Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering
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 (AL...
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Wiley
2023-07-01
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| Series: | Ecology and Evolution |
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| Online Access: | https://doi.org/10.1002/ece3.10297 |
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| author | Yi Li Donghui Xie Yingjie Wang Shuangna Jin Kun Zhou Zhixiang Zhang Weihua Li Wuming Zhang Xihan Mu Guangjian Yan |
| author_facet | Yi Li Donghui Xie Yingjie Wang Shuangna Jin Kun Zhou Zhixiang Zhang Weihua Li Wuming Zhang Xihan Mu Guangjian Yan |
| author_sort | Yi Li |
| collection | DOAJ |
| description | 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 (Rmatch) of tree tops is up to 0.92, the coefficient of determination (R2) 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. |
| format | Article |
| id | doaj-art-d12262243b444f83b6cd60c47a20e913 |
| institution | DOAJ |
| issn | 2045-7758 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj-art-d12262243b444f83b6cd60c47a20e9132025-08-20T02:56:31ZengWileyEcology and Evolution2045-77582023-07-01137n/an/a10.1002/ece3.10297Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clusteringYi Li0Donghui Xie1Yingjie Wang2Shuangna Jin3Kun Zhou4Zhixiang Zhang5Weihua Li6Wuming Zhang7Xihan Mu8Guangjian Yan9State Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products Beijing Normal University Beijing ChinaState Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products Beijing Normal University Beijing ChinaCESBIO University of Toulouse Toulouse FranceState Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products Beijing Normal University Beijing ChinaState Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products Beijing Normal University Beijing ChinaState Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products Beijing Normal University Beijing ChinaState Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products Beijing Normal University Beijing ChinaSchool of Geospatial Engineering and Science Sun Yat‐Sen University Zhuhai ChinaState Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products Beijing Normal University Beijing ChinaState Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products Beijing Normal University Beijing ChinaAbstract 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 (Rmatch) of tree tops is up to 0.92, the coefficient of determination (R2) 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.https://doi.org/10.1002/ece3.10297airborne LiDAR scanningconnection center evolutionindividual tree segmentationunmanned aerial vehicle LiDAR scanningwatershed |
| spellingShingle | Yi Li Donghui Xie Yingjie Wang Shuangna Jin Kun Zhou Zhixiang Zhang Weihua Li Wuming Zhang Xihan Mu Guangjian Yan Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering Ecology and Evolution airborne LiDAR scanning connection center evolution individual tree segmentation unmanned aerial vehicle LiDAR scanning watershed |
| title | Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering |
| title_full | Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering |
| title_fullStr | Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering |
| title_full_unstemmed | Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering |
| title_short | Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering |
| title_sort | individual tree segmentation of airborne and uav lidar point clouds based on the watershed and optimized connection center evolution clustering |
| topic | airborne LiDAR scanning connection center evolution individual tree segmentation unmanned aerial vehicle LiDAR scanning watershed |
| url | https://doi.org/10.1002/ece3.10297 |
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