Stem Profile Estimation of Pinus densiflora in Korea Using Machine Learning Models: Towards Precision Forestry

Abstract

The stem taper function is essential in predicting diameter outside bark (DOB) variations along the tree height, contributing to volume estimation, harvest planning, and precision forestry. Traditional taper models, such as the Kozak function, offer interpretability but often fail to capture nonlinear growth dynamics and regional variability, particularly in the upper stem segments. This study aimed to evaluate and compare the prediction accuracy of conventional and machine learning-based taper models using Pinus densiflora, a representative conifer species in Korea. Field data from two ecologically distinct regions (Gangwon and Central Korea) were used to build and test four models: the Kozak taper function, random forest, extreme gradient boosting, and an artificial neural network (ANN). Model performance was assessed using the RMSE, R2, and MAE, along with stem profile visualizations for representative trees. The results showed that the ANN consistently achieved the highest prediction accuracy across both regions, particularly at an upper crown zone relative height (RH) > 0.8, while maintaining smooth and stable taper curves. In contrast, the Kozak model tended to underestimate the diameter of the upper stem. This study demonstrates that machine learning models, particularly ANNs, can effectively enhance the taper prediction precision and serve as practical tools for data-driven forest management and the implementation of precision forestry in Korea.