Remote Sensing of Canada’s Forest Properties · Landsat-based 30-m disturbance mapping 1984-2015...
Transcript of Remote Sensing of Canada’s Forest Properties · Landsat-based 30-m disturbance mapping 1984-2015...
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Remote Sensing of Canada’s Forest Properties:
An Invaluable Toolbox to Answer Key Forest Science Questions
CIF E-Lecture, February 20th 2019
André Beaudoin, NRCan/CFS/LFC
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Outline
• Introduction
• National forest attribute maps at 250 m resolution:
– Initial & Improved maps
– Applications
• Regional/national attribute maps at 30 m resolution:– Forest Inventory in Northwestern Canada
– Multi-source National Biomass Map of Boreal Forests
• Conclusion & Perspective
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Acknowledgement of Many Contributors
• CFS/LFC: P. Bernier, L. Guindon, P. Villemaire, R. St-Amant, K. Powell, M. Marchand, J. Guo, C. Gignac; S. Gauthier, Y. Boulanger, D. Boucher, D. Paré, N. Mansuy, K. Sambaraju …
• CFS/NoFC: G. Castilla, R. Hall, R. Skakun, M. Filiatrault, M. Gartrell; D. Thompson, B. Simpson…
• U. Lethbridge: C. Hopkinson, C. Mahoney • Gov’t of NWT: L. Smith, K. Groenewegen• CFS/PFC/National Forest Inventory: G. Stinson, S. Magnussen,
A. Dyk
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Programs and Support
• CFS: – Sustainable Forest Management program
– CFS Forest Change Initiative
• Government of NWT, MFFPQ
• Space agencies:
– Canadian Space Agency (GRIP program)
– Japanese Space Agency
• Global Forest Observation Initiative
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Introduction: Context
• Key knowledge of Canada’s forests through National Forest Inventory (NFI)
– 2 x 2 km NFI photo-plots: 1% sample of Canada– Compilation of provincial inventories– Statistical inference of key attributes by ecozone
• Increased need for consistent and frequent wall-to-wall map products of forest properties across Canada for reporting and modelling purposes
• Multi-source/scale/temporal remote sensing (RS) data are needed to cover the remaining 99%
• Towards dynamic national forest mapping: Landcover/Disturbances/Forest attributes
5
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Introduction: Spaceborne Remote Sensing
• Spaceborne remote sensing sources: increasingly available! • Criteria: spaceborne, free, archived & highly processed, multi-yeartime series,
covers all of Canada, moderate resolution (25 - 250 m) – Passive, Optical:
• MODIS, multi-spectral, 250 m, yearly summer and winter composite, 2001 - 2015 (NASA/CCMEO)
• Landsat 5/7/8, multi-spectral, 30 m, dense time series, 1986 - current (NASA/USGS/CFS)
– Active, Radar: • L-band PALSAR, dual-polarized backscatter (HH, HV), 25 m,
2007-2010; 2014-current (JAXA/CFS)
– Active, LiDAR: • Airborne LiDAR survey• Spaceborne ICESAT-GLAS (NASA)
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Introduction: Active vs Passive RS
MODISLandsat
PALSAR ICESAT-GLAS
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Introduction: RS Sources and Reference Sets
…
…
Selected reference sets over boreal forests
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Introduction: Modelling Attributes from
RS and Ancillary Variables
• Simple to implement, yet robust and flexible
• Proven efficient in other RS-based large area/national forestinventories (Finland, Sweden, USA…)
• Non-parametric machine learning algorithms:
– k Nearest Neighbours (k-NN)
– Random Forests (RF)
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Introduction: Method Workflow
Modelling:optimization/
validation
Reference sampling set
(cal/val)
RS features
Ancillaryfeatures
(topography, climate…)
Independent validation set
Attributemaps
Mapping
Validation
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
NFI photo-plot 1% sample
Maps of 123 NFI forest attributes: - 4 land cover classes (treed, non-treed…) - 11 structure (height, volume, biomass…) - 108 species compositions
2001 MODIS Imagery 250 m(CCMEO)
TopographyClimate
k-NNalgorithm
National 250 m maps: Initial 2001 V0 maps
2014: First national 2001 maps of forest attributes based on 250 m MODIS imagery and NFI photo-plots using k-NN interpolation
Beaudoin et al., 2014, CJFR
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
National 250 m maps: Initial V0 maps
• Limitations: – Resolution: moderate, still relevant!
