HR: 0800h
AN: G51C-0614    [Abstracts]
TI: Using Lidar to distinguish leaf area index in cottonwood trees and improve riparian water use estimates in the Upper San Pedro River Basin
AU: * Farid, A
EM: farid@hwr.arizona.edu
AF: University of Arizona, Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ 85721, USA, Tucson, 85721,
AU: Goodrich, D
EM: Dave.Goodrich@ARS.USDA.GOV
AF: USDA-ARS-SWRC, USDA-ARS-SWRC, Southwest Watershed Research Center, Tucson, AZ, USA, Tucson, 85719,
AU: Durcik, M
EM: mdurcik@hwr.arizona.edu
AF: University of Arizona, Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ 85721, USA, Tucson, 85721,
AU: Sorooshian, S
EM: soroosh@uci.edu
AF: University of California, Irvine, Department of Civil and Environmental Engineering, University of California, Irvine, CA, USA, Irvine, 92697,
AB: Estimation of riparian forest structural attributes, such as the Leaf Area Index (LAI), is an important step in identifying the amount of water use in riparian forest areas. In this research, small footprint lidar data were used to estimate biophysical properties of young, mature, and old cottonwood trees in the Upper San Pedro River Basin, Arizona, USA. Canopy height and maximum and mean laser heights were derived for the cottonwood trees from lidar data. Linear regression models were used to develop equations relating lidar height metrics with corresponding field measured LAI for each age class of cottonwoods. Four metrics (tree height, height of median energy, ground return ratio, and canopy return ratio) were derived by synthetically constructing a large footprint lidar waveform from small-footprint lidar data which were compared to ground-based high- resolution Intelligent Laser Ranging and Imaging System (ILRIS) scanner images. These four metrics were incorporated into a stepwise regression procedure to predict field-derived LAI for different age classes of cottonwoods. The Penman-Monteith model was then used to estimate transpiration of the cottonwoods using the lidar-derived canopy metrics. These transpiration estimates compared very well to ground-based sap flux transpiration estimates indicating lidar-derived LAI can be used to improve riparian cottonwood water-use estimates. Future research will attempt to fuse high spatial resolution multispectral or hyperspectral data and lidar data to improve classification results for species identification in the Upper San Pedro River Basin.
DE: 1632 Land cover change
DE: 1640 Remote sensing (1855)
DE: 1719 Hydrology
SC: Geodesy [G]
MN: 2007 Fall Meeting