From Data to Knowledge — Faster: GOES Early Fire Detection System to Inform Operational Wildfire Response and Management

Abstract:

Fire managers at various levels require near-real-time, low-cost, systematic, and reliable early detection capabilities with minimal latency to effectively respond to wildfire ignitions and minimize the risk of catastrophic development. The GOES satellite images collected for vast territories at high temporal frequencies provide a consistent and reliable source for operational active fire mapping realized by the WF-ABBA algorithm. However, their potential to provide early warning or rapid confirmation of initial fire ignition reports from conventional sources remains underutilized, partly because the operational wildfire detection has been successfully optimized for users and applications for which timeliness of initial detection is a low priority, contrasting to the needs of first responders.

We present our progress in developing the GOES Early Fire Detection (GOES-EFD) system, a collaborative effort led by University of California-Davis and USDA Forest Service. The GOES-EFD specifically focuses on first detection timeliness for wildfire incidents. It is automatically trained for a monitored scene and capitalizes on multiyear cross-disciplinary algorithm research. Initial retrospective tests in Western US demonstrate significantly earlier identification detection of new ignitions than existing operational capabilities and a further improvement prospect. The GOES-EFD-β prototype will be initially deployed for the Western US region to process imagery from GOES-NOP and the rapid and 4 times higher spatial resolution imagery from GOES-R — the upcoming next generation of GOES satellites. These and other enhanced capabilities of GOES-R are expected to significantly improve the timeliness of fire ignition information from GOES-EFD.