Toward Early Wildfire Detection and Improved Monitoring from Satellite Observations

Increased fire frequency, rates of spread, and severity elevate an urgent need to have reliable low-cost capabilities for early detection and monitoring of fires from space at regional and state scales.

Current state-of-the-art methods (MODIS-FIRE and GOES WF-ABBA), although used semioperationally, utilize only a small fraction of the useful remote sensing information, thus limiting their capacity to contribute to reducing societal losses.

We present a new, multitemporal approach to wildfire detection based on the Dynamic Detection Model (DDM). The DDM uses past images of the surveyed scene to automatically detect anomalous changes in surface thermo-physical properties and separate them from the observed changes driven by the weather dynamics. Initial, non-optimized versions of the method were applied to MODIS and GOES-West thermal images over California and showed the potential for a drastic increase in fire detection accuracy, compared to the contextual approach used in the standard algorithms.