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

Transcription

1 Airborne Imaging Spectroscopy and Lidar Data: New Tools for Environmental Monitoring Susan L. Ustin Dept. Land, Air and Water Resources University of California Davis Results Contributed by: Karine Adeline, Nina Amenta, Joaquim Bellvert, Ángeles Casas, Mariano García Stewart He, Margarita Huesca, Shruti Khanna, Alexander Koltunov Keely Roth Kristen Shapiro 34.19m Leaf Mass Area Pigments, Water, total C and N Species Composition, Biomass Canopy Gap Size Distribution Photonics Conference, UCLA March 25, 2016

2 States Processes Spectroscopy can be directly linked to environmental processes and states, providing a pathway to scale to regional and global observations

3 What is an Imaging Spectrometer?

4 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 Huisui PRISMA 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.

5 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 km 2 with 1,100 km of waterways ~65flightlines to cover; Mapped at m/pixel 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 CSTARS, UC Davis

6 Examples of Different Conditions Within Delta Khanna, Bellvert and Shapiro, CSTARS UC Davis

7 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

8 Dynamic Changes in Vegetation in Flooded Rhode Island Khanna, Bellvert and Shapiro, CSTARS UC Davis

9 Changes in Plant Distribution in the Recovery of Wetlands in the Flooded Liberty Island CSTARS UC Davis Khanna, Bellvert and Shapiro

10 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 NEON & CZO boxes Flux towers UC field station USFS LiDAR US National Forests CSTARS UC Davis

11 Spectraof leaves from 18 dominant species from 9 sites, 3 seasons and 2 years produced 12 significant clusters Reflectance Mean spectrum of each cluster Wavelength, nm Euclidean Distance spectral signatures of clusters, solid lines are the mean spectrum of each cluster, +/- 1 SD Keely Roth et al. revised, 2016

12 Distribution of Plant Functional Types and Species in Spectral Clusters By Plant Functional Types Broadleaf annual crop Perennial herb Wetland emergent perennial Deciduous broadleaf shrub Evergreen broadleaf shrub Deciduous broadleaf tree Evergreen broadleaf tree Evergreen needleleaf tree By Species Abies concolor Abies magnifica Arctostaphylos viscida Calocedrus decurrens Ceanothus cordulatus Lepidium latifolium Pinus jeffreyi Pinus lambertiana Pinus ponderosa Pinus sabiana Quercus chrysolepis Quercus douglasii Quercus kelloggii Quercus wislizeni Schoenoplexus acutus Typha ssp. Vitis vinifera Zea mays Keely Roth et al. Revised 2016

13 Relationship between Measured Leaf Chemistry and Spectral Classes Measured leaf properties Total Chlorophyll Total Carotenoids Leaf water content Leaf dry biomass Leaf Mass Area Leaf Thickness Leaf Scattering at 445nm Total Carbon Total Nitrogen Keely Roth et al. in preparation.

14 Leaf and Canopy Reflectance Properties Can Predict Plant Traits like Leaf Mass Area Spring Variation in Leaf Mass Area (LMA) between and within Plant Communities and Seasons Fall at Stanford s Jasper Ridge Biological Preserve ProSail RT Model Inversion Ángeles Casas, CSTARS

15 Inversion of canopy leaf biochemistry by using radiative transfer models Chlorophyll content, Carotenoïd content, Equivalent water thickness, Leaf per mass area Leaf-level PROSPECT Leaf reflectance and transmittance F? or Detailed structure or stylized Illumination geometry Canopy-level DART or? Canopy reflectance Inversion LUT-approach method Remote sensing data + Field collection data AVIRIS Background spectra (leaf/wood optical properties) (LAI, LAD, clumping, crown shape, woody material) and (sun/sensor angles) Karine Adeline et al., CSTARS

16 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) Karine Adeline et al. Preliminary Data Fall Summer Spring 1065m x 1065m; AVIRIS image 18m pixels, 3 seasons: 55,000 simulations

17 Retrieving Forest Structure Parameters from AVIRIS and LiDAR Airborne LiDAR from NEON Airborne Observation Platform (AOP) Imaging spectroscopy data from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Sierra National Forest (Central California) SJER Earth version SOAP TEAK Margarita Huesca et al. CSTARS 2016 San Joaquin (SJER) Soaproot Saddle (SOAP) Teakettle (TEAK)

18 Relating AVIRIS Metrics to LiDAR Structural Variables to Predict Structure from AVIRIS Optical metrics (AVIRIS) Predicted structural variables Random Forest Reference structural variables LiDAR Defining Structural Types Steps 1.Identify unique classes (a,b) 2.Merge non-unique classes (a,c) Criteria a) Spatial continuity b) Minimum class size c) Value proximity Biomass Height HomogeneityClumping Preliminary optical structural types Biomass Height Homogeneity Preliminary LiDAR structural types Clumping Final canopy structural types from optical metrics VALIDATION Final canopy structural types from LiDAR Margarita Huesca et al. CSTARS 2016

19 BIOMASS Reference TEAKETTLE Predicted HOMOGENEITY Predicted Reference HEIGHT Reference Predicted CLUMPINGPredicted Reference High Low No Data Margarita Huesca et al in press

20 Canopy Structural Types Derived from AVIRIS Data at 3 Sites Using 1 Model ST B HT H C 1 L L-M H L 2 L L-M H H 3 M M-H M L-H 4 H M-H L H Reference Predicted Reference ST B HT H C 1 L L-M H L-H 2 L-M H L L 3 L-M H L H 4 M L-M L-H L-H 5 H L H H 6 H M-H H H 7 H M-H L L-H Predicted San Joaquin Experiment Range Soaproot Saddle Canopy Structural Type; L: low; M: medium; H: high. Class color tables correspond to the maps. Low elevation savanna ecosystem canopy structural types were mainly driven by differences in vegetation cover. Mid-elevation mixed conifer forest ST were driven by both biomass and height. Margarita Huesca et al in prep.

21 Characterization of Structural Types from AVIRIS Data at 3 Sites Using 1 Model ST B HT H C 1 L-M L-M H L 2 L-M L-M H H 3 M-H M H L Reference Predicted 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 Teakettle Mid- and high elevation ecosystems had more complex structural patterns High elevation conifer forest structural types were driven mainly by vegetation complexity. Margarita Huesca et al in prep.

22 Identifying and Mapping Habitat for Endangered Black- Backed Woodpecker after the 2013 RIM Mega-Fire Lidar and Imaging Spectroscopy Measurements Ángeles Casas et al Remote Sensing of Environment, 175: 2016, 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

23 Number of trees per pixel (50m x 50m) in the RIM Fire Perimeter Casas et al. 2016

24 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. Casas et al. 2016

25 Conclusions. Range of applications for RS data is rapidly increasing Remote 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 accuracy Most data products still depend on empirical relationships

26 Questions?