ALS Point Cloud Individual Tree Segmentation
Overview
Forest resource surveys are crucial for timely acquisition of forest resource information. One important aspect of forest resource surveys is measuring individual tree parameters within sample plots, such as tree species, location, height, and DBH. To obtain individual tree parameters from LiDAR point cloud data, individual tree segmentation must be performed first. Currently, individual tree segmentation methods can be divided into CHM-based segmentation, point cloud-based segmentation, and image-based segmentation.
CHM-Based Segmentation Processing Workflow
Using LiDAR360 for CHM segmentation generally involves the following steps: denoising, filtering, generating Digital Elevation Model (DEM), Digital Surface Model (DSM) and Canopy Height Model (CHM), and performing individual tree segmentation based on CHM.
Point Cloud-Based Segmentation Processing Workflow
Using LiDAR360 for point cloud segmentation generally involves the following steps: denoising, ground point classification, normalization, and point cloud segmentation. The point cloud segmentation step can be replaced with trunk-based individual tree segmentation.