The Effect of Soil and Topography Factors on Larix gmelinii var. Principis-rupprechtii Forest Mortality and Capability of Decision Tree Binning Method and Generalized Linear Models in Predicting Tree Mortality

Understanding the factors influencing individual tree mortality is essential for sustainable forest management, particularly for Prince Rupprech’s larch (Larix gmelinii var. Principis-rupprechtii) in North China’s natural forests. This study focused on 20 sample plots (20 × 20 m each) established in Shanxi Province, North China. This study compared three individual tree mortality models—Generalized Linear Model (GLM), Linear Discriminant Analysis (LDA), and Bayesian Generalized Linear Model (Bayesian GLM)—finding that both GLM and Bayesian GLM achieved approximately 0.87 validation accuracy on the test dataset. Due to its simplicity, GLM was selected as the final model. Building on the GLM model, six binning methods were applied to categorize diameter at breast height (DBH): equal frequency binning, equal width binning, cluster-based binning, quantile binning, Chi-square binning, and decision tree binning. Among these, the decision tree binning method achieved the highest performance, with an accuracy of 90.12% and an F1 score of 90.06%, indicating its effectiveness in capturing size-dependent mortality patterns. This approach provides valuable insights into factors affecting mortality and offers practical guidance for managing Larix gmelinii var. Principis-rupprechtii forests in temperate regions.