The methods we developed provide a viable option for producing day-of-burning data where agency-generated fire progression maps are of poor quality or unavailable. Used in conjunction with data from weather stations, this method will help researchers from varied disciplines to evaluate the influence of observed daily weather on observed fire-related effects (e.g., smoke productions, carbon emissions, and burn severity) over large landscapes and over large numbers of fires. In fact, this method has already been successfully incorporated into other fire studies, including one that quantified the influence of weather on fire spread.
The relativized burn ratio (RBR) is a Landsat-based burn severity metric that is an alternative to both dNBR (normalized burn ratio algorithim) and RdNBR (relative form of normalized burn ratio algorithim). The correspondence between RBR and field-based measures of burn severity indicates an improvement over dNBR and RdNBR. Given the number of fires analyzed in this study and the large geographic extent, we demonstrated that RBR is a robust metric for measuring and classifying burn severity over a broad range of fire-regime types. As such, the use of RBR should help facilitate the description and study of burn severity patterns, as well as their drivers and consequences in forests like those of the conterminous western US.