– Accuracy: • Poor to good; under/over-estimation
• Poorest: – mountainous areas; taiga ecozones
– rare species; age
– 2001: outdated
– Static vs dynamic for tracking changes
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Improved ref set from NFI photo-plot 1% sample
Maps of 93 NFI forest attributes: - 4 land cover classes (treed, non-treed…) - 11 structure (height, volume, biomass…) - 78 species compositions
2001-2011 MODIS 250 m(CCMEO)
TopographyClimate
Stratified/optimized
k-NNalgorithm
National 250 m maps: Improved V1 maps
2017: improved 2001 maps updated to 2011
Beaudoin et al., 2018, CJFR
2001
2011
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
• Inclusion of various ancillary datasets : – Tree cover*, 2000 (TC, %)
->updated to 2001 and 2011
– Four change classes, 2001-2011 & Fractional Change (FC, %):
• No-change*, **
• Cover loss**: Fire, Harvest
• Cover gain*: regrowth for disturbances prior to 2001
(** Guindon et al. 2014) (*Hansen et al. 2013)
National 250 m maps: Improved V1 maps
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
MODIS-based 250-m disturbance mapping 2000-2011
Fires
Forest harvesting
Flooding
Fires
Forest harvesting
Flooding
Guindon et al., 2014, CJFR
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Landsat-based 30-m disturbance mapping 1984-2015 (CanLAD)
Guindon et al., 2018, Ecosphere
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Landsat-based 30-m disturbance mapping 1984-2015 (CanLAD): exemples of cumulative map of fire and harvesting
Validation• Fires : NBAC 14.4 Mha = 85%• Harvesting: Provincial Inventory
(2.2 Mha)= 85% • Our Validation set = 91%
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
• Widely distributed through NFIS portal (V0) then Open Canada (V1);
One of key national geospatial products available for many applications
Guindon et al.
(2014) CJFR Beaudoin et al.
(2014) CJFR
Boulanger et al.
(2014) CJFR
NFI program
Annual disturbance
mapping
Forest
attributes
Regional fire risks
Led by PFC
National V0/V1 maps: Applications
Beaudoin et al.
(2017) CJFR
Guindon et al.
(2018)
Ecosphere
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Reporting:
State of Canada’s Forests Annual Report
https://www.nrcan.gc.ca/forests/report/16496http://cfs.nrcan.gc.ca/publications?id=39333
20182016
https://nfi.nfis.org/img/knnDownloadImages/kNN_Genus_Dominant_2km_en.png
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Value-added products: FBP Fuel Grid
• FBP Fuel Grid: The Fire Behaviour Prediction (FBP) component of the Canadian Forest Fire Danger Rating System (CFFDRS) uses 16 benchmark fuel types to predict fire behaviour
http://cwfis.cfs.nrcan.gc.ca/downloads/fuels/development/National_Risk_Analysis_Fuels_Map/
ContactsBrian Simpson Canadian Forest Service / Northern Forestry Centre E-mail: [email protected]
Tom SwystunCanadian Forest Service / Great Lake Forestry CentreE-mail: [email protected]
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Value-added products: Peatland Map
Contact
Daniel Thompson
Natural Resources Canada
Canadian Forest Service / Northern Forestry
Centre
Thompson et al., 2016, For. Ecol. Man.
Probability of presence of peatland (%)
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Mountain pine beetle: Lodgepole pine Ash borer: Ash species
Value-added products: Host Species Maps of Key Pests
USFS maps: https://www.nrs.fs.fed.us/people/Wilson
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Value-added products: North American Biomass Map
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Proportion of productivity taken by fire + harvesting:
2011-2040
What is the vulnerability of timber supply under current
harvesting rate and current/future fire regime?
Gauthier et al., 2015, CJFR
harvest > Productivity - fire
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
What is the fire selectivity relative to cover types?
Bernier et al., 2016, Forests
Broadleaved
Mixed-Broadleaved
Mixed-Needleleaved
Needleleaved
Mature
Old
Young
Random
Preferred
Avoided
25
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
What is the fire risk according to
vegetation type?
Short
Medium
Long
Return interval
Bernier et al., 2016, Forests
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Climate + vegetation
Climate
Boulanger et al., 2017, CJFR
What is the impact of vegetation reaction to fire on
projected fire behavior?
27
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017Mansuy et al. 2017
Biomass from
fires
Biomass from
harvest residues
Where do fires and harvesting provide
the most biomass for bioenergy?
