A New Method for Segmenting Individual Trees from the Lidar Point Cloud

TLDR

Lidar provides high‑resolution 3D forest structure data, enabling individual tree segmentation from canopy height models to estimate attributes such as height and crown diameter. This study aims to develop a new algorithm for segmenting individual trees from small‑footprint discrete‑return airborne lidar point clouds. The algorithm was tested on a mixed conifer forest in the Sierra Nevada Mountains, using the point cloud to delineate tree crowns and evaluate performance. Evaluation showed 86 % recall, 94 % precision, and an overall F‑score of 0.9, indicating strong potential for segmenting trees in similar mixed conifer stands with small‑footprint lidar.

Abstract

Light Detection and Ranging (lidar) has been widely applied to characterize the 3-dimensional (3D) structure of forests as it can generate 3Dpoint data with high spatial resolution and accuracy. Individual tree segmentations, usually derived from the canopy height model, are used to derive individual tree structural attributes such as tree height, crown diameter, canopy-based height, and others. In this study, we develop a new algorithm to segment individual trees from the small footprint discrete return airborne lidar point cloud. We experimentally applied the new algorithm to segment trees in a mixed conifer forest in the Sierra Nevada Mountains in California. The results were evaluated in terms of recall, precision, and F-score, and show that the algorithm detected 86 percent of the trees (“recall”), 94 percent of the segmented trees were correct (“precision”), and the overall F-score is 0.9. Our results indicate that the proposed algorithm has good potential in segmenting individual trees in mixed conifer stands of similar structure using small footprint, discrete return lidar data.