Using Advanced Machine-Learning Algorithms to Estimate the Site Index of Masson Pine Plantations

Abstract

The rapid development of non-parametric machine learning methods, such as random forest (RF), extreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM), provide new methods to predict the site index (SI). However, few studies used these methods for SI modeling of Masson pine, and there is a lack of comparison of model performances. The purpose of this study was to compare the performance of different modeling approaches and the variability between models with different variables. We used 84 samples from the Guangxi Tropical Forestry Experimental Centre. Five-fold cross-validation was used and linear regression models were established to assess the relationship between the dominant height of the stand and different types of variables. The optimal model was used to predict the SI. The results show that the LightGBM model had the highest accuracy. The root mean square error (RMSE) was 3.4055 m, the relative RMSE (RMSE%) was 20.95, the mean absolute error (MAE) was 2.4189 m, and the coefficient of determination (R2) was 0.5685. The model with climatic and soil chemical variables had an RMSE of 2.7507 m, an RMSE% of 17.18, an MAE of 2.0630 m, and an R2 of 0.6720. The soil physicochemical properties were the most important factors affecting the SI, whereas the ability of the climatic factors to explain the variability in the SI in a given range was relatively low. The results indicate that the LightGBM is an excellent SI estimation method. It has higher efficiency and prediction accuracy than the other methods, and it considers the key factors determining site productivity. Adding climate and soil chemical variables to the model improves the prediction accuracy of the SI and the ability to evaluate site productivity. The proposed Masson pine SI model explains 67.2% of the SI variability. The model is suitable for the scientific management of unevenly aged Masson pine plantations.