burn-severity

Remote sensing and spatial analysis tools for Google Earth Engine

Evaluate Wildfire Environmental Impact and Assess Burn Severity Consequences using Cloud Based Geoprocessing via Earth Engine Streamlit App.

AI-powered wildfire damage detection using IBM/NASA Prithivi-EO-2.0 foundation models and hybrid spectral analysis for high-precision burn mapping.

Kumbara: Inventory of Forest and Land Fires in Central Kalimantan Province 2014 - 2024 An application to visualise the severity of land fires using the NBR and dNBR methods and the potential level of land fires using Fire Information for Resource Management System (FIRMS) hotspot points in Central Kalimantan Province, Indonesia.

Open-data project that maps burn severity from the January 2025 Southern California wildfires using Sentinel-2 imagery and dNBR/RBR classification, with an interactive map overlay on OpenStreetMap.

An interactive Earth Observation Web App built with Streamlit, Google Earth Engine, and Sentinel-2 to assess wildfire severity and land cover impact.

Remote-sensing experiments on wildfire burn-severity mapping, cross-event transfer, and pre-fire prediction using Landsat, MTBS, and CanLaBS.

Code for calculating IntFire

Data and supplementary material for "Influence of Burn Severity on Avifaunal Communities in Southern Tropical Dry Deciduous Forest, Western Ghats" (Ecology and Evolution, 2026).

Ranks burned watersheds by post-fire debris-flow susceptibility from free public data — for fires that never get a formal USGS or state assessment. Auto-acquired 3DEP terrain + dNBR, D8 delineation, burn × slope × area scoring. Validated on Thomas Fire → Montecito. A within-fire relative ranking, never a prediction.