Fallen Tree Detection and Mapping from Airborne Laser Scanning Point Clouds Using Physics-Guided Supervoxel Learning

Accurate fallen tree detection from airborne laser scanning (ALS) point clouds is important for forest structural assessment and disturbance monitoring. However, fallen trees remain difficult to extract from operational ALS data because near-ground stems are often fragmented by understory occlusion, uneven sampling, and geometric confusion with shrubs, terrain surfaces, and other low vegetation. This study proposes a physics-guided supervoxel framework for fallen tree detection from ALS point clouds. The framework represents ALS observations as spatially coherent supervoxels and describes each supervoxel using local geometric features, cylindrical morphology, and graph-based neighborhood context. Cylindrical descriptors encode the elongated stem structure of fallen trees, while graph-topological features represent spatial continuity among adjacent supervoxels. A stacking ensemble classifier is trained with SHAP-based feature selection, followed by graph-constrained spatial refinement to improve local label consistency. The framework was trained using 30 plots and evaluated on seven held-out forest plots from the Dutch Actueel Hoogtebestand Nederland 4 (AHN4) dataset, with point densities of 10–15 pts/m2. Using point-level reference labels, the stacking classifier achieved a mean fallen tree F1-score of 83.86% across the test plots, which increased to 85.39% after spatial refinement. Ablation results show that cylindrical descriptors provided the largest gain, improving the fallen tree F1 score by 11.77%. These results indicate that physics-guided supervoxel learning can produce interpretable fallen tree maps from operational ALS data and support subsequent analyses of near-ground forest structure and disturbance effects.