Susan L. UstinDept. Land, Air and Water ResourcesUniversity of California Davis

Leaf Mass AreaPigments, Water, total C and NSpecies Composition, BiomassCanopy Gap Size Distribution

34.19m

Airborne Imaging Spectroscopy and Lidar Data: New Tools for Environmental Monitoring

Results Contributed by:

Karine Adeline,Nina Amenta,

Joaquim Bellvert,Ángeles Casas,Mariano García

Stewart He,Margarita Huesca,

Shruti Khanna, Alexander Koltunov

Keely RothKristen Shapiro

Photonics Conference, UCLA March 25, 2016

Spectroscopy can be directly linked to environmental processes and states, providing a

pathway to scale to regional and global observationsStat

es

Processes

What is an Imaging Spectrometer?

What is an Imaging Spectrometer?* Large # of bands

* Contiguous spectrum

* Airborne Examples:

AVIRIS-C, AVIRIS-ng

CAO, NEON-AOP

PRISM

CASI HYMAP

SpecTIR AISA

Probe 1 HYDICE

*Satellite Examples

Hyperion

CRIS/PROBA

In fabrication:

EnMap PRISMA

Huisui

AVIRIS has 224 bands and AVIRIS-ng has 385 bands across the reflected solar spectrum. In contrast the multispectral Landsat 5 and 7 had 6 bands and the new Landsat 8 has 9 bands over this region. The imaging spectroscopy data has higher information content about environmental conditions.

AVIRIS-ng Coverage of Full Delta In 2014 and 2015 (~2.2TB raw data)

HyMap Coverage of Full Delta in 2004, 2005, 2006, 2007 and 2008; ~ 2.5 TB raw data

What is the cover fraction of floating, submerged and emergent weeds in the Sacramento-San Joaquin Delta?

AVIRIS-ng flights in

2014

Total area = 2,800 km2 with 1,100 km of waterways

~65flightlines to cover; Mapped at 2.5-3.5 m/pixel

CSTARS, UC Davis

Examples of Different Conditions Within Delta

Khanna, Bellvert and Shapiro, CSTARS UC Davis

Mean Spectra of Some Common Invasive Weeds in the Delta

How has the Total Area Covered by Submerged and Floating Invasives Species Changed Between Years?

Khanna, Bellvert and Shapiro, CSTARS UC Davis

Dynamic Changes in Vegetation in Flooded Rhode IslandK

hann

a, B

ellv

erta

nd S

hapi

ro, C

STA

RS U

C D

avis

Changes in Plant Distribution in the Recovery of Wetlands in the Flooded Liberty Island

CSTARS UC DavisKhanna, Bellvert and Shapiro

AVIRIS Data Summary for HyspIRI Project

3 Northern HYSPIRI Boxes flown 3 seasons for 3 years:

11 lines in Tahoe Box 10 lines in Yosemite/NEON

Box 12 lines in Bay Box Approximately 775 GB

AVIRIS 18m imagery for these boxes

HyspIRI flight boxes

Flux towers

UC field station

US National Forests

NEON & CZO boxes

USFS LiDAR

CSTARS UC Davis

Spectraof leaves from 18 dominant species from 9 sites, 3 seasons and 2 years produced 12 significant clusters

Euclidean Distance

500 1000 1500 2000 2500Wavelength, nm

123456789101112

Keely Roth et al. revised, 2016

Mean spectrum of each cluster0.6

0.4

0.2

0.0

Refle

ctan

ce

spectral signatures of clusters, solid lines are the mean spectrum of each cluster, +/- 1 SD

Abies concolorAbies magnificaArctostaphylos viscidaCalocedrus decurrensCeanothus cordulatusLepidium latifoliumPinus jeffreyiPinus lambertianaPinus ponderosa

Pinus sabianaQuercus chrysolepisQuercus douglasiiQuercus kelloggiiQuercus wislizeniSchoenoplexus acutusTypha ssp.Vitis viniferaZea mays

Broadleaf annual cropPerennial herbWetland emergent perennialDeciduous broadleaf shrubEvergreen broadleaf shrubDeciduous broadleaf treeEvergreen broadleaf treeEvergreen needleleaf tree

Distribution of “Plant Functional Types” and Species in Spectral Clusters

Keely Roth et al. Revised 2016

By Plant Functional Types

By Species

Keely Roth et al. in preparation.

Relationship between Measured Leaf Chemistry and Spectral Classes

Measured leaf properties

Total ChlorophyllTotal CarotenoidsLeaf water contentLeaf dry biomassLeaf Mass AreaLeaf ThicknessLeaf Scattering at 445nmTotal CarbonTotal Nitrogen

Leaf and Canopy Reflectance Properties Can Predict Plant Traits like Leaf Mass Area

Variation in Leaf Mass Area (LMA) between and within Plant Communities and Seasons

at Stanford’s Jasper Ridge Biological Preserve

Spring

Fall

ProSail RT Model Inversion Ángeles Casas, CSTARS

F

Inversion of canopy leaf biochemistry by using radiative transfer models

PROSPECTLeaf-level

DARTCanopy-level

Chlorophyll content, Carotenoïd content,Equivalent water thickness, Leaf per mass area

Leaf reflectanceand transmittance

Canopy reflectance

AVIRIS

(leaf/wood optical properties)(LAI, LAD, clumping, crown shape,woody material)

and (sun/sensor angles)Remote sensing

data+

Field collection data

Inversion LUT-approachmethod

or

?or

?

