Advanced intelligent recognition algorithm for tree canopy carbon sequestration capacity based on 3D visualization: application in Larch plantations under drought stress
•Developed an intelligent 3D canopy recognition algorithm integrating LiDAR, UAV, and deep learning techniques.•Quantified drought-induced changes in LAI, light penetration, and canopy architecture in Larix kaempferi plantations.•Demonstrated how optimized leaf arrangement enhances photosynthetic ef...
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| Published in: | Ecological indicators Vol. 178; p. 113780 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.09.2025
Elsevier |
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| ISSN: | 1470-160X |
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| Abstract | •Developed an intelligent 3D canopy recognition algorithm integrating LiDAR, UAV, and deep learning techniques.•Quantified drought-induced changes in LAI, light penetration, and canopy architecture in Larix kaempferi plantations.•Demonstrated how optimized leaf arrangement enhances photosynthetic efficiency under water stress.•Achieved accurate, real-time carbon sequestration estimates using structural and physiological integration.•Provided a scalable framework for adaptive forest carbon management under diverse climate scenarios.
This study presents an advanced 3D visualization-based intelligent algorithm to assess and enhance Larix kaempferi carbon sequestration under drought stress. This approach addresses the critical impacts of drought on canopy structure and photosynthetic efficiency, significantly reducing carbon gain in larch plantations. Our research utilizes high-precision 3D canopy models combined with detailed physiological data to reveal the negative effects of drought on the cumulative leaf area index (cLAI) and maximum photosynthetic efficiency (Amax). The findings demonstrate that while drought stress reduces overall leaf area, the optimized leaf arrangement and minimized ineffective leaf area enable trees to more efficiently utilize water for photosynthesis, thereby preserving or even enhancing their carbon sequestration capacity. By leveraging 3D reconstruction technology, this study provides real-time, accurate data that significantly improves our understanding of forest ecosystem dynamics under extreme climatic conditions. The intelligent algorithm developed offers a robust tool for predicting and optimizing forest carbon sequestration, presenting new opportunities for forest management and conservation. The application of advanced 3D visualization and intelligent algorithms enhances decision-making processes for forest managers and stakeholders, promoting scientifically sound strategies for climate adaptation. This study underscores the transformative potential of cutting-edge 3D modeling technologies in advancing forest conservation and management practices. |
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| AbstractList | •Developed an intelligent 3D canopy recognition algorithm integrating LiDAR, UAV, and deep learning techniques.•Quantified drought-induced changes in LAI, light penetration, and canopy architecture in Larix kaempferi plantations.•Demonstrated how optimized leaf arrangement enhances photosynthetic efficiency under water stress.•Achieved accurate, real-time carbon sequestration estimates using structural and physiological integration.•Provided a scalable framework for adaptive forest carbon management under diverse climate scenarios.
This study presents an advanced 3D visualization-based intelligent algorithm to assess and enhance Larix kaempferi carbon sequestration under drought stress. This approach addresses the critical impacts of drought on canopy structure and photosynthetic efficiency, significantly reducing carbon gain in larch plantations. Our research utilizes high-precision 3D canopy models combined with detailed physiological data to reveal the negative effects of drought on the cumulative leaf area index (cLAI) and maximum photosynthetic efficiency (Amax). The findings demonstrate that while drought stress reduces overall leaf area, the optimized leaf arrangement and minimized ineffective leaf area enable trees to more efficiently utilize water for photosynthesis, thereby preserving or even enhancing their carbon sequestration capacity. By leveraging 3D reconstruction technology, this study provides real-time, accurate data that significantly improves our understanding of forest ecosystem dynamics under extreme climatic conditions. The intelligent algorithm developed offers a robust tool for predicting and optimizing forest carbon sequestration, presenting new opportunities for forest management and conservation. The application of advanced 3D visualization and intelligent algorithms enhances decision-making processes for forest managers and stakeholders, promoting scientifically sound strategies for climate adaptation. This study underscores the transformative potential of cutting-edge 3D modeling technologies in advancing forest conservation and management practices. This study presents an advanced 3D visualization-based intelligent algorithm to assess and enhance Larix kaempferi carbon sequestration under drought stress. This approach addresses the critical impacts of drought on canopy structure and photosynthetic efficiency, significantly reducing carbon gain in larch plantations. Our research utilizes high-precision 3D canopy models combined with detailed physiological data to reveal the negative effects of drought on the cumulative leaf area index (cLAI) and maximum photosynthetic efficiency (Amax). The findings demonstrate that while drought stress reduces overall leaf area, the optimized leaf arrangement and minimized ineffective leaf area enable trees to more efficiently utilize water for photosynthesis, thereby preserving or even enhancing their carbon sequestration capacity. By leveraging 3D reconstruction technology, this study provides real-time, accurate data that significantly improves our understanding of forest ecosystem dynamics under extreme climatic conditions. The intelligent algorithm developed offers a robust tool for predicting and optimizing forest carbon sequestration, presenting new opportunities for forest management and conservation. The application of advanced 3D visualization and intelligent algorithms enhances decision-making processes for forest managers and stakeholders, promoting scientifically sound strategies for climate adaptation. This study underscores the transformative potential of cutting-edge 3D modeling technologies in advancing forest conservation and management practices. |
| ArticleNumber | 113780 |
| Author | Zhang, Shougong Chen, Dongsheng Sun, Xiaomei Cheng, Min Wu, Chunyan |
| Author_xml | – sequence: 1 givenname: Chunyan orcidid: 0009-0005-2118-7518 surname: Wu fullname: Wu, Chunyan organization: State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, People's Republic of China – sequence: 2 givenname: Min surname: Cheng fullname: Cheng, Min organization: Institute of Land and Urban-Rural Development, Zhejiang University of Finance and Economics, Hangzhou 310018, People's Republic of China – sequence: 3 givenname: Dongsheng surname: Chen fullname: Chen, Dongsheng organization: State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, People's Republic of China – sequence: 4 givenname: Shougong surname: Zhang fullname: Zhang, Shougong organization: State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, People's Republic of China – sequence: 5 givenname: Xiaomei surname: Sun fullname: Sun, Xiaomei email: xmsun@caf.ac.cn organization: State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, People's Republic of China |
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| Keywords | Photosynthetic efficiency 3D visualization LiDAR and UAVs Point cloud data Intelligent recognition algorithm Carbon sequestration |
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| Title | Advanced intelligent recognition algorithm for tree canopy carbon sequestration capacity based on 3D visualization: application in Larch plantations under drought stress |
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