Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data
Forest tree information is crucial for monitoring forest resources and developing forestry management strategies. Airborne laser scanning is an efficient and rapid method for acquiring 3-D point cloud data of forests, making up for the shortcomings of optical remote sensing. Especially for unmanned...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10907937/ |
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| author | Shihua Li Shunda Zhao Zhilin Tian Hao Tang Zhonghua Su |
| author_facet | Shihua Li Shunda Zhao Zhilin Tian Hao Tang Zhonghua Su |
| author_sort | Shihua Li |
| collection | DOAJ |
| description | Forest tree information is crucial for monitoring forest resources and developing forestry management strategies. Airborne laser scanning is an efficient and rapid method for acquiring 3-D point cloud data of forests, making up for the shortcomings of optical remote sensing. Especially for unmanned aircraft vehicle laser scanning, the high-density point cloud provides the possibility of a detailed depiction of forest structures. Extracting individual trees from point cloud data is a prerequisite for fine forestry research. Currently, existing methods fail to fully utilize the height information, density information, and vertical structure details of tree crowns within point cloud data, resulting in complex algorithmic processes and reduced reliability. This study proposes a novel method that combines the canopy height model (CHM) and point cloud data for segmenting individual trees and employs height and density information for morphological detection of tree canopies. First, based on CHM-based segmentation, the point cloud density feature is introduced to identify the wrong segmented trees. Next, the height and density information are combined to guide the selection of vertical profiles. Finally, a 3-D morphological detection is conducted on the profiles to extract the potential trees. We validate the accuracy of the method on German forests. The results indicated that the average <italic>F</italic>1-scores of six study plots were 0.95, which improved by 5% after the introduction of density information. The primary source of errors was the irregularity of tree morphology. Our method produced reasonable results across different parameter settings, demonstrating its insensitivity to parameter configurations. |
| format | Article |
| id | doaj-art-272292cf60a64d8ebff6b759606a54a3 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-272292cf60a64d8ebff6b759606a54a32025-08-20T02:26:15ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01188897890910.1109/JSTARS.2025.354665110907937Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR DataShihua Li0https://orcid.org/0000-0003-4807-5012Shunda Zhao1Zhilin Tian2https://orcid.org/0000-0002-2901-0574Hao Tang3https://orcid.org/0009-0002-4948-8900Zhonghua Su4https://orcid.org/0009-0003-4553-4874School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Computer and Software Engineering, Xihua University, Chengdu, ChinaForest tree information is crucial for monitoring forest resources and developing forestry management strategies. Airborne laser scanning is an efficient and rapid method for acquiring 3-D point cloud data of forests, making up for the shortcomings of optical remote sensing. Especially for unmanned aircraft vehicle laser scanning, the high-density point cloud provides the possibility of a detailed depiction of forest structures. Extracting individual trees from point cloud data is a prerequisite for fine forestry research. Currently, existing methods fail to fully utilize the height information, density information, and vertical structure details of tree crowns within point cloud data, resulting in complex algorithmic processes and reduced reliability. This study proposes a novel method that combines the canopy height model (CHM) and point cloud data for segmenting individual trees and employs height and density information for morphological detection of tree canopies. First, based on CHM-based segmentation, the point cloud density feature is introduced to identify the wrong segmented trees. Next, the height and density information are combined to guide the selection of vertical profiles. Finally, a 3-D morphological detection is conducted on the profiles to extract the potential trees. We validate the accuracy of the method on German forests. The results indicated that the average <italic>F</italic>1-scores of six study plots were 0.95, which improved by 5% after the introduction of density information. The primary source of errors was the irregularity of tree morphology. Our method produced reasonable results across different parameter settings, demonstrating its insensitivity to parameter configurations.https://ieeexplore.ieee.org/document/10907937/Densityindividual tree segmentationmorphologyregion growingunmanned aerial vehicle (UAV) light detection and ranging (LiDAR) |
| spellingShingle | Shihua Li Shunda Zhao Zhilin Tian Hao Tang Zhonghua Su Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Density individual tree segmentation morphology region growing unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) |
| title | Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data |
| title_full | Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data |
| title_fullStr | Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data |
| title_full_unstemmed | Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data |
| title_short | Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data |
| title_sort | individual tree segmentation based on region growing and density guided canopy 3 d morphology detection using uav lidar data |
| topic | Density individual tree segmentation morphology region growing unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) |
| url | https://ieeexplore.ieee.org/document/10907937/ |
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