Individual-tree segmentation and extraction based on LiDAR point cloud data
To extract forest parameters and individual tree information accurately and efficiently from plantations, this study focuses on a plantation of Pinus tabulaeformis in Chongli District in China. Utilizing LiDAR point cloud data and ground-measured data from 30 plots, we examined the sensitivity of in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Insight Society
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/117084/ http://psasir.upm.edu.my/id/eprint/117084/1/117084.pdf |
| _version_ | 1848867162706411520 |
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| author | Liu, Xiaofeng Abdullah, Muhamad Taufik Mustaffa, Mas Rina Nasharuddin, Nurul Amelina |
| author_facet | Liu, Xiaofeng Abdullah, Muhamad Taufik Mustaffa, Mas Rina Nasharuddin, Nurul Amelina |
| author_sort | Liu, Xiaofeng |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | To extract forest parameters and individual tree information accurately and efficiently from plantations, this study focuses on a plantation of Pinus tabulaeformis in Chongli District in China. Utilizing LiDAR point cloud data and ground-measured data from 30 plots, we examined the sensitivity of individual tree segmentation to key parameters by varying the grid values of the point cloud distance discriminant clustering algorithm and adjusting the canopy height resolution (CHR) of the watershed algorithm. The objective was to identify the optimal parameters for both algorithms in terms of tree height extraction precision. In the task of individual tree extraction, the point cloud distance discriminant clustering algorithm outperformed the watershed algorithm. This was evidenced by significantly higher recall, precision, and F1-score. However, in terms of tree height precision, as measured by the coefficient of determination and root mean square error (RMSE), the watershed algorithm proved superior. Specifically, the watershed algorithm achieved a coefficient of determination of 0.88 and an RMSE of 0.93 meters, indicating greater precision in estimating tree parameters. Nonetheless, the optimal parameter settings for the watershed algorithm need to be adjusted based on stand density. Thus, through this study, we found that for individual-tree extraction from LiDAR point cloud data, the initial setting of different grid values and resolutions has a significant impact on segmentation precision. It is essential to design tailored approaches for processing point cloud data under varying environmental conditions to achieve optimal results and precision. |
| first_indexed | 2025-11-15T14:32:07Z |
| format | Article |
| id | upm-117084 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:32:07Z |
| publishDate | 2024 |
| publisher | Insight Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1170842025-04-25T04:15:01Z http://psasir.upm.edu.my/id/eprint/117084/ Individual-tree segmentation and extraction based on LiDAR point cloud data Liu, Xiaofeng Abdullah, Muhamad Taufik Mustaffa, Mas Rina Nasharuddin, Nurul Amelina To extract forest parameters and individual tree information accurately and efficiently from plantations, this study focuses on a plantation of Pinus tabulaeformis in Chongli District in China. Utilizing LiDAR point cloud data and ground-measured data from 30 plots, we examined the sensitivity of individual tree segmentation to key parameters by varying the grid values of the point cloud distance discriminant clustering algorithm and adjusting the canopy height resolution (CHR) of the watershed algorithm. The objective was to identify the optimal parameters for both algorithms in terms of tree height extraction precision. In the task of individual tree extraction, the point cloud distance discriminant clustering algorithm outperformed the watershed algorithm. This was evidenced by significantly higher recall, precision, and F1-score. However, in terms of tree height precision, as measured by the coefficient of determination and root mean square error (RMSE), the watershed algorithm proved superior. Specifically, the watershed algorithm achieved a coefficient of determination of 0.88 and an RMSE of 0.93 meters, indicating greater precision in estimating tree parameters. Nonetheless, the optimal parameter settings for the watershed algorithm need to be adjusted based on stand density. Thus, through this study, we found that for individual-tree extraction from LiDAR point cloud data, the initial setting of different grid values and resolutions has a significant impact on segmentation precision. It is essential to design tailored approaches for processing point cloud data under varying environmental conditions to achieve optimal results and precision. Insight Society 2024-10-31 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/117084/1/117084.pdf Liu, Xiaofeng and Abdullah, Muhamad Taufik and Mustaffa, Mas Rina and Nasharuddin, Nurul Amelina (2024) Individual-tree segmentation and extraction based on LiDAR point cloud data. International Journal on Advanced Science, Engineering and Information Technology, 14 (5). pp. 1800-1808. ISSN 2088-5334; eISSN: 2460-6952 https://ijaseit.insightsociety.org/index.php/ijaseit/article/view/11332 10.18517/ijaseit.14.5.11332 |
| spellingShingle | Liu, Xiaofeng Abdullah, Muhamad Taufik Mustaffa, Mas Rina Nasharuddin, Nurul Amelina Individual-tree segmentation and extraction based on LiDAR point cloud data |
| title | Individual-tree segmentation and extraction based on LiDAR point cloud data |
| title_full | Individual-tree segmentation and extraction based on LiDAR point cloud data |
| title_fullStr | Individual-tree segmentation and extraction based on LiDAR point cloud data |
| title_full_unstemmed | Individual-tree segmentation and extraction based on LiDAR point cloud data |
| title_short | Individual-tree segmentation and extraction based on LiDAR point cloud data |
| title_sort | individual-tree segmentation and extraction based on lidar point cloud data |
| url | http://psasir.upm.edu.my/id/eprint/117084/ http://psasir.upm.edu.my/id/eprint/117084/ http://psasir.upm.edu.my/id/eprint/117084/ http://psasir.upm.edu.my/id/eprint/117084/1/117084.pdf |