Using airborne lidar to predict Leaf Area Index in cottonwood trees and refine riparian water-use estimates

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 study, 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. This research applied the Penman–Monteith model to estimate transpiration of the cottonwoods using 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.