TLSLeaf: Unsupervised Instance Segmentation of Broadleaf Leaf Count and Area From TLS Point Clouds

Terrestrial laser scanning (TLS) has revolutionized tree-level measurement, enabling accurate trunk, and branch analysis, but it faces challenges in counting and measuring individual leaves in broad-leaved trees. We introduce TLSLeaf, an innovative method designed to accurately measure leaf count and area, specifically tailored for complex tree canopies. TLSLeaf addresses critical gaps in TLS-based leaf analysis through a four-step process: 1) wood-leaf separation; 2) individual leaf segmentation; 3) detection and repair of incomplete leaves; and 4) leaf counting and area measurement. TLSLeaf integrates graph theory and shortest-path algorithms for effective branch-leaf separation, followed by instance segmentation using a similarity graph. To overcome the challenge of TLS canopy occlusion and imperfect leaf scans, incomplete leaves are detected and repaired based on symmetry and concavity principles. TLSLeaf’s accuracy was validated through field scanning, in situ measurements, and destructive sampling. TLSLeaf showed a percentage error of 1.49%–8.60% for leaf counts (ranging from 201 to 4000 leaves) and achieved an $R^{2}$ of 0.95 and RMSE of 5.87 cm2 for leaf areas (ranging from 9.98 to 179.77 cm2). TLSLeaf integrates semantic and instance segmentation through an unsupervised framework, presenting a “white-box” approach that ensures transparency and reproducibility. TLSLeaf represents a significant advancement in leaf-level analysis of TLS point clouds, with potential applications in enhancing tree reconstruction, 3-D radiative transfer modeling, and canopy photosynthesis.