Weakly supervised forest canopy extraction and multi-dimensional joint canopy entropy for quantifying canopy structural complexity using large-scale forest UAV LiDAR data.

Canopy Structural Complexity (CSC) is a key structural attribute for evaluating forest ecological functions and health, with accurate canopy extraction serving as the prerequisite for its quantification and the basis for improving metric reliability and elucidating spatiotemporal canopy dynamics. However, existing canopy extraction methods generally rely on large-scale fully labeled point cloud datasets, which are often impractical for large-scale Unmanned Aerial Vehicle (UAV) LiDAR forest surveys. To address this limitation, we propose a weakly supervised canopy extraction strategy that relies on a limited number of labeled plots. By incorporating a pseudo-label generation strategy and a data-augmentation-based consistency self-supervised constraint, the method effectively improves segmentation performance in unlabeled scenarios. Furthermore, we introduce a multi-dimensional joint canopy entropy index that integrates both global and local canopy features, combining projection-based global canopy entropy with graph-based local canopy entropy to provide a more comprehensive CSC quantification. Experiments were conducted using three labeled plots as the training set and a held-out labeled plot for quantitative evaluation, with numerous unlabeled plots used for qualitative generalization analysis. Results showed that on the labeled test plots, the proposed weakly supervised strategy achieved 85.37 % mIoU and 92.64 % OA, surpassing the best-performing fully supervised method. Visual comparisons in real, unlabeled forest scenes demonstrate that the proposed method maintains clear boundaries between canopy and non-canopy regions, even in areas with significant topographic variation, fragmented canopy, or complex structures, while effectively suppressing false positives in ground and low-vegetation areas. The proposed multi-dimensional joint canopy entropy, when applied to the predictions of the weakly supervised canopy extraction strategy, successfully quantified the CSC using large-scale, unlabeled UAV forest LiDAR point clouds. It yielded results consistent with ecological priors and exhibited robustness under varying levels of point cloud sparsification. Based on normalized scores across three evaluation metrics, the proposed index achieved a score of 0.76, outperforming several commonly used CSC quantification indices.