A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization
Accurate estimation of individual tree above-ground biomass (AGB) is crucial for regional forest AGB measurement. In this study, 64 individual trees of Pinus kesiya var. langbianensis, exhibiting a range of diameters, were felled from natural forests in mountainous regions to develop region-specific...
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Elsevier
2025-06-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225002596 |
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| author | Zhibo Yu Yong Wu Ziyu Zhang Chi Lu Hong Wang Zhi Liu Xiaoli Zhang Lei Bao Jie Pan Guanglong Ou Hongbin Luo |
| author_facet | Zhibo Yu Yong Wu Ziyu Zhang Chi Lu Hong Wang Zhi Liu Xiaoli Zhang Lei Bao Jie Pan Guanglong Ou Hongbin Luo |
| author_sort | Zhibo Yu |
| collection | DOAJ |
| description | Accurate estimation of individual tree above-ground biomass (AGB) is crucial for regional forest AGB measurement. In this study, 64 individual trees of Pinus kesiya var. langbianensis, exhibiting a range of diameters, were felled from natural forests in mountainous regions to develop region-specific allometric equations for AGB. To enhance AGB estimation accuracy, we integrated unmanned aerial vehicle laser scanning (ULS) and backpack laser scanning (BLS) point clouds using the Iterative Closest Point (ICP) algorithm for precise registration and fusion under varying slope conditions, enabling 3D tree reconstruction. Furthermore, a height-filtered segmentation strategy was introduced to further enhance registration accuracy by aligning ULS points above 2 m with BLS data. The results showed that: 1) Unnormalized point clouds exhibited better registration performance than normalized ones, indicating that slope has a significant impact on registration accuracy. 2) The segmentation-driven registration method performed best in integrating tree trunks and crowns, with the lowest registration error (RMSE = 0.2581 m) observed in the mid-slope areas (15-25°). In contrast, higher registration errors were found in steep (>25°) and gentle (0-15°) slopes, with RMSE of 0.2976 m and 0.2814 m, respectively. 3) The AGB allometric equation derived from the felled trees demonstrated high accuracy (R2 = 0.9879, RMSE = 22.9243 kg). 4) The individual tree AGB estimation based on breast height (DBH) and height (H) extracted from the fused point clouds had R2 values ranging from 0.8721 to 0.9730, with RMSE between 20.0242 kg to 48.6254 kg. This framework provides valuable insights for accurate forest resource surveys and management in complex terrain and highly heterogeneous regions. |
| format | Article |
| id | doaj-art-6fdae08bde324cb7a42dea5f7e961f6d |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-6fdae08bde324cb7a42dea5f7e961f6d2025-08-20T03:19:56ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-06-0114010461210.1016/j.jag.2025.104612A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration OptimizationZhibo Yu0Yong Wu1Ziyu Zhang2Chi Lu3Hong Wang4Zhi Liu5Xiaoli Zhang6Lei Bao7Jie Pan8Guanglong Ou9Hongbin Luo10Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, ChinaKey Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, ChinaKey Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, ChinaKey Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, ChinaKey Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, ChinaKey Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, ChinaKey Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, ChinaKey Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, ChinaKey Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, ChinaKey Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, China; Corresponding authors at: Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, ChinaKey Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China; Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, China; Corresponding authors at: Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, ChinaAccurate estimation of individual tree above-ground biomass (AGB) is crucial for regional forest AGB measurement. In this study, 64 individual trees of Pinus kesiya var. langbianensis, exhibiting a range of diameters, were felled from natural forests in mountainous regions to develop region-specific allometric equations for AGB. To enhance AGB estimation accuracy, we integrated unmanned aerial vehicle laser scanning (ULS) and backpack laser scanning (BLS) point clouds using the Iterative Closest Point (ICP) algorithm for precise registration and fusion under varying slope conditions, enabling 3D tree reconstruction. Furthermore, a height-filtered segmentation strategy was introduced to further enhance registration accuracy by aligning ULS points above 2 m with BLS data. The results showed that: 1) Unnormalized point clouds exhibited better registration performance than normalized ones, indicating that slope has a significant impact on registration accuracy. 2) The segmentation-driven registration method performed best in integrating tree trunks and crowns, with the lowest registration error (RMSE = 0.2581 m) observed in the mid-slope areas (15-25°). In contrast, higher registration errors were found in steep (>25°) and gentle (0-15°) slopes, with RMSE of 0.2976 m and 0.2814 m, respectively. 3) The AGB allometric equation derived from the felled trees demonstrated high accuracy (R2 = 0.9879, RMSE = 22.9243 kg). 4) The individual tree AGB estimation based on breast height (DBH) and height (H) extracted from the fused point clouds had R2 values ranging from 0.8721 to 0.9730, with RMSE between 20.0242 kg to 48.6254 kg. This framework provides valuable insights for accurate forest resource surveys and management in complex terrain and highly heterogeneous regions.http://www.sciencedirect.com/science/article/pii/S1569843225002596Point cloud registrationIndividual tree AGBULSBLSTerrain |
| spellingShingle | Zhibo Yu Yong Wu Ziyu Zhang Chi Lu Hong Wang Zhi Liu Xiaoli Zhang Lei Bao Jie Pan Guanglong Ou Hongbin Luo A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization International Journal of Applied Earth Observations and Geoinformation Point cloud registration Individual tree AGB ULS BLS Terrain |
| title | A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization |
| title_full | A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization |
| title_fullStr | A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization |
| title_full_unstemmed | A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization |
| title_short | A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization |
| title_sort | precise estimation framework for individual tree agb of pinus kesiya var langbianensis utilizing point cloud registration optimization |
| topic | Point cloud registration Individual tree AGB ULS BLS Terrain |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225002596 |
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