airborne imaging spectroscopy and lidar data: new …...aviris-ng coverage of full delta in 2014 and...
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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?