UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA

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=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.

Plant phenotyping relevance

UAV LiDAR・ハイパースペクトル融合による個体樹木の高さ、樹冠径、被覆率、密度推定を開発・検証しており、植物形質取得が研究の中心である。

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