Nearly all regions of the world are expected to experience a net negative impact of climate change on water resources and freshwater ecosystems. The intensity and characteristics of the impact, however, can vary significantly from region to region. Some regions are likely to experience water shortages. Coupled with increasing demand, this is likely to result in large increases in the number of people at risk of water scarcity. For a long-term management planning of a region’s water resources, e.g., arid and semi-arid regions, in the face of the evolving climate change impacts, it is important that the properties of vegetation and canopy be quantified. Vegetation patterns and associated canopy structure influence landscape functions such as water use, biomass production, and energy cycles. In this study, small-footprint lidar (light detecting and ranging) data were used to estimate biophysical properties of young, mature, and old cottonwood trees in the Upper San Pedro River Basin, Arizona, USA. 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 Leaf Area Index (LAI) for different age classes of cottonwoods. This research applied the Penman-Monteith model to estimate transpiration of the cottonwood clusters 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 refine water use estimates.

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