Developing a generalized nonlinear mixed-effects biomass model at stand-level under different age conditions for Chinese fir based on LiDAR and ground survey data in southern China

Xinsheng Zhu¹Tianbao Huang¹Ziyang Liu¹Lang Bai¹Yongfeng Yang²Jinsheng Ye³Qiulai Wang³Ram P. Sharma⁴Liyong Fu^5*

• ¹Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, P. R. of China, Beijing, China
• ²Hanan Sanya Urban Ecosystem Observation and Research, Staton, Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China, Beijing, China
• ³Guangdong Forestry Survey and Planning Institute, Guangzhou, Guangdong, China
• ⁴Institute of Forestry, Tribhuvan University, Kathmandu, Nepal
• ⁵Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Haidian, China

Chinese fir (Cunninghamia lanceolata) is a key afforestation and timber species in southern China, and accurate estimation of its stand biomass is essential for forest resource assessment, ecological industry development, and ecosystem management.However, conventional biomass prediction methods often suffer from limited accuracy and efficiency, necessitating the development of more robust modeling approaches.This study utilized data from 154 forest stands in Guangdong Province to develop biomass regression models incorporating random effects and dummy variables, based on airborne LiDAR-derived metrics. Among the 41 highly correlated LiDAR variables assessed, only two-the 5% cumulative height percentile and the leaf area index-were retained in the final model. The results demonstrated that the logistic mixed-effects model was most effective for estimating leaf biomass, while the empirical mixed-effects model was better suited for other biomass components. Nonlinear models outperformed linear models, with the nonlinear mixed-effects model incorporating stand age as a random effect achieving the highest predictive accuracy. Furthermore, machine learning techniques further enhanced model performance (R²=0.855~0.939). Validation with independent test samples confirmed the reliability and robustness of the nonlinear mixed-effects model. This study highlights the effectiveness of airborne LiDAR data in enabling efficient and precise stand biomass estimation and underscores the significant influence of stand developmental stages on biomass modeling. The findings provide a rigorous and scalable methodological framework for large-scale artificial forest biomass estimation, offering valuable insights for forest resource monitoring, ecological industry development, and ecosystem management strategies.