Illumination geometryDetailed structure

or stylized

Background spectra

Karine Adeline et al. , CSTARS

Prediction of canopy leaf chemistry & structure from Inversion of linked DART-PROSPECT radiative transfer models at the TONZI Ameriflux Site (Tahoe Box; Foothill Woodland Savanna)

Spri

ngSu

mm

erFa

ll

1065m x 1065m; AVIRIS image 18m pixels, 3 seasons: 55,000 simulationsKar

ine

Ade

line

et a

l. Pr

elim

inar

y D

ata

Airborne LiDAR from NEON AirborneObservation Platform (AOP)Imaging spectroscopy data from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

Sierra National Forest (Central California)

Teakettle(TEAK)

SoaprootSaddle(SOAP)

San Joaquin(SJER)

Earth version 7.1.5.1557

SJER

SOAP TEAK

Margarita Huesca et al. CSTARS 2016

Retrieving Forest Structure Parameters from AVIRIS and

LiDAR

Biomass

Defining Structural TypesSteps

1.Identify unique classes (a,b)2.Merge non-unique classes (a,c)

Criteriaa) Spatial continuityb) Minimum class sizec) Value proximity

Final canopy structural types from optical metrics

Optical metrics (AVIRIS)

LiDARRandom Forest

Predicted structural variables

Reference structural variables

Height ClumpingHomogeneity Biomass

Final canopy structural types from LiDAR

Height ClumpingHomogeneity

VALIDATION

Preliminary LiDAR structural types

Preliminary optical structural types

Relating AVIRIS Metrics to LiDAR Structural Variables to Predict Structure from AVIRIS

Margarita Huesca et al. CSTARS 2016

TEAKETTLEReference Predicted Reference Predicted

Reference Predicted Reference Predicted

BIOMASS HEIGHT

HOMOGENEITY CLUMPING

High

LowNo Data

Margarita Huesca et al. 2016 in press

Canopy Structural Type; L: low; M: medium; H: high. Class color tables correspond to the maps.

San Joaquin Experiment Range

Reference Predicted

Canopy Structural Types Derived from AVIRIS Data at 3 Sites Using 1 ModelST B HT H C

1 L L-M H L2 L L-M H H3 M M-H M L-H4 H M-H L H

ST B HT H C1 L L-M H L-H2 L-M H L L3 L-M H L H4 M L-M L-H L-H5 H L H H6 H M-H H H7 H M-H L L-H

Reference Predicted

Soaproot Saddle

Low elevation savanna ecosystem canopy structural types were mainly driven bydifferences in vegetation cover.

Mid-elevation mixed conifer forest ST were driven by both biomass and height.Margarita Huesca et al. 2016 in prep.

ST B HT H C1 L-M L-M H L

2 L-M L-M H H

3 M-H M H L

4 M-H M H H

5 M-H M L L

6 M-H M L H

7 M-H H L L

8 H L H H

Reference Predicted

Characterization of Structural Types from AVIRIS Data at 3 Sites Using 1 Model

Teakettle

Mid- and high elevation ecosystems had more complex structural patterns

High elevation conifer forest structural types were driven mainly byvegetation complexity.

Margarita Huesca et al. 2016 in prep.

Lidar and Imaging Spectroscopy Measurements

Ángeles Casas et al. 2016. Remote Sensing of Environment, 175: 2016, 231–241

• Multi-return lidar, > 10 points/m2

• >2TB raw data

• AVIRIS • 4 flighlines• 20 m pixels• 224 spectral bands• Before (June 2013)

and After (November 2013)

• >0.5 TB raw data

Identifying and Mapping Habitat for Endangered Black-Backed Woodpecker after the 2013 RIM Mega-Fire

Number of trees per pixel (50m x 50m) in the RIM Fire Perimeter

Cas

as e

t al.

2016

Habitat map for the Black-backed Woodpecker across the Rim Fire

Based on pre-harvest conditions with thresholds for conifer snag basal areas (csBA) obtained from Tingley et al. (2014). Pixel resolution = 20 m. Cas

as e

t al.

2016

Conclusions

. Range of applications for RS data is rapidly increasingRemote sensing data sets are getting larger: More spectral bands More parts of the EM spectrum (UV to microwave) Better spatial resolution (submeter) Direct downlink for real time satellite data Larger regions to be measured (up to global scales) More frequent data collections (subdaily to seasonal) Synthesize multi-sensor data, multi-date data

Users want data in near real time with high accuracyMost data products still depend on empirical relationships

Questions?