Individual Tree Segmentation From Airborne LiDAR Data Based on Automatic Treetop Detection and Simulated Stem-Branch Points Compensation

Accurate retrieval of tree structural attributes is essential for effective forest management and resource assessment. Airborne laser scanning (ALS) provides detailed threedimensional data from a top-down perspective, enabling the analysis of forest canopy structures at a large-scale. However, due to its limited penetration through dense canopies, ALS point clouds under the crown is often sparse or even missing, making individual tree segmentation particularly challenging. Therefore, we propose a novel individual tree segmentation method based on treetop detection and point compensation. First, a center shift algorithm (CSA) is proposed to detect treetop candidates, which are then refined to identify reliable treetops through geometricfeature analysis of these candidates' neighborhoods. Next, simulated stem-branch points are compensated with crown shape constraints to reconstruct sparse under-canopy structures. Finally, individual trees are segmented via a shortest-path algorithm applied to a graph that represents connectivity between adjacent points. The proposed method is evaluated using a public available dataset. The average extraction rate, completeness of treetop detection, and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for tree height estimation were 108%, 51%, and 0.93, respectively. In comparison to existing methods, the obtained the proposed method gave a similar F-Score as the best method, i.e., only 1% lower, but achieves a significantly better extraction rate, demonstrating superior overall performance. The proposed method enables accurate individual tree parameter extraction, advancing forest inventory efficiency and providing a foundation for ecological studies.