Item talk:Q239824
From geokb
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"abstract": "Forest vegetation classification and structure measurements are fundamental steps for planning, monitoring, and evaluating large-scale forest changes including restoration treatments. High spatial and spectral resolution remote sensing data are critically needed to classify vegetation and measure their 3-dimensional (3D) canopy structure at the level of individual species. Here we test high-resolution lidar, hyperspectral, and multispectral data collected from unmanned aerial vehicles (UAV) and demonstrate a lidar-hyperspectral image fusion method in treated and control forests with varying tree density and canopy cover as well as in an ecotone environment to represent a gradient of vegetation and topography in northern Arizona, U.S.A. The fusion performs better (88% overall accuracy) than either data type alone, particularly for species with similar spectral signatures, but different canopy sizes. The lidar data provides estimates of individual tree height (R2\u00a0=\u00a00.90; RMSE\u00a0=\u00a02.3\u00a0m) and crown diameter (R2\u00a0=\u00a00.72; RMSE\u00a0=\u00a00.71\u00a0m) as well as total tree canopy cover (R2\u00a0=\u00a00.87; RMSE\u00a0=\u00a09.5%) and tree density (R2\u00a0=\u00a00.77; RMSE\u00a0=\u00a00.69 trees/cell) in 10\u00a0m cells across thin only, burn only, thin-and-burn, and control treatments, where tree cover and density ranged between 22 and 50% and 1\u20133.5 trees/cell, respectively. The lidar data also produces highly accurate digital elevation model (DEM) (R2\u00a0=\u00a00.92; RMSE\u00a0=\u00a00.75\u00a0m). In comparison, 3D data derived from the multispectral data via structure-from-motion produced lower correlations with field-measured variables, especially in dense and structurally complex forests. The lidar, hyperspectral, and multispectral sensors, and the methods demonstrated here can be widely applied across a gradient of vegetation and topography for monitoring landscapes undergoing large-scale changes such as the forests in the southwestern U.S.A.",
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