Forest tree species classification and entropy-derived uncertainty mapping using extreme gradient boosting and Sentinel-1/2 data - Abdi, Abdulhakim M. ; 0000-0001-6486-8747; Wang, Fan (arXiv.org, 2025)
We present a wall-to-wall map of dominant tree species in Swedish forests accompanied by pixel-level uncertainty estimates. The tree species classification is based on spatiotemporal metrics derived from Sentinel-1 and Sentinel-2 satellite data, combined with field observations from the Swedish National Forest Inventory and auxiliary data on geomorphometry and canopy height. We apply an extreme gradient boosting model with Bayesian optimization to relate field observations to satellite-derived features and generate the final species map. Classification uncertainty is quantified using Shannon’s entropy of the predicted class probabilities, which provide a spatially explicit measure of model confidence. The final model achieved an overall accuracy of 85% (F1 score = 0.82, Matthews correlation coefficient = 0.81), and mapped species distributions showed strong agreement with official forest statistics (r = 0.96). Variable importance analysis revealed that the most influential predictors were optical bands from Sentinel-2, particularly those acquired in spring and summer. This study provides scalable, interpretable, and policy-relevant method for tree species mapping with integrated uncertainty that are well-suited to meet emerging legislative and environmental goals.