文献类型：期刊文献

英文题名：Developing compatibility biomass model based on UAV LiDAR data of Chinese fir (Cunninghamia lanceolata) in Southern China

作者：Wu, Zheyuan[1] Xie, Dongbo[1] Liu, Ziyang[1] Chen, Qiao[1] Ye, Qiaolin[2] Ye, Jinsheng[3] Wang, Qiulai[3] Liao, Xingyong[4] Wang, Yongjun[4] Sharma, Ram P.[5] Fu, Liyong[1]

第一作者：Wu, Zheyuan

通信作者：Fu, LY[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing, Peoples R China;[2]Nanjing Forestry Univ, Coll Informat Sci & Technol & Artificial Intellige, Coinnovat Ctr Sustainable Forestry Southern China, State Key Lab Tree Genet & Breeding, Nanjing, Peoples R China;[3]Guangdong Forestry Survey & Planning Inst, Guangzhou, Peoples R China;[4]Chengdu Acad Agr & Forestry Sci, Chengdu, Peoples R China;[5]Tribhuvan Univ, Inst Forestry, Kathmandu, Nepal

年份：2025

卷号：16

外文期刊名：FRONTIERS IN PLANT SCIENCE

收录：;Scopus(收录号：2-s2.0-105018956080);WOS:【SCI-EXPANDED(收录号:WOS:001592568000001)】；

基金：The author(s) declare financial support was received for the research and/or publication of this article. This study was supported by The Fundamental Research Funds for the Central Nonprofit Research Institution of CAF (Grant No. CAFYBB2022ZB002); an airborne LiDAR-based model for estimating stand volume and aboveground biomass of major tree species in Guangdong Province (Grant No. 2021KJCX001).

语种：英文

外文关键词：biomass model; dummy variable model; compatibility model; growth and development stage; Cunninghamia lanceolata

摘要：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 high-resolution 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 R-2 by 2.6%. The fitting accuracy for bark and branch biomass saw the most significant improvements, with increases in R-2 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.