IT-Seg: Morphology-Constrained and Spatially Guided Individual Tree Segmentation from Terrestrial and Mobile Point Clouds

Individual trees are essential components of forest ecosystems, and accurate tree-level segmentation provides a crucial foundation for forest ecosystem modeling and biodiversity assessment. We propose a novel point cloud-based individual tree segmentation method guided by morphological prior constraints and single-tree canopy radiative effective extent (SCREE), a metric used to define the canopy boundary for each tree, computed from high-dimensional features output by a network. First, the original point cloud is divided into a central point set and a buffer point set based on morphological prior constraints. On this basis, different strategies are applied for trunk extraction from the central and buffer points: trunks in the central point set are extracted through adaptive density-based filtering, while trunks in the buffer point set are extracted using a semantic segmentation network. Finally, canopy points are assigned to individual trunks to obtain complete tree structures. This assignment is guided by the proposed SCREE, inferred using a diameter at breast height–height generative model to generate a constrained space. Validated on six forest scenes from three public data sets, the proposed method achieves an average instance-level accuracy of 84.71% and an average point-level accuracy of 81.85%, demonstrating strong robustness across diverse forest environments. Moreover, compared with existing methods, our method exhibits higher stability when handling variations in samples and the presence of small trees across different types of scanners.