DIKUL - logo
E-viri
Celotno besedilo
Recenzirano
  • UAV lidar and hyperspectral...
    Sankey, Temuulen; Donager, Jonathon; McVay, Jason; Sankey, Joel B.

    Remote sensing of environment, 06/2017, Letnik: 195
    Journal Article

    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=0.90; RMSE=2.3m) and crown diameter (R2=0.72; RMSE=0.71m) as well as total tree canopy cover (R2=0.87; RMSE=9.5%) and tree density (R2=0.77; RMSE=0.69 trees/cell) in 10m 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) (R2=0.92; RMSE=0.75m). 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. •Forest thinning and prescribed burning result in subtle changes.•Such changes require high resolution data to estimate.•UAV lidar and hyperspectral image fusion performs well in such estimates.•Structure-from motion data performs less well, but adequately.