Fire is a keystone process in many ecosystems of western North America. Severe fires kill and consume large amounts of above‐ and belowground biomass and affect soils, resulting in long‐lasting consequences for vegetation, aquatic ecosystem productivity and diversity, and other ecosystem properties. We analyzed the occurrence of, and trends in, satellite‐derived burn severity across six ecoregions in the Southwest and Northwest regions of the United States from 1984 to 2006 using data from the Monitoring Trends in Burn Severity project. Using 1,024 fires from the Northwest (4,311,871 ha) and 497 fires from the Southwest (1,434,670 ha), we examined the relative influence of fine‐scale topography and coarse‐scale weather and climate on burn severity (the degree of change from before the fire to one year after) using the Random Forest machine learning algorithm. Together, topography, climate, and weather explained severe fire occurrence with classification accuracies ranging from 68% to 84%. Topographic variables were relatively more important predictors of severe fire occurrence than either climate or weather variables. Predictability of severe fire was consistently lower during years with widespread fires, suggesting that local control exerted by topography may be overwhelmed by regional climatic controls when fires burn in dry conditions. Annually, area burned severely was strongly correlated with area burned in all ecoregions (Pearson's correlation 0.86–0.97; p < 0.001), while the proportion of area burned severely was significantly correlated with area burned only in two ecoregions (p ≤ 0.037). During our short time series, only ecoregions in the Southwest showed evidence of a significant increase (p ≤ 0.036) in annual area burned and area burned severely, and annual proportion burned severely increased in just one of the three Southwest ecoregions. We suggest that predictive mapping of the potential for severe fire is possible, and will be improved with climate data at the scale of the topographic and Landsat‐derived burn severity data. Although severity is a value‐laden term implying negative ecosystem effects, we stress that severity can be objectively measured and recognize that high severity fire is an important ecological process within the historical range of variability in some ecosystems.

Satellite-inferred burn severity data have become increasingly popular over the last decade for management and research purposes. These data typically quantify spectral change between pre-and post-fire satellite images (usually Landsat). There is an active debate regarding which of the two main equations, the delta normalized burn ratio (dNBR) and its relativized form (RdNBR), is most suitable for quantifying burn severity; each has its critics. In this study, we propose and evaluate a new Landsat-based burn severity metric, the relativized burn ratio (RBR), that provides an alternative to dNBR and RdNBR. For 18 fires in the western US, we compared the performance of RBR to both dNBR and RdNBR by evaluating the agreement of these metrics with field-based burn severity measurements. Specifically, we evaluated (1) the correspondence between each metric and a continuous measure of burn severity (the composite burn index) and (2) the overall accuracy of each metric when classifying into discrete burn severity classes (i.e., unchanged, low, moderate, and high). Results indicate that RBR corresponds better to field-based measurements (average R 2 among 18 fires = 0.786) than both dNBR (R 2 = 0.761) and RdNBR (R 2 = 0.766). Furthermore, the overall classification accuracy achieved with RBR (average among 18 fires = 70.5%) was higher than both dNBR (68.4%) and RdNBR (69.2%). Consequently, we recommend RBR as a robust alternative to both dNBR and RdNBR for measuring and classifying burn severity. OPEN ACCESSRemote Sens. 2014, 6 1828

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