Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data

Forest tree information is crucial for monitoring forest resources and developing forestry management strategies. Airborne laser scanning is an efficient and rapid method for acquiring 3-D point cloud data of forests, making up for the shortcomings of optical remote sensing. Especially for unmanned aircraft vehicle laser scanning, the high-density point cloud provides the possibility of a detailed depiction of forest structures. Extracting individual trees from point cloud data is a prerequisite for fine forestry research. Currently, existing methods fail to fully utilize the height information, density information, and vertical structure details of tree crowns within point cloud data, resulting in complex algorithmic processes and reduced reliability. This study proposes a novel method that combines the canopy height model (CHM) and point cloud data for segmenting individual trees and employs height and density information for morphological detection of tree canopies. First, based on CHM-based segmentation, the point cloud density feature is introduced to identify the wrong segmented trees. Next, the height and density information are combined to guide the selection of vertical profiles. Finally, a 3-D morphological detection is conducted on the profiles to extract the potential trees. We validate the accuracy of the method on German forests. The results indicated that the average F1-scores of six study plots were 0.95, which improved by 5% after the introduction of density information. The primary source of errors was the irregularity of tree morphology. Our method produced reasonable results across different parameter settings, demonstrating its insensitivity to parameter configurations.