Improving Forest Age Prediction Performance Using Ensemble Learning Algorithms Base on Satellite Remote Sensing Data

Abstract

Forest age plays a crucial role in assessing forest structure, carbon sinks, and other ecological functions. How to estimate forest age by satellite remote sensing data has been a hot research topic at home and abroad. This study focused on the forests of Zhejiang Province, utilizing Landsat 5 as the remote sensing data source to extract distribution information of broadleaf and coniferous forests. Then, the remote sensing feature variables were screened and the age of broadleaf forests and coniferous forests was estimated by using the multiple linear regression model MLR, the machine learning model (K-nearest neighbor method regression model KNN, support vector regression model SVR), and the ensemble learning model (adaptive boosting model AdaBoost, random forest model RF, and eXtreme gradient boosting XGBoost). After analyzing the forest age estimation results from different models, the best-performing model was selected to create a spatial distribution map of forest age in Zhejiang Province. The study shows that the ensemble machine learning model can better realize the remote sensing inversion of forest age. The optimal model for broadleaf forests is the XGBoost model, with a prediction accuracy R of 0.836 and a root mean square error (RMSE) of 5.823a. And the top model for coniferous forests is the RF model, with a prediction accuracy R of 0.895 and an RMSE of 5.076a. Compared with the MLR model, the best broadleaf and coniferous forest age inversion models improved the accuracy R by 45.57% and 58.21%, and reduced the RMSE by 52.67% and 47.54%, respectively. Additionally, the analysis revealed that 50% of the remote sensing feature variables involved in forest age inversion are texture features, indicating that texture is an important feature variable for the construction of forest age remote sensing inversion models.

Keywords: Forest age, Satellite remote sensing, machine learning, Ensemble learning