A remote sensing-based approach to estimating the fire spread rate parameter for individual burn patch extraction

For the past two decades, satellite-derived activef fire data have been used in a multitude of operational applications and in a large and growing body of research on the role of fire within the Earth system. More recent work with satellite-based active fire data has been directed toward estimating what are in effect broad-scale fire spread rates that are in turn used as an important temporal parameter for the extraction of individual-fire boundaries from burned area maps. Here we use data mining to identify active fire clusters that serve as an input to a fire spread reconstruction algorithm to derive optimal global fire spread rates suitable for fire-perimeter extraction. The spread rates calculated for the active fire clusters, which are useful for applications beyond perimeter extraction, correlate with the spread rates based on reference fire boundaries (R (2) = .82, NRMSE = 2.6%) and are generally compatible with other studies, despite key differences in data acquisition methods and quantities measured.

Automated summary

Satellite-derived active fire data have been widely used in research and operational applications for studying the role of fire in the Earth system. This study uses data mining to identify active fire clusters and derive optimal global fire spread rates for fire-perimeter extraction.