Individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation
Terrestrial laser scanning (TLS) accurately captures tree structural information and provides prerequisites for tree-scale estimations of forest biophysical attributes. Quantifying tree-scale attributes from TLS point clouds requires segmentation, yet the occlusion effects severely affect the accura...
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Forest Ecosystems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2197562024000745 |
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| author | Qingjun Zhang Shangshu Cai Xinlian Liang |
| author_facet | Qingjun Zhang Shangshu Cai Xinlian Liang |
| author_sort | Qingjun Zhang |
| collection | DOAJ |
| description | Terrestrial laser scanning (TLS) accurately captures tree structural information and provides prerequisites for tree-scale estimations of forest biophysical attributes. Quantifying tree-scale attributes from TLS point clouds requires segmentation, yet the occlusion effects severely affect the accuracy of automated individual tree segmentation. In this study, we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion effects. Firstly, region growing and point compensation algorithms are used to determine the location of tree roots. Secondly, the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor (KNN). Thirdly, neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory consumption. Finally, a graph describing connectivity between a point and its neighbors is constructed, and it is utilized to complete individual tree segmentation based on the shortest path algorithm. The proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo, Finland, and it reached the highest mean accuracy of 77.5%, higher than previous studies on tree detection. We also extracted and validated the tree structure attributes using manual segmentation reference values. The RMSE, RMSE%, bias, and bias% of tree height, crown base height, crown projection area, crown surface area, and crown volume were used to evaluate the segmentation accuracy, respectively. Overall, the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands. |
| format | Article |
| id | doaj-art-2ec7f0c303164e99bf2a35c4ed33b21b |
| institution | OA Journals |
| issn | 2197-5620 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Forest Ecosystems |
| spelling | doaj-art-2ec7f0c303164e99bf2a35c4ed33b21b2025-08-20T02:11:38ZengKeAi Communications Co., Ltd.Forest Ecosystems2197-56202024-01-011110023810.1016/j.fecs.2024.100238Individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensationQingjun Zhang0Shangshu Cai1Xinlian Liang2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, ChinaCorresponding author.; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, ChinaTerrestrial laser scanning (TLS) accurately captures tree structural information and provides prerequisites for tree-scale estimations of forest biophysical attributes. Quantifying tree-scale attributes from TLS point clouds requires segmentation, yet the occlusion effects severely affect the accuracy of automated individual tree segmentation. In this study, we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion effects. Firstly, region growing and point compensation algorithms are used to determine the location of tree roots. Secondly, the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor (KNN). Thirdly, neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory consumption. Finally, a graph describing connectivity between a point and its neighbors is constructed, and it is utilized to complete individual tree segmentation based on the shortest path algorithm. The proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo, Finland, and it reached the highest mean accuracy of 77.5%, higher than previous studies on tree detection. We also extracted and validated the tree structure attributes using manual segmentation reference values. The RMSE, RMSE%, bias, and bias% of tree height, crown base height, crown projection area, crown surface area, and crown volume were used to evaluate the segmentation accuracy, respectively. Overall, the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands.http://www.sciencedirect.com/science/article/pii/S2197562024000745Terrestrial laser scanningIndividual tree segmentationGraphThe shortest pathEllipsoid directional searchingPoint compensation |
| spellingShingle | Qingjun Zhang Shangshu Cai Xinlian Liang Individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation Forest Ecosystems Terrestrial laser scanning Individual tree segmentation Graph The shortest path Ellipsoid directional searching Point compensation |
| title | Individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation |
| title_full | Individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation |
| title_fullStr | Individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation |
| title_full_unstemmed | Individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation |
| title_short | Individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation |
| title_sort | individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation |
| topic | Terrestrial laser scanning Individual tree segmentation Graph The shortest path Ellipsoid directional searching Point compensation |
| url | http://www.sciencedirect.com/science/article/pii/S2197562024000745 |
| work_keys_str_mv | AT qingjunzhang individualtreesegmentationinoccludedcomplexforeststandsthroughellipsoiddirectionalsearchingandpointcompensation AT shangshucai individualtreesegmentationinoccludedcomplexforeststandsthroughellipsoiddirectionalsearchingandpointcompensation AT xinlianliang individualtreesegmentationinoccludedcomplexforeststandsthroughellipsoiddirectionalsearchingandpointcompensation |