“Objective” Tropical mangroves are one of the most productive and biodiverse forest resources but face several challenges such as monoculture planting structures, low ecosystem quality, low survival rates of planted mangroves, and threats from extreme weather and pests. Conducting surveys on mangrove forest resources provides essential data for the scientific management and conservation of these resources. Accurate segmentation of individual trees is a prerequisite for the inventory of such forest resources. Terrestrial laser scanning (TLS) can provide massive, high-precision, and high-resolution 3D point cloud data. However, the point cloud data are characterized by irregularities, varying densities due to distance, and incompleteness due to occlusions. Furthermore, the mangrove scene is complex with interlaced large and small trees and tree occlusions, making precise individual tree segmentation a considerable challenge. Traditional methods such as local maximum detection based on Canopy Height Models (CHM), have demonstrated good performance in simple plot scenarios. However, in the complex canopy interwoven environments of mangroves, where the upper canopy features are weak, these methods are less effective. Currently, there is a lack of research on individual tree segmentation algorithms for mangroves based on TLS point clouds. To address these issues, we aim to propose an individual tree segmentation algorithm applicable for complex mangrove scenes.“Method” This study innovatively combines deep learning and traditional algorithms to propose a high-precision individual tree segmentation framework for TLS point clouds in complex mangrove scenes. The framework initially employs the deep learning network RandLA-Net for ground filtering and wood-leaf separation. Subsequently, mangrove main stems are segmented using a connected component segmentation method. Finally, individual tree segmentation is achieved through the multiple tree tops constraint module.“Results” To assess the accuracy of the algorithm, we use three measures: completeness, correctness, and accuracy. We also conduct a comparative analysis with two classical algorithms. The experimental results demonstrate that the completeness of the proposed method across different mangrove plots is greater than 0.85, with an average of 0.90; the correctness of the proposed method is greater than the two classic algorithms in four plots; the mean accuracy of the proposed method in different sample plots reaches 0.87, which is significantly higher than the two classic algorithms, thus proving the effectiveness and reliability of our method.“Conclusion” This paper proposes an individual tree segmentation framework for TLS point clouds in complex mangrove scenes. Seven sample plots with various data characteristics were annotated to assess accuracy. The experimental results show that, compared to other algorithms, the proposed method achieved the highest accuracy. Despite the differing characteristics of the sample plots, the overall accuracy of the proposed method exceeded 0.8, demonstrating its effectiveness and robustness.

tropical mangroves;terrestrial laser scanning;point cloud;individual tree segmentation;deep learning