dnbr

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paulinelemenkova / seferihisar-menderes-burn-severity

Reproducible Python & GMT/PyGMT workflow for mapping burn severity of the June 2025 Seferihisar–Menderes (Orhanli) forest fire from Landsat 8/9, using an explainable Random Forest (SHAP).

machine-learning random-forest landsat remote-sensing wildfire explainable-ai shap pygmt burn-severity dnbr

Updated Aug 22, 2026
Python

AamnaNaveed / wildfire-recovery-geospatial-modeling

Star

Spatially explicit modelling of post-fire vegetation recovery across 15 California wildfires (2018–2022) using Sentinel-2, terrain, and climate.

machine-learning reproducible-research california remote-sensing earth-observation wildfire sentinel-2 burn-severity spatial-cross-validation dnbr

Updated Oct 2, 2026
Python

seemonkumawat1234-arch / savanna-burn-mapper

Star

Burnt-area and burn-severity mapping from Sentinel-2 using dNBR, with severity breakpoints tuned for northern Australian savanna. Validated against NAFI fire-scar mapping. CPU only, 41 tests.

python geospatial gis remote-sensing rasterio earth-observation sentinel-2 savanna northern-australia fire-mapping dnbr burnt-area

Updated Sep 24, 2026
Python

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