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Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS
Authors:David A. Fuentes  John A. Gamon  Helen C. Claudio  Zhiyan Mao  Abdullah F. Rahman  Hongyan Luo
Affiliation:a Department of Biology and Microbiology, California State University LA, Los Angeles, CA 90032, USA
b Department of Geography and Urban Analysis, California State University LA, Los Angeles, CA 90032, USA
c Texas Tech University, Department of Range, Wildlife and Fisheries Management, Box 42125, Lubbock, TX 79409, USA
d Department of Biology, San Diego State University, San Diego, CA 92182, USA
Abstract:Using simple models derived from spectral reflectance, we mapped the patterns of ecosystem CO2 and water fluxes in a semi-arid site in southern California during a period of extreme disturbance, marked by drought and fire. Employing a combination of low (∼ 2 km) and high (∼ 16 km) altitude images from the hyperspectral Airborne Visible Infrared Imaging Spectrometer (AVIRIS), acquired between April 2002 and September 2003, and ground data collected from an automated tram system, several vegetation indices were calculated for Sky Oaks field station, a FLUXNET and SpecNet site located in northern San Diego County (CA, USA). Based on the relationships observed between the fluxes measured by the eddy covariance tower and the vegetation indices, net CO2 and water vapor flux maps were derived for the region around the flux tower. Despite differences in the scale of the images (from ∼ 2 m to 16 m pixel size) as well as marked differences in environmental conditions (drought in 2002, recovery in early 2003, and fire in mid 2003), net CO2 and water flux modeled from AVIRIS-derived reflectance indices (NDVI, PRI and WBI) effectively tracked changes in tower fluxes across both drought and fire, and readily revealed spatial variation in fluxes within this landscape. After an initial period of net carbon uptake, drought and fire caused the ecosystem to lose carbon to the atmosphere during most of the study period. Our study shows the power of integrating optical and flux data in LUE models to better understand factors driving surface-atmosphere carbon and water vapor flux cycles, one of the main goals of SpecNet.
Keywords:Hyperspectral remote sensing   AVIRIS   Carbon flux   Water vapor flux   Disturbance   Light-use efficiency   Vegetation indices   Modeling
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