Predicting Individual Tree Diameter at Breast Height (DBH) for Genetically Diverse Catalpa bungei Using Nonlinear Mixed-Effects Models and UAV LiDAR Data

Yang Zhang^1,2Miaomiao Zhang^3,4Qiao Chen^1,2*Liyong Fu^1,2Wenjun Ma^3,4Guangshuang Duan⁵Xinru Fu^1,2Ziyan Zheng^1,2Chuangye Wu⁶Qingqing Wang⁶Yuheng Shun⁶Pan Li⁶

• ¹Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Haidian, China
• ²Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Beijing, China
• ³Chinese Academy of Forestry Research Institute of Forestry, Beijing, China
• ⁴State Key Laboratoryof Tree Genetics and Breeding, Key Laboratory of TreeBreeding and Cultivation of State Forestry Administration, Beijing, China
• ⁵Xinyang Normal University, Xinyang, China
• ⁶Wen County Institute of Forestry Science, Jiaozuo, China

Diameter at breast height (DBH) is a key parameter for assessing tree growth, carbon storage, and ecological functions. Traditional ground surveys are inefficient, labor-intensive, and terrain-limited, making them unsuitable for large-scale monitoring. Airborne LiDAR, as an advanced remote sensing tool, provides an efficient and non-destructive method for DBH estimation. However, most existing LiDAR-based models overlook the influence of genotype differences, limiting prediction accuracy. In this study, we used data from 2,899 Catalpa bungei trees of different genotypes to develop a nonlinear mixed-effects (NLME) model that incorporates genotype as a random effect. This approach significantly improved DBH estimation accuracy and enhanced model generalizability by using LiDAR-derived tree height(LH) and LiDAR-derived crown diameter(LCD) as core predictors. The results showed that, considering genotype effects, the proposed NLME model outperformed both traditional regression models and dummy-variable models (R² = 0.8624, RMSE = 1.1330, TRE = 3.9555), demonstrating the important role of genotype differences in improving model accuracy. Additionally, the study systematically evaluated the impact of various sampling strategies on model performance. Random sampling, in particular, improved prediction accuracy while effectively reducing measurement costs, showcasing strong practical potential. 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 not only technical support for forest 1 Yang Zhang et al. Predicting DBH with LiDAR management and ecosystem monitoring but also a methodological foundation for predicting tree growth under varying site and genetic conditions.