28
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
What are the current and projected cumulative effects of disturbances (fire, harvest, drought, insects) on wood volume?
Boucher et al., 2018, Ecological Applications.
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Can we track decadal attribute changes using 2001
and 2011 national maps?
unknown
• Integration of:
- Improved 2001 & 2011 kNN 250 m maps: AGB, TREED & NLS,
2001 to 2011, 30 m CanLAD change and Hansen cover loss maps:
% fire, harvest & unknown
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Global change in needle-leaf proportion (NLS, %) Global change in treed proportion (TREED, %)
• Decadal change estimates summarized over 20 x 20 km units centered of NFI photo-plots
Global change in biomass (AGB, t/ha)
Can we track decadal attribute changes using 2001
and 2011 national maps?
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
∆𝐴𝐺𝐵 = 𝐺𝐴𝐺𝐵 − (𝐿𝐴𝐺𝐵𝑓𝑖𝑟𝑒+ 𝐿𝐴𝐺𝐵ℎ𝑎𝑟𝑣𝑒𝑠𝑡 + 𝐿𝐴𝐺𝐵𝑢𝑛𝑘𝑛𝑜𝑤𝑛) + 𝑒𝐴𝐺𝐵
• AGB global change with five modelled change components:
Global change in AGB DAGB (t/ha) Change driver: dominant change component
1.2 2001-2011 drivers of attribute changes across Canada
Beaudoin et al., 2019, in prep
What are the key drivers of
attribute changes across Canada ?
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
• Access to national map products along with peer-reviewed articles:
• Maps of Canada’s forest attributes: • Initial 2001 V0 maps:
https://nfi.nfis.org/en/maps• Beaudoin, A., Bernier, P.Y., Guindon, L., Villemaire, P., Guo, X.J., Stinson, G., Bergeron, T., Magnussen, S., and Hall,
R.J. 2014. Mapping attributes of Canada’s forests at moderate resolution through kNN and MODIS imagery. Canadian Journal of Forest Research, 44(5): 521–532. doi:10.1139/cjfr-2013-0401.
• Improved 2001 and 2011 V1 maps:https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990.
• Beaudoin, A., Bernier, P.Y., Villemaire, P., Guindon, L., and Guo, X. 2018. Tracking forest attributes across Canada between 2001 and 2011 using a k nearest neighbours mapping approach applied to MODIS imagery. Canadian Journal of Forest Research, 48(1): 85-93. doi:10.1139/cjfr-2017-0184.
• Disturbance maps (CanLAD): • 30-m resolution change detection and attribution (fire and harvest) product from 1984 onwards (CanLaD):
https://open.canada.ca/data/en/dataset/add1346b-f632-4eb9-a83d-a662b38655ad.• Guindon, L., Villemaire, P., St-Amant, R., Bernier, P.Y., Beaudoin, A., Caron, F., Bonucelli, M., and Dorion, H., 2017.
Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30 m resolution product of fire and harvest detection and attribution since 1984. doi:10.23687/add1346b-f632-4eb9-a83d-a662b38655ad.
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
• Some of the peer-reviewed articles based on the use of national RS-based attribute maps and disturbance maps
• Boucher et al. 2018. Current and projected impacts of fire, drought and insects on timber volume across Canada. Ecological Applications. doi.org/10.1002/eap.1724.
• Boulanger et al. 2017. Changes in mean forest age in Canada’s forests could limit future increases in area burned but compromise potential harvestable conifer volumes. Canadian Journal of Forest Research 47(6): 755-764. doi:10.1139/cjfr-2016-0445.
• Mansuy et al. 2017. Estimating the spatial distribution and locating hotspots of forest biomass fromharvest residues and fire-damaged stands in Canad’s managed forests. Biomass and Bioenergy97:90:99. doi.org/10.1016/j.biombioe.2016.12.014.
• Boulanger, et al. 2016. Climate change impacts on forest landscapes along the Canadian southern boreal forest transition zone. Landscape Ecology, 32(7):1415-1431. doi:10.1007/s10980-016-0421-7.
• Bernier et al. 2016. Mapping local effects of forest properties on fire risk across Canada. Forests 7(8), 157. doi:10.3390/f7080157.
• Thompson et al. 2016. Using forest structure to predict the distribution of treed boreal peatlands in Canada. Forest Ecology and Management 372:19-27. doi:10.1016/j.foreco.2016.03.056.
