Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data
Accurate extraction of tree parameters is vital for the calculation of high-quality forest volume or biomass. The Light Detection and Ranging (Lidar) technology with its ability to acquire three-dimensional forest stand structures is an important data source for extracting tree parameters. However,...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2024.2439399 |
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| author | Yunhe Li Guiying Li Sirong Wang Dengsheng Lu |
| author_facet | Yunhe Li Guiying Li Sirong Wang Dengsheng Lu |
| author_sort | Yunhe Li |
| collection | DOAJ |
| description | Accurate extraction of tree parameters is vital for the calculation of high-quality forest volume or biomass. The Light Detection and Ranging (Lidar) technology with its ability to acquire three-dimensional forest stand structures is an important data source for extracting tree parameters. However, complex forest stand structures due to dense canopy and abundant understory vegetation can seriously affect the extraction performance of tree parameters. This study selected Chinese fir plantations to examine how different forest stand structure conditions affected tree parameter extraction based on the integration of handheld laser scanning (HLS) and airborne laser scanning (ALS) data. This study also explored the ability of developing models using extracted tree height and crown width, or directly using the calculated variables from point clouds to estimate the diameter at breast height (DBH) and stem volume. The methods mainly consisted of two parts: (1) Selecting appropriate height to identify seed points for tree segmentation, and subsequently extracting tree parameters; (2) Taking tree height, crown width, and tree-level point cloud metrics as independent variables to establish regression and random forest models for estimating tree DBHs and stem volumes. Results showed that (1) tree DBHs were accurately extracted with relative root mean square error (RMSEr) of 7.1%−10.5% and stem volume with an RMSEr of 12.3%−16.8% under different understory conditions; (2) As stand structure became complex, direct extraction of tree DBHs from HLS data became a challenging task. However, developing a DBH estimation model using tree height and crown width proved feasible; (3) In the complex-understory condition, the volume estimation model utilizing point cloud variables achieved good performance with RMSEr of 28.0%. This research provides new insights for extracting tree parameters using HLS and ALS data, offering a potential replacement for field measurements of tree DBH, height, and stem volume for Chinese fir plantations. |
| format | Article |
| id | doaj-art-da1dc643bc67495d9be98fced07af1fc |
| institution | OA Journals |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-da1dc643bc67495d9be98fced07af1fc2025-08-20T02:36:49ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532024-12-0112210.1080/10095020.2024.2439399Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar dataYunhe Li0Guiying Li1Sirong Wang2Dengsheng Lu3Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, Fujian Normal University, Fuzhou, ChinaKey Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, Fujian Normal University, Fuzhou, ChinaBaisha State Forest Farm, Longyan, ChinaKey Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, Fujian Normal University, Fuzhou, ChinaAccurate extraction of tree parameters is vital for the calculation of high-quality forest volume or biomass. The Light Detection and Ranging (Lidar) technology with its ability to acquire three-dimensional forest stand structures is an important data source for extracting tree parameters. However, complex forest stand structures due to dense canopy and abundant understory vegetation can seriously affect the extraction performance of tree parameters. This study selected Chinese fir plantations to examine how different forest stand structure conditions affected tree parameter extraction based on the integration of handheld laser scanning (HLS) and airborne laser scanning (ALS) data. This study also explored the ability of developing models using extracted tree height and crown width, or directly using the calculated variables from point clouds to estimate the diameter at breast height (DBH) and stem volume. The methods mainly consisted of two parts: (1) Selecting appropriate height to identify seed points for tree segmentation, and subsequently extracting tree parameters; (2) Taking tree height, crown width, and tree-level point cloud metrics as independent variables to establish regression and random forest models for estimating tree DBHs and stem volumes. Results showed that (1) tree DBHs were accurately extracted with relative root mean square error (RMSEr) of 7.1%−10.5% and stem volume with an RMSEr of 12.3%−16.8% under different understory conditions; (2) As stand structure became complex, direct extraction of tree DBHs from HLS data became a challenging task. However, developing a DBH estimation model using tree height and crown width proved feasible; (3) In the complex-understory condition, the volume estimation model utilizing point cloud variables achieved good performance with RMSEr of 28.0%. This research provides new insights for extracting tree parameters using HLS and ALS data, offering a potential replacement for field measurements of tree DBH, height, and stem volume for Chinese fir plantations.https://www.tandfonline.com/doi/10.1080/10095020.2024.2439399Airborne Lidarhandheld laser scannertree segmentationtree parametersChinese fir |
| spellingShingle | Yunhe Li Guiying Li Sirong Wang Dengsheng Lu Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data Geo-spatial Information Science Airborne Lidar handheld laser scanner tree segmentation tree parameters Chinese fir |
| title | Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data |
| title_full | Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data |
| title_fullStr | Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data |
| title_full_unstemmed | Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data |
| title_short | Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data |
| title_sort | retrieval of chinese fir tree parameters under different understory conditions with the integration of handheld and airborne lidar data |
| topic | Airborne Lidar handheld laser scanner tree segmentation tree parameters Chinese fir |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2024.2439399 |
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