Li, W., Q. Guo, M. Jakubowski and M. Kelly. 2012. A new method for segmenting individual trees from the lidar point cloud. Photogrammetric Engineering and Remote Sensing 78(1): 75-84
Light Detection and Ranging (lidar) has been widely applied to characterize the 3-dimensional (3D) structure of forests as it can generate 3D point 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. The new algorithm adopts a top-to-bottom region growing approach that segments trees individually and sequentially from the tallest to the shortest. We experimentally applied the new algorithm to segment trees in a mixed coniferous forest in the Sierra Nevada Mountains in California, USA. The results were evaluated in terms of recall, precision, and F-score, and show that the algorithm detected 86% of the trees (“recall”), 94% 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. Pdf download.
Keywords: lidar, point cloud, tree segmentation, F-score