Forest tree species classification and entropy-derived uncertainty mapping using extreme gradient boosting and Sentinel-1/2 data

• URL: http://arxiv.org/abs/2509.18228v1
• Date: Mon, 22 Sep 2025 12:01:49 GMT
• Title: Forest tree species classification and entropy-derived uncertainty mapping using extreme gradient boosting and Sentinel-1/2 data
• Authors: Abdulhakim M. Abdi, Fan Wang,
• Abstract summary: We present a new 10-meter map of tree species in Swedish forests accompanied by pixel-level uncertainty estimates.<n>The tree species classification is based on metrics derived from Sentinel-1 and Sentinel-2 satellite data, combined with field observations from the Swedish National Forest Inventory.
• Score: 6.334209619488757
• License: http://creativecommons.org/licenses/by/4.0/
• 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 (r = 0.96).

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