B42A-04
Detection of Extreme Climate Event Impacts to Terrestrial Productivity From Airborne Hyperspectral Imagery
Thursday, 17 December 2015: 11:05
2006 (Moscone West)
Ankur R Desai1, Sean DuBois1, Aditya Singh1, Shawn Serbin2, Michael Goulden3, Dennis D Baldocchi4, Walter C Oechel5, Eric L Kruger1 and Philip A Townsend6, (1)University of Wisconsin Madison, Madison, WI, United States, (2)Brookhaven National Laboratory, Upton, NY, United States, (3)University of California Irvine, Irvine, CA, United States, (4)University of California Berkeley, Dept of Environmental Science, Policy, & Management, Berkeley, CA, United States, (5)San Diego State University, San Diego, CA, United States, (6)University of Wisconsin, Madison, WI, United States
Abstract:
Changes in drought frequency and intensity are likely to be some of the largest climate anomalies to influence the net productivity of ecosystems, especially in already water-limited regions. However, the physiological mechanisms that drive this response are limited by primarily infrequent and small-scale leaf-level measurements. Here, we integrated eddy covariance flux tower estimates of gross primary productivity (GPP) across an elevation-gradient in California with airborne imagery from the NASA HyspIRI Preparatory campaign to evaluate the potential of hyperspectral imagery to detect responses of GPP to prolonged drought. We observed a number of spectral features in the visible, infrared, and shortwave infrared regions that yielded stronger linkages than traditional broadband indices with space and time variation in GPP across a range of ecosystems in California experiencing water stress in the past three years. Further, partial least squares regression (PLSR) modeling offers the ability to generate predictive models of GPP from narrowband hyperspectral remote sensing that directly links plant chemistry and spectral properties to productivity, and could serve as a significant advance over broadband remote sensing of GPP anomalies.
