ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1520666
This article is part of the Research TopicAccurate Measurement and Dynamic Monitoring of Forest ParametersView all 8 articles
Developing Compatibility Biomass Model Based on UAV LiDAR Data of Chinese fir (Cunninghamia lanceolata) in Southern China
Provisionally accepted- 1Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Haidian, China
- 2Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu Province, China
- 3Guangdong Forestry Survey and Planning Institute, Guangzhou, Guangdong, China
- 4Chengdu Agriculture And Forestry Academy Of Sciences, Chengdu, Sichuan Province, China
- 5Institute of Forestry, Tribhuvan University, Pokhara, Nepal
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Chinese fir (Cunninghamia lanceolata) is a key native tree species in southern China. Accurate estimation of above-ground biomass and its distribution is essential for the sustainable use of Chinese fir forests. UAV-based high-density point clouds and highresolution spectral data provide critical remote sensing for detailed 3D tree structure analysis. This study aimed to explore the aboveground biomass allocation characteristics across the different growth stages of Chinese fir and to develop accurate biomass models. Measurements of 20,836 Chinese fir trees were used for the purpose.Through the comparative analysis of four basic models, the Power Function model was identified as the optimal one, particularly excelling in fitting the accuracy for stem and bark biomass. To further enhance the model's fitting performance, age groups were introduced into the dummy model, categorizing the Chinese fir forests into the five distinct growth stages. Results showed age groups used as dummy variables led to an average increase in R² by 2.6%. The fitting accuracy for bark and branch biomass saw the most significant improvements, with increases in R² by 4.2% and 3.1%. To address the inconsistency between the sum of individual biomass components and total biomass, we employed a seemingly unrelated regression (SUR) model. Even though fitting accuracy for individual tree components decreased by an average of 2.5%, from a practical perspective SUR model would be more suitable for understanding the interrelationships between different components. These findings offer robust support for accurately estimating the aboveground biomass in Chinese fir forests across different growth stages.
Keywords: Biomass model, Dummy variable model, Compatibility model, Growth and development stage, Cunninghamia lanceolata
Received: 31 Oct 2024; Accepted: 09 Jul 2025.
Copyright: © 2025 Wu, Xie, Liu, Chen, Ye, Ye, Wang, Liao, Wang, Sharma and Fu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Liyong Fu, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Haidian, China
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