Comparison of random forest and extreme gradient boosting algorithms in land cover classification in Van Yen district, Yen Bai province, Vietnam

Land cover classification using remote sensing data plays a crucial role in resource management and environmental monitoring.This study compares the performance of Random Forest (RF) and Extreme Gradient Boosting (XGBoost) in land cover classification in Van Yen District, Yen Bai Province, Vietnam.The input data includes Sentinel-1 radar images, Sentinel-2 optical images, and a total of 7,214 sample points collected for model development using the Google Colab platform.The results indicate that both RF and XGBoost achieve high performance, with overall accuracy (OA) ranging from 94.8% to 96.3% and Kappa coefficients between 0.936 and 0.955.Notably, RF demonstrates greater stability and higher accuracy than XGBoost in both scenarios: using Sentinel-2 alone and combining Sentinel-2 with Sentinel-1.This study provides a scientific basis for selecting appropriate algorithms and data to improve land cover classification efficiency in the region.