{"ID":2867647,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18228","arxiv_id":"2509.18228","title":"Forest tree species classification and entropy-derived uncertainty mapping using extreme gradient boosting and Sentinel-1/2 satellite data","abstract":"We present a new 10-meter 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. 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 (Spearman's rho = 0.94).","short_abstract":"We present a new 10-meter 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 Inventor...","url_abs":"https://arxiv.org/abs/2509.18228","url_pdf":"https://arxiv.org/pdf/2509.18228v2","authors":"[\"Abdulhakim M. Abdi\",\"Fan Wang\"]","published":"2025-09-22T12:01:49Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"stat.ML\"]","methods":"[]","has_code":false}