• Gauthier et al. 2015. Vulnerability of timber supply to projected changes in fire regime in Canada’s managed forests. Canadian Journal of Forest Research 45(11):1439-1447. doi.org/10.1139/cjfr-2015-0079
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Satellite-based Forest Inventory in Northwestern Canada
* Excerpt from a conference presented at ForestSAT 2018, College Park MD, Oct 9
G Castilla1, R Hall1, A Beaudoin2, R Skakun1, M Filiatrault1, M Gartrell1, C Mahoney3, C Hopkinson3, P Villemaire2, L Guindon2, L Smith4, K Groenewegen4
1 Canadian Forest Service, Northern Forestry Centre2 Canadian Forest Service, Laurentian Forestry Centre3 University of Lethbridge, Department of Geography4 Government of Northwest Territories, Forest Management Division
Satellite-based Forest Inventory in Northwestern Canada*
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
IntroductionContext:
• Canada-wide Earth Observation for the Sustainable Development of forests (EOSD) land cover map c. 2000 was the only source of information for most Northwest Territories (NWT) forests.
• GNWT and CFS produced c.2007 EOSD in southern NWT, and wondered:Could an approach based on scaling field, multi-sensor remote sensing data and models be used to estimate forest structure as value-added products to the new EOSD map that could complement GNWT forest inventory (<10% of NWT forests)?
Objective:
• Estimate stand height, crown closure, stand volume, total volume, aboveground biomass (AGB), and age in 30 m cells deemed forested by the land cover map.
Approach:
• Integrate field, airborne laser scanning (ALS), ICESat’s Geoscience Laser Altimeter System (GLAS) data, optical (Landsat) and SAR (PALSAR) imagery, and models to estimate and map forest attributes.
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Study Area
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Amplitude (volts)
Ca
no
py
He
igh
t
Top of Canopy
Ground
Heig
ht (m
)
Total Biomass
Total Volume
Stand Volume
Crown Closure
Stand Age
Stand Height
Field data
EOSD
Modelling
ALS
GLAS
Mapping
GLAS
K-NNpredictors
IndependentALS
K-NN
Assessment
Conifer
Deciduous
Mixedwood
Wetland treed
Methods
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Field models of volume and biomass
Use of field measured stand/Lorey height to estimate stand attributes follows approach of Saatchi et al. (2011) PNAS 108: 9899-9904.
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Modeling approach:1. Airborne laser scanning data processed with Fusion
2. Fusion metrics related to stand attributes in 38 field plots
3. Attributes: Stand height, Lorey height, crown closure
NB: Stand height and Lorey height used for GLAS model calibration
ALS Stand Attribute Models
Variable Model Adj R2 RMSEStand ht (m) 0.53 + 0.96 P95 0.89 1.4 m
Lorey ht (m) 0.64 + 0.84 P95 0.89 1.2 m
Crown closure (%) 77.06 x Lz0.25 0.63 5%
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
ICESat - GLAS
Objective: to create a large set of surrogate forest inventory plots for k-NN FI mapping
GLAS: Geoscience Laser Altimeter System
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
GLAS Models using ALS
Model Adjusted R2 RMSE
Lorey ht (m) = 2.46 + 0.91 p85 0.89 1.1 m
Stand ht (m) = 2.05 + 1.12 p85 0.89 1.4 m
Crown closure (%) = 78.74 Lz0.26 0.55 6.4 %
N=43
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
4.4 k-NN mapping & 4.3 accuracy assessment
• K-NN Mapping C++ code (CFS/LFC) used to map the five attributes across C,D,M and AGB over treed wetland land cover classes
Crown closure (%) Stand height (m)
Stand volume (m3/ha)
Total volume (m3/ha)
AGB (t/ha)
k-NN mapping
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
1 35 1 100 1 500
Stand Height (m) Crown Closure (%) Total Volume (m3/ha)
Phase 1
Phase 2
Results
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
k-NN ValidationGoals• To provide confidence intervals for the mean pixel-wise bias and absolute error for each forest attribute:
• To assess the spatial distribution of errors along the Boreal Transect ALS
Materials• Available NFI ground plots (~80, measured in 2002-2006)
• Boreal Transect ALS metrics in forested 25 m cells
• Collocated (to all of the above) k-NN pixels
Methods• NFI forest attributes computed from raw ground data using same methods as MVI ground plots and plot coordinates used to sample the k-NN rasters
