Improving Chinese Fir Plantations DBH Inversion Accuracy Using Ensemble Learning Models Base on UAV-LiDAR
Diameter at breast height (DBH) is a fundamental measurement indicator in forest resource surveys. This study explores the use of uncrewed aerial vehicle light detection and ranging (UAV-LiDAR) for individual tree DBH inversion in Chinese fir (<italic>Cunninghamia lanceolata</italic>) pl...
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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/10964709/ |
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| author | Jiuen Xu Yinyin Zhao Xuejian Li Lujin Lv Jiacong Yu Meixuan Song Lei Huang Fangjie Mao Huaqiang Du |
| author_facet | Jiuen Xu Yinyin Zhao Xuejian Li Lujin Lv Jiacong Yu Meixuan Song Lei Huang Fangjie Mao Huaqiang Du |
| author_sort | Jiuen Xu |
| collection | DOAJ |
| description | Diameter at breast height (DBH) is a fundamental measurement indicator in forest resource surveys. This study explores the use of uncrewed aerial vehicle light detection and ranging (UAV-LiDAR) for individual tree DBH inversion in Chinese fir (<italic>Cunninghamia lanceolata</italic>) plantations with complex terrain and rich understory vegetation, and compares the results with those from Backpack-LiDAR. First, individual tree segmentation was performed on the UAV-LiDAR point cloud data from the study area, and individual tree point cloud feature variables were extracted by applying different height thresholds. Then, three types of models—statistical model multiple linear regression (MLR), traditional machine learning models including K-nearest neighbor regression and support vector regression, and ensemble learning models including random forest, extreme gradient boosting, and categorical boosting (CatBoost)—were employed for DBH inversion. The results show that: 1) Using data above a 5-m height threshold effectively reduces interference from understory vegetation; 2) Key feature variables, such as canopy volume (V), tree height (Hmax), the interquartile range of cumulative height percentiles (AIHiq), and canopy area (S), significantly affect DBH inversion, with V contributing 25% to the feature importance; 3) Ensemble learning models, particularly CatBoost, outperform the other models, achieving an <italic>R</italic><sup>2</sup> of 0.81% —14.1% higher than MLR; 4) DBH inversion closely matches field observed data, and UAV-LiDAR performs better than Backpack-LiDAR in complex forest environments. This study provides an efficient and cost-effective approach to forest resource monitoring. |
| format | Article |
| id | doaj-art-e0c4e48868b44292bed947d086828af2 |
| institution | Kabale University |
| 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-e0c4e48868b44292bed947d086828af22025-08-20T03:48:43ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118108461086310.1109/JSTARS.2025.356070410964709Improving Chinese Fir Plantations DBH Inversion Accuracy Using Ensemble Learning Models Base on UAV-LiDARJiuen Xu0Yinyin Zhao1Xuejian Li2https://orcid.org/0000-0002-7553-2576Lujin Lv3Jiacong Yu4Meixuan Song5Lei Huang6Fangjie Mao7https://orcid.org/0000-0003-2005-3452Huaqiang Du8https://orcid.org/0000-0002-6765-2279State Key Laboratory of Subtropical Silviculture, the Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, and the School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, ChinaState Key Laboratory of Subtropical Silviculture, the Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, and the School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, ChinaState Key Laboratory of Subtropical Silviculture, the Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, and the School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, ChinaState Key Laboratory of Subtropical Silviculture, the Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, and the School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, ChinaState Key Laboratory of Subtropical Silviculture, the Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, and the School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, ChinaState Key Laboratory of Subtropical Silviculture, the Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, and the School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, ChinaState Key Laboratory of Subtropical Silviculture, the Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, and the School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, ChinaState Key Laboratory of Subtropical Silviculture, the Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, and the School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, ChinaState Key Laboratory of Subtropical Silviculture, the Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, and the School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, ChinaDiameter at breast height (DBH) is a fundamental measurement indicator in forest resource surveys. This study explores the use of uncrewed aerial vehicle light detection and ranging (UAV-LiDAR) for individual tree DBH inversion in Chinese fir (<italic>Cunninghamia lanceolata</italic>) plantations with complex terrain and rich understory vegetation, and compares the results with those from Backpack-LiDAR. First, individual tree segmentation was performed on the UAV-LiDAR point cloud data from the study area, and individual tree point cloud feature variables were extracted by applying different height thresholds. Then, three types of models—statistical model multiple linear regression (MLR), traditional machine learning models including K-nearest neighbor regression and support vector regression, and ensemble learning models including random forest, extreme gradient boosting, and categorical boosting (CatBoost)—were employed for DBH inversion. The results show that: 1) Using data above a 5-m height threshold effectively reduces interference from understory vegetation; 2) Key feature variables, such as canopy volume (V), tree height (Hmax), the interquartile range of cumulative height percentiles (AIHiq), and canopy area (S), significantly affect DBH inversion, with V contributing 25% to the feature importance; 3) Ensemble learning models, particularly CatBoost, outperform the other models, achieving an <italic>R</italic><sup>2</sup> of 0.81% —14.1% higher than MLR; 4) DBH inversion closely matches field observed data, and UAV-LiDAR performs better than Backpack-LiDAR in complex forest environments. This study provides an efficient and cost-effective approach to forest resource monitoring.https://ieeexplore.ieee.org/document/10964709/Categorical boosting (Catboost)Chinese firdiameter at breast height (DBH)ensemble learningunmanned aerial vehicle light detection and ranging (UAV-LiDAR) |
| spellingShingle | Jiuen Xu Yinyin Zhao Xuejian Li Lujin Lv Jiacong Yu Meixuan Song Lei Huang Fangjie Mao Huaqiang Du Improving Chinese Fir Plantations DBH Inversion Accuracy Using Ensemble Learning Models Base on UAV-LiDAR IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Categorical boosting (Catboost) Chinese fir diameter at breast height (DBH) ensemble learning unmanned aerial vehicle light detection and ranging (UAV-LiDAR) |
| title | Improving Chinese Fir Plantations DBH Inversion Accuracy Using Ensemble Learning Models Base on UAV-LiDAR |
| title_full | Improving Chinese Fir Plantations DBH Inversion Accuracy Using Ensemble Learning Models Base on UAV-LiDAR |
| title_fullStr | Improving Chinese Fir Plantations DBH Inversion Accuracy Using Ensemble Learning Models Base on UAV-LiDAR |
| title_full_unstemmed | Improving Chinese Fir Plantations DBH Inversion Accuracy Using Ensemble Learning Models Base on UAV-LiDAR |
| title_short | Improving Chinese Fir Plantations DBH Inversion Accuracy Using Ensemble Learning Models Base on UAV-LiDAR |
| title_sort | improving chinese fir plantations dbh inversion accuracy using ensemble learning models base on uav lidar |
| topic | Categorical boosting (Catboost) Chinese fir diameter at breast height (DBH) ensemble learning unmanned aerial vehicle light detection and ranging (UAV-LiDAR) |
| url | https://ieeexplore.ieee.org/document/10964709/ |
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