From Peaks to Crowns: A Morphology-Based UAV-LiDAR Framework for Individual Tree Segmentation

Abstract. Recognising individual trees has important applications in forest ecology and management. Conventional individual tree segmentation methods tend to favour dominant trees with pronounced canopy surface features but have limited capability in detecting subdominant trees that are partially occluded or have smaller crowns. To mitigate this issue, we propose a morphology-based method for individual tree segmentation. First, a treetop extraction method is developed based on morphological criteria. Candidate treetops are initially detected using local maximum filtering, followed by classification and validation through vertical profile analysis integrated with crown morphological characteristics. Subsequently, the extracted treetops serve as seed points to guide individual tree crown delineation within a Min-cut/Max-flow graph cut framework, leveraging the spatial relationships among points. Our method enhances the detection of subdominant trees, with detection rates climbing to 90–95%, and achieves an average F-score of 0.8 for crown delineation, which outperforms the other methods by 0.24 points. By integrating treetop information with local crown features, the proposed method improves the detection and segmentation accuracy of subdominant trees in complex forest environments, supporting overstory structure analysis and individual tree inventory in intricate forests.