کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5754803 | 1621205 | 2017 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
کامپیوتر در علوم زمین
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
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 (R2Â =Â 0.90; RMSEÂ =Â 2.3Â m) and crown diameter (R2Â =Â 0.72; RMSEÂ =Â 0.71Â m) as well as total tree canopy cover (R2Â =Â 0.87; RMSEÂ =Â 9.5%) and tree density (R2Â =Â 0.77; RMSEÂ =Â 0.69 trees/cell) in 10Â 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) (R2Â =Â 0.92; RMSEÂ =Â 0.75Â 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 195, 15 June 2017, Pages 30-43
Journal: Remote Sensing of Environment - Volume 195, 15 June 2017, Pages 30-43
نویسندگان
Temuulen Sankey, Jonathon Donager, Jason McVay, Joel B. Sankey,