Estimation of individual tree aboveground biomass of genetically diverse Catalpa bungei based on nonlinear mixed-effects models and UAV LiDAR data
These results highlight the superiority of NLME AGB models over linear approaches and demonstrate that accounting for genotype effects is critical for reliable biomass estimation. The proposed framework provides an efficient and cost-effective solution for biomass monitoring, tree breeding, carbon sink assessment, and precision forestry.
Authors
- M. B. Zhang
- Yuheng Shun
- Chuangye Wu
- Qingqing Wang (ORCID: https://orcid.org/0000-0002-9731-2064)
- Qiao Chen (ORCID: https://orcid.org/0000-0002-6458-8742)
- Xu Fu (ORCID: https://orcid.org/0009-0009-8200-3072)
- Guangshuang Duan
- Pan Li
- Ziyan Zheng
- Yang Zhang
- Wenjun Ma
- Liyong Fu
Institutions
- Xinyang Normal University (CN)
- Institute of Forestry (RS)
- Institute of Forest Resource Information Techniques (CN)
- Chinese Academy of Forestry (CN)
- Research Institute of Forestry (CN)
- State Forestry and Grassland Administration (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2026-03-09
- DOI
- https://doi.org/10.3389/fpls.2026.1759637
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chinese Academy of Forestry