A Comparison of Tree Segmentation Methods for Savanna Tree Extraction from TLS Point Clouds

Detecting trees accurately from terrestrial laser scanning (TLS) point clouds is crucial for processing terrestrial LiDAR data in individual tree analyses. Due to the heterogeneity of savanna ecosystems, our understanding of how various segmentation methods perform on savanna trees remains limited. Therefore, we compared two segmentation algorithms based on the ecological theory of resource distribution, which enables the prediction of the branching geometry of plants. This approach suggests that the shortest path along the vegetation from a point on the tree to the ground remains within the same tree. The algorithms were tested on a 15.2 ha plot scanned at 0.025° resolution during the dry season, using a Riegl VZ1000 Terrestrial Laser Scanner (TLS) in October 2019 at the Skukuza Flux Tower in Kruger National Park, South Africa. Individual tree segmentation was performed on the cloud using the comparative shortest-path (CSP) algorithm, implemented in LiDAR 360 (v 5.4), and the shortest path-based tree isolation method (SPBTIM), implemented in MATLAB (R2022a). The accuracy of each segmentation method was validated using 125 trees that were segmented and manually edited. Results were evaluated using recall (r), precision (p), and the F-score (F). Both algorithms detected (recall) 90% of the trees. The SPBTIM achieved a precision of 91%, slightly higher than the CSP’s 90%. Overall, both methods demonstrated an F-score of 0.90, indicating equal segmentation accuracy. Our findings suggest that both techniques can reliably segment savanna trees, with no significant difference between them in practical application. These results provide valuable insights into the suitability of each method for savanna ecosystems, which is essential for ecological monitoring and efficient TLS data processing workflows.