An adaptive method for individual tree segmentation synthesizing canopy cover and competitive mechanism using UAV data
Accurate individual tree segmentation (ITS) is crucial for precision forestry and small-scale carbon sink accounting; however, canopy overlap in complex forest stands—particularly in northern plantations, presents substantial challenges for conducting ITS using LiDAR point cloud. This study introduc...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-11-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003693 |
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| author | Qiyu Guo Kangning Li Xiaojun Qiao Jinbao Jiang Yinpeng Zhao |
| author_facet | Qiyu Guo Kangning Li Xiaojun Qiao Jinbao Jiang Yinpeng Zhao |
| author_sort | Qiyu Guo |
| collection | DOAJ |
| description | Accurate individual tree segmentation (ITS) is crucial for precision forestry and small-scale carbon sink accounting; however, canopy overlap in complex forest stands—particularly in northern plantations, presents substantial challenges for conducting ITS using LiDAR point cloud. This study introduces an adaptive ITS method that incorporates canopy cover as the primary constraint in marker-controlled watershed segmentation. This addresses two typical segmentation biases: low canopy cover areas that are prone to under-segmentation are refined using the DBSCAN spatial clustering to recover missed tree boundaries, whereas high canopy cover regions that were prone to over-segmentation were optimized using Hegyi index-enhanced improved K-means clustering method of raw point cloud data for context-aware region merging. By fusing the canopy height model (CHM) efficiency for rapid canopy contour extraction with point cloud-derived 3D structural details, this “cover-degree-driven, scene-adaptive” method balances computational speed and segmentation precision. The method was validated across 28 plots, the method achieving F1 scores of 0.89–0.95 for four tree species and outperforming traditional ITS methods in mixed forests with F1 improvements of 0.12–0.24. This method enhances the ITS accuracy of individual tree aboveground biomass estimation, thereby directly facilitating efficient small-scale carbon accounting, streamlined forest inventories, and sustainable precision management in complex ecosystems. |
| format | Article |
| id | doaj-art-65bdef95e3fc4023ab07c3d63b682921 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-65bdef95e3fc4023ab07c3d63b6829212025-08-20T03:38:19ZengElsevierEcological Informatics1574-95412025-11-019110336010.1016/j.ecoinf.2025.103360An adaptive method for individual tree segmentation synthesizing canopy cover and competitive mechanism using UAV dataQiyu Guo0Kangning Li1Xiaojun Qiao2Jinbao Jiang3Yinpeng Zhao4College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaCorresponding author.; College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaAccurate individual tree segmentation (ITS) is crucial for precision forestry and small-scale carbon sink accounting; however, canopy overlap in complex forest stands—particularly in northern plantations, presents substantial challenges for conducting ITS using LiDAR point cloud. This study introduces an adaptive ITS method that incorporates canopy cover as the primary constraint in marker-controlled watershed segmentation. This addresses two typical segmentation biases: low canopy cover areas that are prone to under-segmentation are refined using the DBSCAN spatial clustering to recover missed tree boundaries, whereas high canopy cover regions that were prone to over-segmentation were optimized using Hegyi index-enhanced improved K-means clustering method of raw point cloud data for context-aware region merging. By fusing the canopy height model (CHM) efficiency for rapid canopy contour extraction with point cloud-derived 3D structural details, this “cover-degree-driven, scene-adaptive” method balances computational speed and segmentation precision. The method was validated across 28 plots, the method achieving F1 scores of 0.89–0.95 for four tree species and outperforming traditional ITS methods in mixed forests with F1 improvements of 0.12–0.24. This method enhances the ITS accuracy of individual tree aboveground biomass estimation, thereby directly facilitating efficient small-scale carbon accounting, streamlined forest inventories, and sustainable precision management in complex ecosystems.http://www.sciencedirect.com/science/article/pii/S1574954125003693Individual tree segmentationAdaptive marker-controlled watershed algorithmPoint cloud optimizationCanopy coverTree-growth competition |
| spellingShingle | Qiyu Guo Kangning Li Xiaojun Qiao Jinbao Jiang Yinpeng Zhao An adaptive method for individual tree segmentation synthesizing canopy cover and competitive mechanism using UAV data Ecological Informatics Individual tree segmentation Adaptive marker-controlled watershed algorithm Point cloud optimization Canopy cover Tree-growth competition |
| title | An adaptive method for individual tree segmentation synthesizing canopy cover and competitive mechanism using UAV data |
| title_full | An adaptive method for individual tree segmentation synthesizing canopy cover and competitive mechanism using UAV data |
| title_fullStr | An adaptive method for individual tree segmentation synthesizing canopy cover and competitive mechanism using UAV data |
| title_full_unstemmed | An adaptive method for individual tree segmentation synthesizing canopy cover and competitive mechanism using UAV data |
| title_short | An adaptive method for individual tree segmentation synthesizing canopy cover and competitive mechanism using UAV data |
| title_sort | adaptive method for individual tree segmentation synthesizing canopy cover and competitive mechanism using uav data |
| topic | Individual tree segmentation Adaptive marker-controlled watershed algorithm Point cloud optimization Canopy cover Tree-growth competition |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125003693 |
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