Predicting individual tree diameter at breast height for genetically diverse Catalpa bungei using nonlinear mixed-effects models and UAV LiDAR data.

Abstract

Introduction 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. Methods 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 improved model generalizability by using LiDAR-derived tree height (LH) and LiDAR-derived crown diameter (LCD) as core predictors. Multiple sampling strategies were also evaluated to assess their impact on model performance. Results The results showed that, considering genotype effects, the proposed NLME model outperformed both traditional regression models and dummy-variable models (R 2 = 0.8624, RMSE = 1.1330, TRE = 3.9555), demonstrating the important role of genotype differences in improving model accuracy. Random sampling further improved prediction accuracy while effectively reducing measurement costs. Discussion 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.

Plant phenotyping relevance

UAV LiDARと非線形混合効果モデルを用いて個体樹木のDBHを推定する手法を開発・比較検証しており、植物形質取得が研究の中心である。

Code and data availability

The supplied blocks describe UAV LiDAR data collection and NLME modeling for 2,899 Catalpa bungei trees, but contain no data availability statement, public repository deposit, or author code URL. The only URL present is the article DOI itself. No paper-specific public dataset, point cloud, image, or code asset is shown

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