Early detection of pine wilt disease infection in conifer-broadleaf mixed forests using UAV-based multi-sensor data...

Pine wilt disease (PWD), caused by the pine wood nematode, threatens forest ecosystems in China. Early detection of infected trees is essential for timely disease management but remains challenging in conifer-broadleaf mixed forests because of crown overlap and complex canopy structures. This study aimed to comprehensively evaluate the potential of unmanned aerial vehicle (UAV)-based hyperspectral, LiDAR (light detection and ranging), and thermal data for multi-stage PWD detection at individual tree scale.

An object-based classification and point cloud segmentation (OBPCS) method was developed to delineate individual pine trees by integrating LiDAR point clouds and UAV multi-spectral imagery. Compared with the canopy height model (CHM)-based watershed approach, OBPCS significantly improved segmentation accuracy (F-score = 0.88 versus 0.72). Random forest models were then constructed using crown biochemical, structural, and temperature features to classify five PWD infection stages. Among single-sensor datasets, hyperspectral data achieved the highest accuracy (overall accuracy (OA) = 78%, κ = 0.72), outperforming LiDAR (OA = 46%, κ = 0.32) and thermal data (OA = 28%, κ = 0.10). Multi-sensor fusion further increased classification accuracy to 82% (κ = 0.77) and improved early-stage detection accuracy by 7%. Variable importance analysis revealed that pigment-related indices were the most influential features, followed by LiDAR return intensity and point distribution metrics.

UAV-based multi-sensor fusion provides an effective approach for early detection of PWD in structurally complex mixed forests. The proposed framework improves individual-tree delineation and early-stage diagnosis, offering practical support for early warning and fine-scale monitoring of disease progression in forest ecosystems. © 2026 Society of Chemical Industry.