Stem Volume Prediction of Chamaecyparis obtusa in South Korea Using Machine Learning and Field-Measured Tree Variables

Accurate estimation of individual tree stem volume is essential for forest resource assessment and the implementation of sustainable forest management. In South Korea, traditional regression models based on non-destructive and easily measurable field variables such as diameter at breast height (DBH) and total height (TH) have been widely used to construct stem volume tables. However, these models often fail to adequately capture the nonlinear taper of tree stems. In this study, we evaluated and compared the predictive performance of traditional regression models and two machine learning algorithms—Random Forest (RF) and Extreme Gradient Boosting (XGBoost)—using stem profile data from 1000 destructively sampled Chamaecyparis obtusa trees collected across 318 sites nationwide. To ensure compatibility with existing national stem volume tables, all models used only DBH and TH as input variables. The results showed that all three models achieved high predictive accuracy (R2 > 0.997), with XGBoost yielding the lowest RMSE (0.0164 m3) and MAE (0.0126 m3). Although differences in performance among the models were marginal, the machine learning approaches demonstrated flexible and generalizable alternatives to conventional models, providing a practical foundation for large-scale forest inventory and the advancement of digital forest management systems.