Tropical natural forests play a crucial role in regulating the climate and maintaining global ecosystem functions. However, they face significant challenges due to human activities and climate change. Accurate classification of these forests can help reveal their spatial distribution patterns and support conservation efforts. This study employed four machine learning algorithms—random forest (RF), support vector machine (SVM), Logistic Regression (LR), and Extreme Gradient Boosting (XGBoost)—to classify tropical rainforests, tropical monsoon rainforests, tropical coniferous forests, broadleaf evergreen forests, and mangrove forests on Hainan Island using optical and synthetic aperture radar (SAR) multi-source remote sensing data. Among these, the XGBoost model achieved the best performance, with an overall accuracy of 0.89 and a Kappa coefficient of 0.82. Elevation and red-edge spectral bands were identified as the most important features for classification. Spatial distribution analysis revealed distinct patterns, such as mangrove forests occurring at the lowest elevations and tropical rainforests occupying middle and low elevations. The integration of optical and SAR data significantly enhanced classification accuracy and boundary recognition compared to using optical data alone. These findings underscore the effectiveness of machine learning and multi-source data for tropical forest classification, providing a valuable reference for ecological monitoring and sustainable management.
Xie et al. (Sun,) studied this question.