• k-NN crown closure was classified into NFI density classes and accuracy assessed using a confusion matrix
• FS ALS models applied to BT ALS and 25m cells centroids were used to sample k-NN raster
(Wulder et al. 2012. CJRS 38:600-618)
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Bias and MAE (NFI-based) Results
Stand height (m)Total Volume
(m3/ha)Stand volume
(m3/ha)Total biomass
(t/ha)
∆ |∆| ∆ |∆| ∆ |∆| ∆ |∆|
N 40 40 40 40 40 40 46 46
Mean -2.18 4.14 7.75 69.77 4.37 44.09 11.25 35.36
Median -2.57 3.27 24.27 54.70 10.61 30.95 15.27 20.99
StDev 4.90 3.37 99.37 70.31 65.53 48.16 49.39 35.92
95%CI [-4, -0.7] [3, 5][-23, 39]
[48, 92]
[-16, 25]
[29, 59]
[-3, 26][25, 46]
95% CI for the estimate of CC Overall Accuracy:[43, 74]
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Bias and MAE (BT-based) Results
All
BT K-NN ∆ |∆|
N108949
0108949
0108949
0108949
0
Min 2.51 4.00 -27.75 0.00
Max 34.96 32.00 24.80 27.75
Mean 11.54 11.50 -0.04 3.10
Median 9.46 10.00 0.01 2.13
Stand height
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Stand height
BT ALS vs k-NN: Conifer
Crown closure
Land cover (CDMW)
< -5-5 - -3-3 - 00 - 33 - 5> 5
∆ Stand height (m)
< -10-10 - -5-5 - 00 - 55 - 10> 10
∆ Crown closure (%)
0 1 2 km
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Stand height
Land cover(CDMW)
< -5-5 - -3-3 - 00 - 33 - 5> 5
∆ Stand height (m)
< -10-10 - -5-5 - 00 - 55 - 10> 10
∆ Crown closure (%)
0 1 2 km
Crown closure
BT ALS vs k-NN: Broadleaf
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Conclusions
• Using a multi-scale, multi-source framework involving a limited number of field plots and a much larger number of GLAS surrogate plots, it was possible to estimate key forest attributes in each 30 m forested cell in an area the size of California.
• Despite the inherent uncertainties in scaling up from field and airborne to satellite LiDAR, the results attained demonstrate the viability of an inventory mapping approach over a northern boreal region where field plots are scant.
Mahoney et al. (2018) A Forest Attribute Mapping Framework: A Pilot Study in a Northern Boreal Forest, Northwest Territories, Canada. Remote Sensing 10(9), 1338.
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Multi-Source National Biomass Map of Boreal Forests
• based on lessons learned in NWT:
– Improve national biomass maps:
• 30 m resolution
• For boreal forests (non-inventoried taiga ecozones)
• From optical-radar synergy
• From two candidate training sets (NFI, GLAS)
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
RS Imagery and Reference Sets
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
1- Developed approach for yearly mosaic (LFC)
• Yearly mosaic and
cloud shadow detection 1984-2017
2- Developed approach for Multiyear Mosaic for
imputation (LFC) (Seamless, Original scenes)
Landsat mosaic development
1984 – 2017
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
2009 2010
20082007
PALSAR Multi-Year Compositing
Beaudoin et al., in prep.
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Method Workflow
RF modelling:optimization/
validation
Sampling set GLAS, NFI, QC
plots
RS features
Ancillaryfeatures
(topo, climate…)
Independent validation set:
QC plots
Attributemaps
Mapping
Validation
Canada 2010 PALSAR mosaic
Canada 2010 Landsat mosaic
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Results
GLAS-based, Landsat + PALSAR NFI-based, Landsat + PALSAR
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Map Comparison/Validation in QuebecGLAS-based NFI-based Plot-based
QC inventory plots: courtesy MFFPQ
Beaudoin et al., in prep.
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017
Conclusion and Perspectives
• Improved national baseline forest information is needed in Canada(science, reporting, policy and management)
• Multi-source EO data offers the only viable means for such purposes (dynamic, transparent, systematic, repeatable, and spatially exhaustive)
• New and awaited CFS baseline map products have proven their utility for a variety of strategic uses
• NFI is key and enables value-added map products (amplification effect)
• Further improvements will provide attribute maps with higher spatial/temporal/estimation accuracy, moving towards better monitoring of Canada’s forests