MASTER Fire Detection Documentation¶
Detect active fire from NASA MASTER airborne thermal imaging data.
This project processes Level 1B (L1B) calibrated radiance from the MODIS/ASTER Airborne Simulator (MASTER) and identifies fire pixels using physics-based threshold detection, contextual anomaly analysis, multi-pass consistency filtering, and machine learning.
The data comes from the FireSense 2023 campaign – prescribed burns on the Kaibab Plateau in Arizona (October 18–20, 2023).
Contents
- Operator’s Guide
- Science and Algorithm Guide
- Instrument Overview
- Channel Selection
- Radiometric Processing
- Fire Detection: Absolute Threshold
- Fire Detection: Contextual Anomaly
- Multi-Pass Consistency Filter
- SWIR for False Positive Discrimination
- Day/Night Classification
- Vegetation Mapping (NDVI)
- Mosaic Gridding
- Connected Component Fire Zone Analysis
- ML Fire Detection
- Assumptions and Limitations
- References
- Machine Learning Fire Detection
- API Reference
- Script Reference