Incorporated neighborhood and environmental effects to model individual-tree height using random forest regression

Abstract

ABSTRACT In forest resource inventory, tree height is often estimated by easily measurable diameter from height-diameter model. In this study, we tried to use random forest (RF), an important machine learning method, to model individual-tree height. Results showed that the optimized RF model had better fitting and prediction accuracy (R 2 = 0.8146 and RMSE = 2.2527 m). In terms of relative importance, diameter at breast height (DBH) was the most important factor, followed by neighborhood-related variables and other variables related to environmental conditions. Further, tree height was generally positively affected by DBH, mean diameter of neighbors, DBH dominance, number of neighbors, and mean annual precipitation, but negatively affected by elevation. The results indicated that the RF-based height model was statistically reliable and highly accurate, and it had strong interpretability with ecological significance. Our study will provide a new perspective for the application of machine learning algorithms to forest dynamic modeling.