Jonathon Donager
Jason L. McVay
Joel B. Sankey
Temuulen T. Sankey
2017
<p><span>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 (</span><i>R</i><sup><i>2</i></sup><span> </span><span>=</span><span> </span><span>0.90; RMSE</span><span> </span><span>=</span><span> </span><span>2.3</span><span> </span><span>m) and crown diameter (</span><i>R</i><sup><i>2</i></sup><span> </span><span>=</span><span> </span><span>0.72; RMSE</span><span> </span><span>=</span><span> </span><span>0.71</span><span> </span><span>m) as well as total tree canopy cover (</span><i>R</i><sup><i>2</i></sup><span> </span><span>=</span><span> </span><span>0.87; RMSE</span><span> </span><span>=</span><span> </span><span>9.5%) and tree density (</span><i>R</i><sup><i>2</i></sup><span> </span><span>=</span><span> </span><span>0.77; RMSE</span><span> </span><span>=</span><span> </span><span>0.69 trees/cell) in 10</span><span> </span><span>m cells across thin only, burn only, thin-and-burn, and control treatments, where tree cover and density ranged between 22 and 50% and 1–3.5 trees/cell, respectively. The lidar data also produces highly accurate digital elevation model (DEM) (</span><i>R</i><sup><i>2</i></sup><span> </span><span>=</span><span> </span><span>0.92; RMSE</span><span> </span><span>=</span><span> </span><span>0.75</span><span> </span><span>m). 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.</span></p>
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10.1016/j.rse.2017.04.007
en
Elsevier
UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA
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