Rapid Burn Severity Mapping for Post-Fire Flood and Landslide Risk Awareness

Wildland fires transform ecosystems leaving post-fire landscapes vulnerable to hazards like landslides, flash floods, and debris flows. To assess these risks, meteorologists depend on optical satellite imagery to delineate burned area perimeters and estimate the ecosystem burn severity. However, existing workflows for accessing and visualizing analysis-ready satellite data are not optimized for ongoing fire events.

Optical remote sensing depends on clear-sky imagery where reflected sunlight is not obstructed by cloud or smoke cover. This can be rare during a fire or in recently burned areas. Satellites with high spatial resolution (Landsat 8 and 9, Sentinel 2a and 2b) have a narrow swath of coverage and may not capture a clear-sky image for days or weeks after a fire ignites due to persistent smoke. Meanwhile, burned areas are vulnerable to post-fire risks like flooding as soon as the ecosystem starts burning. To reduce this data gap, we developed a tool that synthesizes multi-satellite optical imagery to create burn severity maps using the most timely clear-sky data available. The Advanced Baseline Imager (ABI) aboard GOES, and the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard NOAA-20 and Suomi-NPP, provide hypertemporal data suited to this objective. ABI, a geostationary radiometer, delivers imagery of Continental United States every five minutes, while polar-orbiting NOAA-20 and Suomi NPP scan the US once daily. High frequency, spatially-coarse GOES imagery compliments the less frequent but higher resolution VIIRS imagery.

Our burn-mapping tool is designed to support National Weather Service meteorologists and hydrologists. A user-friendly interface allows practitioners to develop burn severity maps, customize their scale and location, and select before/after fire event dates. The resulting maps are available for download in multiple open-standard, GIS-compatible formats. Powered by Google Earth Engine, the processing is entirely automated and runs on-demand using the high-speed parallel processing of Google Cloud. Because GOES data is continuously uploaded to Google Cloud in near real-time, this App reduces latency by co-locating data storage, access, analysis and visualization.