Assessing uncertainty and demonstrating potential for estimating fire rate of spread at landscape scales based on time sequential airborne thermal infrared imaging

An important property of wildfire behaviour is rate of spread (ROS). The objectives of this study are to evaluate the uncertainty of landscape-scale ROS estimates derived from repetitive airborne thermal infrared (ATIR) georeferenced imagery and the utility of such estimates for understanding fire behaviour and controls on spread rates. Time-sequential ATIR image data were collected for the Cedar, Detwiler, and Rey Fires, which burned in California during summers of 2016 and 2017. We analyse error, uncertainty, and precision of ROS estimates associated with co-location accuracy, delineation of active fire front positions, and generation of fire spread vectors. The major sources of uncertainty influencing accuracy of ROS estimates are co-registration accuracy of sequential image pairs and procedures for delineating active fire front locations and spread vectors between them; none of these were found to be substantial. Median ROS estimates are 11 m min(-1) for the Cedar Fire and 8 m min(-1) for the Detwiler Fire, both of which burned through mixed shrub and tree areas of the Sierra Nevada foothills and were estimated for downslope spread events. Of the three study fires, the fastest spread rates (average spread of 25 m min(-1) with maximum of 39 m min(-1)) are estimated for the Rey Fire, which burned on variable directional slopes through chaparral shrubland vegetation.