A Hierarchical Approach to Three-Dimensional Segmentation of LiDAR Data at Single-Tree Level in a Multilayered Forest

Small-footprint high-density LiDAR data provide information on both the dominant and the subdominant layers of the forest. However, tree detection is usually carried out in the Canopy Height Model (CHM) image domain, where not all the dominant trees are distinguishable and the understory vegetation is not visible. To address these issues, we propose a novel method that integrates the analysis of the CHM with that of the point cloud space (PCS) to 1) improve the accuracy in the detection and delineation of the dominant trees and 2) identify and delineate the subdominant trees. By means of a derivative analysis of the horizontal profile of the forest, the method detects the missed crowns and delineates the crown boundaries directly in the PCS. Then, for each segmented crown, the vertical profile is analyzed to identify the presence of subcanopies and extract them. The proposed method does not require any prior knowledge on the stand properties (e.g., crown size and forest density). Experimental results obtained on two LiDAR data sets characterized by different laser point density show that the proposed method always improved the detection rate compared to other state-of-the-art techniques. It correctly detected 97% and 92% of the dominant trees measured in situ in high-and low-density LiDAR data, respectively. Moreover, it automatically identified 77% of the subdominant trees manually extracted by an expert operator in the high-density LiDAR data.