest cover loss Proc Natl Acad
Sci USA 2010 107 8650ndash8655 [CrossRef]
38 Foga S Scaramuzza PL Guo S Zhu Z Dilley RD Beckmann T Schmidt GL Dwyer JL Hughes MJ
Laue B Cloud detection algorithm comparison and validation for operational Landsat data products Remote
Sens Environ 2017 194 379ndash390 [CrossRef]
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 20 of 21
39 National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center Lidar 101
An Introduction to Lidar Technology Data and Applications NOAA Coastal Services Center Charleston
SC USA 2012
40 Maltamo M Naesset E Forestry applications of airborne laser scanning concepts and case studies
In Managing Forest Ecosystems Managing Forest Ecosystems Springer Dordrecht The Netherland 2014
41 Gatziolis D Comparison of lidar- and photointerpretation-based estimates of canopy cover In Monitoring
across Borders 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern
Mensurationists McWilliams W Roesch FA Eds e-Gen Technical report SRS-157 US Department of
Agriculture Forest Service Southern Research Station Asheville NC USA 2012 pp 231ndash235
42 Korhonen L Ali-Sisto D Tokola T Tropical forest canopy cover estimation using satellite imagery and
airborne lidar reference data Silva Fenn 2015 49 18 [CrossRef]
43 Ma Q Su Y Guo Q Comparison of Canopy Cover Estimations From Airborne LiDAR Aerial Imagery
and Satellite Imagery IEEE J Sel Top Appl Earth Obs Sens 2017 10 4225ndash4236 [CrossRef]
44 McGarigal K Cushman SA Neel MC Ene E FRAGSTATS Spatial Pattern Analysis Program for
Categorical Maps Available online wwwumassedulandecoresearchfragstatsfragstatshtml (accessed on
4 February 2019)
45 Homer C Dewitz J Yang L Jin S Danielson P Xian G Coulston J Herold N Wickham J Megown K
Completion of the 2011 National Land Cover Database for the conterminous United Statesndashrepresenting a
decade of land cover change information Photogram Eng Remote Sens 2015 81 345ndash354
46 Forman TT Some general principles of landscape and regional ecology Landsc Eco 1995 10 133ndash142
[CrossRef]
47 Fahrig L Non-optimal animal movement in human-altered landscapes Funct Ecol 2007 21 1003ndash1015
[CrossRef]
48 Wiens JA Central Concepts and Issues of Landscape Ecology In Applying Landscape Ecology in Biological
Conservation Springer Nature Basingstoke UK 2002 pp 3ndash21
49 Lovett GM Jones CG Turner MG Weathers KC Ecosystem Function in Heterogeneous Landscapes
Springer New York NY USA 2005
50 Fahrig L Baudry J Brotons L Burel FG Crist TO Fuller RJ Sirami C Siriwardena GM Martin J
Functional landscape heterogeneity and animal biodiversity in agricultural landscapes Eco Lett 2011 14
101ndash112 [CrossRef]
51 Reynolds RT Meador AJSM Youtz JA Nicolet T Matonis MS Jackson PL DeLorenzo DG
Graves AD Restoring Composition and Structure in Southwestern Frequent-Fire Forests A Science-Based
Framework for Improving Ecosystem Resiliency Gen Tech Rep RMRSGTR-310 Department of Agriculture
Forest Service Rocky Mountain Research Station Fort Collins CO USA 2013 p 2013
52 Huffman DW Saacutenchez-Meador AJ Greco B Canopy Cover and How it Relates to Other Forest Attributes
as an Indicator of Forest Conditions Available online httpscdm17192contentdmoclcorgdigitalcollection
p17192coll1id861rec25 (accessed on 4 February 2019)
53 Roccaforte JP Fuleacute PZ Covington WW Landscape-scale changes in canopy fuels and potential fire
behaviour following ponderosa pine restoration treatments Int J Wildland Fire 2008 17 293ndash303 [CrossRef]
54 Agee JK Skinner CN Basic principles of forest fuel reduction treatments Ecol Manag 2005 211 83ndash96
[CrossRef]
55 Fuleacute PZ Covington WW Moore MM Determining reference conditions for forest ecosystem management
pf southwestern Ponderosa pine forests Ecol Appl 1997 7 895ndash908 [CrossRef]
56 Saacutenchez-Meador AJ Parysow PF Moore MM A new method for delineating tree patches and assessing
spatial reference conditions of Ponderosa pine forests in northern Arizona Restor Eco 2011 19 490ndash499
[CrossRef]
57 Battaglia MA Gannon B Brown PM Fornwalt PJ Cheng AS Huckaby LS Changes in forest
structure since 1860 in ponderosa pine dominated forests in the Colorado and Wyoming Front Range USA
Ecol Manag 2018 422 147ndash160 [CrossRef]
58 Covington WW Moore MM Heinlein TA Waltz AEM Fuleacute PZ Natural variability in forests of the
Grand Canyon USA J Biogeogr 2002 29 31ndash47
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-
Forests 2019 10 465 21 of 21
59 Dickson BG Sisk TD Sesnie SE Reynolds RT Rosenstock SS Vojta CD Ingraldi MF Rundall JM
Integrating single-species management and landscape conservation using regional habitat occurrence models
The northern goshawk in the Southwest USA Landsc Ecol 2014 29 803ndash815 [CrossRef]
60 Saacutenchez-Meador AJ Moore MM Bakker JD Parysow PF 108 years of change in spatial pattern
following selective harvest of a ponderosa pine stand in northern Arizona USA J Veg Sci 2009 20 79ndash90
copy 2019 by the authors Licensee MDPI Basel Switzerland This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (httpcreativecommonsorglicensesby40)
- Introduction
- Materials and Methods
-
- Study Area
- Remotely Sensed Data
- Use of LiDAR as Reference Data
-
- Results
-
- Ability of the Four Spatial Products (Landsat-8 Sentinel-2 NAIP and LiDAR) to Predict Metrics of Canopy Cover
- Forest Canopy Patch Metrics across Remotely-Sensed Data Product Landscape and Extent
-
- Discussion
- Conclusions
- References
-