Predicting individual tree diameter at breast height for genetically diverse Catalpa bungei using nonlinear mixed-effects models and UAV LiDAR data
This research introduces a new framework for integrating genotype variability into DBH prediction models and offers valuable insights for future LiDAR-based studies in genetically heterogeneous plantations. The findings provide technical support for forest management and ecosystem monitoring, as well as a methodological foundation for predicting tree growth under varying site and genetic conditions.
Authors
- Yuheng Shun
- Zetian Zheng (ORCID: https://orcid.org/0000-0002-0304-571X)
- Wenjun Ma (ORCID: https://orcid.org/0000-0002-9266-4802)
- Guangshuang Duan (ORCID: https://orcid.org/0009-0007-4875-3123)
- Liyong Fu (ORCID: https://orcid.org/0000-0002-5794-9458)
- Chuangye Wu
- Xiuwen Fu (ORCID: https://orcid.org/0000-0001-5611-3504)
- Miaomiao Zhang (ORCID: https://orcid.org/0000-0002-9025-5458)
- Pan Li (ORCID: https://orcid.org/0000-0002-9407-7740)
- Yang Zhang (ORCID: https://orcid.org/0000-0001-8054-1429)
- Qingqing Wang
- Qiao Chen (ORCID: https://orcid.org/0000-0002-6458-8742)
Institutions
- Xinyang Normal University (CN)
- Jiaozuo University (CN)
- Institute of Forest Resource Information Techniques (CN)
- Research Institute of Forestry (CN)
- State Forestry and Grassland Administration (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-12-19
- DOI
- https://doi.org/10.3389/fpls.2025.1716546
- Citations
- 1
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.80