# Estimation of individual tree aboveground biomass of genetically diverse Catalpa bungei based on nonlinear mixed-effects models and UAV LiDAR data

## TL;DR

This study uses UAV LiDAR and a statistical model to accurately estimate the biomass of genetically diverse Catalpa bungei trees, improving carbon accounting and forestry practices.

## Contribution

A novel nonlinear mixed-effects model integrating UAV LiDAR and genotype effects for precise individual tree biomass estimation.

## Key findings

- The NLME model outperformed traditional models with high accuracy (R²=0.7916, RMSE=3.7095).
- Incorporating genotype effects reduced TRE by 23.29% compared to a basic power function model.
- Calibration with just two trees per genotype maintained high accuracy while reducing field effort.

## Abstract

Accurate estimation of individual tree aboveground biomass (AGB) is essential for tree species selection, carbon accounting, and precision forestry. Unmanned aerial vehicle (UAV) LiDAR provides rapid access to detailed tree structural information, offering a promising tool for high-frequency biomass assessment.

In this study, a nonlinear mixed-effects (NLME) model integrating UAV LiDAR and field measurements was developed to quantify the influence of genetic heterogeneity and environmental factors on AGB estimation of Catalpa bungei. Data from 2,941 trees across 79 genotypes were collected in Henan Province, including LiDAR-derived tree height (LH), LiDAR-derived crown diameter (LCD), and AGB. By incorporating genotype as a random effect and planting density as a dummy variable, the NLME model significantly outperformed traditional dummy-variable models.

Genotype effects explained significant AGB variation, achieving high accuracy (R²=0.7916, RMSE = 3.7095) and reducing TRE by 23.29% compared to the basic power function model. Leave-one-genotype-out cross-validation confirmed robustness. Calibration with the four largest trees yielded the best performance (TRE = 13.09%), while a simplified scheme using only two trees per genotype maintained high accuracy (TRE = 13.24%), markedly reducing field effort.

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.