Wildfire mapping
Fire perimeters, satellite fire detections, burn severity, smoke plumes and exposure: mapping wildfires from open CAL FIRE, NOAA GOES, HMS and Sentinel-2 data.
Mapping a wildfire means combining sources that see it differently. Perimeters from fire agencies give the final shape; geostationary satellites such as GOES see the fire every few minutes at kilometre scale; polar-orbiting VIIRS sees it at 375 metres a few times a day; NOAA analysts draw the smoke. Each answers a different question: where, when, how hard, and who breathed it.
The guides here follow real fires, mainly California’s 2024 Park Fire, through CAL FIRE’s perimeter services, GOES-18 fire detections, NOAA HMS fire points and smoke polygons, and the century of earlier perimeters it burned into. Each step is checked against the record, from computed acreage against the official figure to satellite detections against the final perimeter.
🧭 Start with the concepts
The ideas the how-to guides below assume you already have.
⚙️ Then do the task
Step-by-step walkthroughs with code you can copy and adapt.
- How to Download CAL FIRE Wildfire Perimeters in Python
- How to Find Where a Wildfire Reburned Earlier Fires in Python
- How to Track a Wildfire with GOES Satellite Fire Detections in Python
- How to Map a Wildfire's Daily Progression from Satellite Detections in Python
- How to Use NOAA HMS Satellite Fire Points in Python
- How to Map Wildfire Smoke Plumes from NOAA HMS in Python
Show every how-to guide in this topic
- How to Estimate How Many People Were Under Wildfire Smoke in Python
- How to Calculate Fire Weather Indices in Python
- How to Calculate Wildfire Season Statistics in Python
- How to Calculate Fire Radiative Energy From GOES in Python
- How to Count Buildings Inside a Wildfire Perimeter in Python
- How to Measure Exposure in Rings Around a Fire Perimeter in Python
🔧 When it breaks
The errors this topic produces, with the cause explained rather than just the command.
Related topics
Subjects that keep coming up alongside this one, most overlap first.