UAV LiDAR expands the understanding of forest tree diversity

Abstract

Timely and accurate monitoring of forest tree diversity is essential to support ecological evaluation and sustainable forest management. Recent studies have emphasized the importance of light detection and ranging (LiDAR) features in explaining tree diversity. However, how LiDAR-drived features, particularly height heterogeneity, relate to plot-level tree species diversity across spatial scales has not been systematically evaluated. To address this, we conducted a field survey in natural secondary forests and collected unmanned aerial vehicle (UAV) LiDAR data. Traditional diversity indices were calculated using relative density and importance value, and their correlations with LiDAR features were analyzed. These analyses incorporated assessments of different spatial resolutions and heterogeneity metrics for estimating tree diversity. The results revealed that (1) the diversity indices calculated with importance value correlate better with LiDAR features than with relative density; (2) among heterogeneity metrics, Rao’s Q outperformed the coefficient of variation (CV), and heterogeneity metrics overall performed better than structural features in estimating tree diversity; (3) the LiDAR data provided fine-scale structural information, with the highest accuracy for estimating tree diversity achieved at a 1 m spatial resolution; and (4) features from the cover category, including canopy cover (CC), cover of the herbaceous layer (CH), and cover of the tree layer (CT), demonstrated greater robustness and stronger correlations with tree diversity across different spatial resolutions. Our study suggests that using high-resolution UAV LiDAR data, combined with diversity indices based on importance values, can enhance biodiversity assessment and inform forest management strategies aimed at promoting structural complexity and supporting tree diversity.