A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR
Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/8/1442 |
| _version_ | 1827695621054660608 |
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| author | Kaisen Ma Yujiu Xiong Fugen Jiang Song Chen Hua Sun |
| author_facet | Kaisen Ma Yujiu Xiong Fugen Jiang Song Chen Hua Sun |
| author_sort | Kaisen Ma |
| collection | DOAJ |
| description | Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction. |
| first_indexed | 2024-03-10T12:30:47Z |
| format | Article |
| id | doaj.art-47ab7636930b4ba8a9d3acd5e26dd514 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:30:47Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-47ab7636930b4ba8a9d3acd5e26dd5142023-11-21T14:41:35ZengMDPI AGRemote Sensing2072-42922021-04-01138144210.3390/rs13081442A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDARKaisen Ma0Yujiu Xiong1Fugen Jiang2Song Chen3Hua Sun4Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaSchool of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaResearch Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaResearch Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaResearch Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaDetecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.https://www.mdpi.com/2072-4292/13/8/1442single-tree segmentationUAVLiDARvegetation point cloud density modelimproved watershed algorithm |
| spellingShingle | Kaisen Ma Yujiu Xiong Fugen Jiang Song Chen Hua Sun A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR Remote Sensing single-tree segmentation UAV LiDAR vegetation point cloud density model improved watershed algorithm |
| title | A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR |
| title_full | A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR |
| title_fullStr | A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR |
| title_full_unstemmed | A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR |
| title_short | A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR |
| title_sort | novel vegetation point cloud density tree segmentation model for overlapping crowns using uav lidar |
| topic | single-tree segmentation UAV LiDAR vegetation point cloud density model improved watershed algorithm |
| url | https://www.mdpi.com/2072-4292/13/8/1442 |
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