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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2509.18228 |
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| _version_ | 1866914176795934720 |
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| author | Abdi, Abdulhakim M. Wang, Fan |
| author_facet | Abdi, Abdulhakim M. Wang, Fan |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forest tree species classification and entropy-derived uncertainty mapping using extreme gradient boosting and Sentinel-1/2 satellite data Abdi, Abdulhakim M. Wang, Fan Quantitative Methods Machine Learning 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). |
| title | Forest tree species classification and entropy-derived uncertainty mapping using extreme gradient boosting and Sentinel-1/2 satellite data |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2509.18228 |