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United States Department of Agriculture

Agricultural Research Service

Title: Using Airborne Lidar to Predict Leaf Area Index in Cottonwood Trees and Refine Riparian Water Use Estimates 1877

item Farid, A
item Goodrich, David - Dave
item Bryant, R.
item Sorooshian, S.

Submitted to: Journal of Arid Environments
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/24/2007
Publication Date: N/A
Citation: N/A

Interpretive Summary: Arid and semi-arid regions account for approximately one-third of the land mass of earth. These regions are experiencing continued pressure from population growth in many parts of the world. Water is a critical resource in these regions and is often in short supply. To maintain the economic, social, and ecological viability of these areas it is essential that decision makers and resource managers have a solid scientific basis on which to make watershed based decisions including management of riparian vegetation. Riparian trees use water in proportion to their size, and are especially large users of water in flood plains along rivers in semi-arid environments. It's difficult to measure tree size and their density of leaves using traditional ground-based techniques over large areas to estimate reach level channel water water use. For this reason new techniques that are more accurate and efficient need to be developed. This study demonstrated that a lidar (light detecting and ranging) system mounted in an airplane can accurately measure features of forest canopy and leaf areas that are related to water use. Such information is not readily available from other remote sensing methods. The leaf area derived from lidar, and weather information was used in this study to estimate riparian cottonwood water use. The lidar derived estimates compared very well with those derived from ground-based methods. This information can be used in many forestry, ecological and hydrologic applications that will improve management of hydrologic resources and ecological models.

Technical Abstract: Quantification of riparian forest structure is important for developing a better understanding of how riparian forest ecosystems function. Additionally, estimation of riparian forest structural attributes, such as 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. The lidar data were acquired in June 2003. Canopy height, maximum laser height, and mean laser height were derived for the cottonwood trees from the data. Linear regression models were used to develop equations relating lidar height metrics with corresponding field-measured LAI for each age class of cottonwoods. The lidar height metrics show a good degree of correlation with field-measured LAI. 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. These four metrics were incorporated into a regression procedure to predict field-derived LAI for different age classes of cottonwoods. Metrics from lidar synthetic waveform are able to significantly estimate LAI. Furthermore, this research applied the well-known Penman-Monteith model to estimate transpiration of the cottonwood clusters using lidar-derived canopy metrics, such as height and LAI, so improved riparian water use estimates could be made.

Last Modified: 06/22/2017
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