Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering

Yi Li; Donghui Xie; Yingjie Wang; Shuangna Jin; Kun Zhou; Zhixiang Zhang; Weihua Li; Wuming Zhang; Xihan Mu; Guangjian Yan

doi:10.1002/ece3.10297

Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering

Ecol Evol. 2023 Jul 12;13(7):e10297. doi: 10.1002/ece3.10297. eCollection 2023 Jul.

Authors

Affiliations

¹ State Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products Beijing Normal University Beijing China.
² CESBIO University of Toulouse Toulouse France.
³ School of Geospatial Engineering and Science Sun Yat-Sen University Zhuhai China.

Abstract

Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m². Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (R _match) of tree tops is up to 0.92, the coefficient of determination (R ²) of tree height estimation is up to .94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping.

Keywords: airborne LiDAR scanning; connection center evolution; individual tree segmentation; unmanned aerial vehicle LiDAR scanning; watershed.