COS 21-4 - Imaging spectroscopic analysis of biochemical traits for shrub species in Great Basin, USA

Monday, August 7, 2017: 2:30 PM
B114, Oregon Convention Center
Yi Qi, Department of Land, Air and Water Resources, University of California, Davis, Davis, CA; Brown University, Providence, RI, Susan Ustin, Land, Air and Water Resources, UC Davis, Center for Spatial Technologies and Remote Sensing, Davis, CA and Nancy F. Glenn, Geosciences, Boise State University, Boise, ID

The biochemical traits of plant canopies are important predictors of photosynthetic capacity and nutrient cycling. Imaging spectroscopy has demonstrated the ability to characterize the plant biochemical properties of species in tropical, temperate, boreal and Mediterranean ecosystems. However, studies of shrub species in dryland ecosystem is limited partially due to the sparse vegetation cover and manifold shrub structure. This study developed spectroscopic analysis for the determination of foliar biochemistry (specific leaf mass, water content, dry matter content, nitrogen content and carbon content) in two widely-distributed shrub species, big sagebrush and bitterbrush in Great Basin. It demonstrated the potential of airborne imaging spectrometer (AVIRISng) to measure shrub biochemical traits at canopy and landscape scales.


This study demonstrated the ability of airborne imaging spectrometer AVIRISng to estimate five biochemical traits of shrub species in dryland ecosystem. Partial-least-squares-regression (PLSR) generated good projection modeling results at both canopy and landscape scales. The wavelength contributions were broadly similar for all traits, but also displayed significant distinctions in specific wavelengths of importance, especially within the SWIR region. In addition, the wavelengths of highest importance corresponded to spectral regions of known chemical absorption features, including those related to foliar lignin, cellulose, nitrogen, and water. An important future step is to combine similar data sets from other shrub species to refine and standardize both data and methods as a basis to operationally estimate foliar traits in dryland ecosystem. Such research will facilitate more rapid prediction of vegetation traits for remote sensing and ecological research.