Using Airborne Lidar to Differentiate Cottonwood Trees in a Riparian Area and Refine Riparian Water Use Estimates
AdvisorGoodrich, David C.
Committee ChairGoodrich, David C.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractAirborne lidar (light detecting and ranging) is a useful tool for probing the structure of forest canopies. Such information is not readily available from other remote sensing methods and is essential for modern forest inventories. 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. The lidar data were acquired in June 2003 and 2004, using Optech's 1233 ALTM (Optech Incorporated, Toronto, Canada). Canopy height, crown diameter, stem diameter at breast height (dbh), canopy cover, and mean intensity of return laser pulses from the canopy surface are estimated for the cottonwood trees from lidar data. The lidar estimates show a good degree of correlation with ground-based measurements. This study also demonstrates that other parameters of young, mature, and old cottonwood trees such as height and canopy cover, when derived from lidar, are significantly different (p < 0.05). These lidar-derived canopy metrics provided the basis for a supervised image classification of cottonwood age categories, using a maximum likelihood algorithm. The results of classification illustrate the potential of airborne lidar data to differentiate age classes of cottonwood trees for riparian areas quickly and quantitatively.In addition, 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 (we summed up a series of Gaussian pulses that vertically stacked at the elevations produced by the small-footprint elevation data to create a modeled large-footprint return waveform and compared the synthetic waveforms with ground-based Intelligent Laser Ranging and Imaging System (ILRIS) scanner images in cottonwood trees). These four metrics were incorporated into a stepwise regression procedure to predict field-derived LAI for different age classes of cottonwoods.Additionally, this study applied the Penman-Monteith model to estimate transpiration of the cottonwood clusters using lidar-derived canopy metrics, such as height and LAI, and compared it with transpiration measured by sap flow, so that improved riparian water use estimates could be made.