Automatic tree crown segmentation using dense forest point clouds from Personal Laser Scanning (PLS)

Abstract

Among digital-based technologies to monitor forest ecosystems, personal laser scanning (PLS) has high potential to characterize even complex deciduous and rainforests. PLS data include a complete and detailed 3D representation of forest stands, but tree individuals need to be segmented accurately before retrieving tree characteristics. As manual on-screen segmentation is time-consuming and labor intensive, we suggest an automatic voxel-based region growing crown segmentation algorithm. Diameter at breast height (dbh), tree height, crown base height (cbh), crown projection area (cpa) and crown volume were automatically extracted from single tree point clouds. The methodology was validated on previously published PLS raw data in terms of segmentation accuracy and measurement precision. Manual segmentation, field measurements, and geometrical crown models were used as reference data. The overall segmentation accuracy of the crowns was 87.02%and tree height was accurately measured with a bias of −0.05 m and a root mean square deviation (RMSD) of 1.21 m (6.33%). Existing geometric crown models proved to be a realistic approximation of the true crown architecture and matched the measured tree crown volume with a bias of −4.62 m3 and a RMSD of 63.02 m3 (31.72%). Tree height and cpa were not affected by segmentation accuracy, but a major challenge remained in estimating cbh. The proposed methodology provides an efficient and low-cost solution for a fully automatic and digital forest inventory.

Keywords

• Personal laser scanning
• Automatic tree segmentation
• Region growing
• Forest point cloud data
• Crown models
• Forest inventory