On timeliness and accuracy of wildfire detection by the GOES WF-ABBA algorithm over California during the 2006 fire season

Abstract

The Wildfire Automated Biomass Burning Algorithm (WF-ABBA) is a state-of-the-art algorithm for geostationary wildfire detection whose results have been increasingly used in a range of environmental applications. At present, the WF-ABBA validation activities and, in general, fire product validation methodologies are at a markedly less advanced stage than the algorithm itself. Particularly, little is known about detection timeliness, despite the value of such information for assessing the potential of geostationary observations to improve tactical decision making of first responders. This paper contributes to reducing this gap in two ways. Firstly, we describe a new methodology that is suitable for evaluating geostationary satellite wildfire detection in terms of incidents with regard to both timeliness and reliability. This methodology utilizes available official multi-agency wildfire reporting information and multitemporal Landsat imagery. Secondly, we apply the proposed validation method to temporally filtered GOES-West WF-ABBA (ver. 6.1) detections for the 2006 fire season over the State of California and present incident-wise and pixel-wise performance information. The results indicate highly reliable pixel-wise performance of WF-ABBA, with about 75% of fire pixels (or more) corresponding to actual recorded active wildfires. A substantial portion of wildfires were detected during their first hour of activity, and a few incidents—even before the initial reports from conventional sources. Although the WF-ABBA performs best at what it was designed for: consistently re-detecting (monitoring) active fires, we believe there is an additional potential for automated detection from current geostationary data to reduce wildfire ignition latencies in the Western U.S. Our results can serve as a guideline for algorithm developers and users of the WF-ABBA fire product.