Highlights What are the main findings? Multi-temporal and multi-sensor machine learning in GEE achieved high classification accuracy (OA up to 96%, kappa up to 93%). RF and SVM consistently outperformed CART and the ensemble model. Sentinel-2 improved species discrimination due to higher spatial and spectral resolution. Classification separability declined over time (decreasing MCC), indicating increasing ecological complexity. Species composition shifted, with a decline in and an increase in . What are the implications of the main findings? Acacia seyalSterculia setigeraThe framework provides a scalable and cost-effective solution for long-term dryland forest monitoring. Species-level mapping improves detection of ecological change under anthropogenic and climatic pressures.Highlights What are the main findings? Multi-temporal and multi-sensor machine learning in GEE achieved high classification accuracy (OA up to 96%, kappa up to 93%). RF and SVM consistently outperformed CART and the ensemble model. Sentinel-2 improved species discrimination due to higher spatial and spectral resolution. Classification separability declined over time (decreasing MCC), indicating increasing ecological complexity. Species composition shifted, with a decline in and an increase in . What are the implications of the main findings? Acacia seyalSterculia setigeraThe framework provides a scalable and cost-effective solution for long-term dryland forest monitoring. Species-level mapping improves detection of ecological change under anthropogenic and climatic pressures.Abstract Timely and accurate mapping of tree species is essential for forest resource inventory, biodiversity conservation, and sustainable ecosystem management, particularly in dryland environments where structural heterogeneity, spectral similarity, and data scarcity complicate classification. This study develops a machine learning-based framework implemented in Google Earth Engine to map dominant tree species in the Elnour Natural Forest Reserve (ENFR), Blue Nile, Sudan, using multi-temporal and multi-sensor remote sensing data. Multi-temporal Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI imagery were integrated with vegetation index (NDVI), topographic variables derived from a digital elevation model (DEM), and field observations. The performance of Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and an unweighted ensemble approach was evaluated across four reference years (2008, 2013, 2018, and 2021). Results show that RF and SVM consistently achieved high classification performance, with overall accuracy (OA) ranging from 85.0% to 92.0% and Kappa coefficients (kappa) from 0.81 to 0.89, while maintaining stable and ecologically realistic species-area estimates. CART showed greater sensitivity to class imbalance and overestimated minor species (OA = 72.0-80.0%, kappa = 0.65-0.74), whereas the ensemble approach amplified misclassification of rare classes (OA = 78.0-84.0%, kappa = 0.70-0.78). The integration of Sentinel-2 data improved species discrimination due to enhanced spatial and spectral resolution, particularly in the red-edge region; however, algorithm selection remained the dominant factor controlling performance. Feature importance analysis identified near-infrared (NIR), shortwave infrared (SWIR), and NDVI variables as the most influential predictors. Multi-temporal analysis revealed declining class separability, reflected by decreasing MCC values, and a shift in species composition, including a decline in Acacia seyal (Delile) and an increase in Sterculia setigera Delile. These patterns indicate increasing ecological complexity driven primarily by anthropogenic pressures, with climatic variability acting as an additional stressor.