Combining Trunk Detection With Canopy Segmentation to Delineate Single Deciduous Trees Using Airborne LiDAR Data

To minimize omission and commission errors due to the lack of adequate utilization of forest structure information, this paper presents a tree delineation approach by combining trunk detection with canopy segmentation. First, all potential tree trunk points are detected and detached from leaf-off data by analyzing the points' vertical histogram, and the obtained points are then clustered using the method based on DBSCAN (Density-Based Spatial Clustering of Application with Noise). Meanwhile, the canopy-based segmentation is implemented using leaf-on data within the same plot. The detected trunks and delineated crown segments are then combined using the matching rules. Finally, single trees are isolated from point clouds, and tree-level structure information is estimated. The novelty of this approach lies in that the trunk detection results and the canopy segmentation results serve as mutual references for final individual tree delineation. Experimental results in a canopy-closed deciduous natural forest show that the presented method can identify 84.0% of trees, 90.7% of the identified trees are correct, and the total segmentation accuracy is 87.2%. The determination coefficient R
²
of tree height is 0.96, and the mean difference of tree position is 76 cm. The results imply that the presented approach has good potential for isolating single trees from airborne LiDAR point clouds and estimating tree-level structural parameters in deciduous forests.