Landsat-based fire severity datasets are an invaluable resource for monitoring and research purposes. These gridded fire severity datasets are generally produced with pre- and post-fire imagery to estimate the degree of fire-induced ecological change. Here, we introduce methods to produce three Landsat-based fire severity metrics using the Google Earth Engine (GEE) platform: The delta normalized burn ratio (dNBR), the relativized delta normalized burn ratio (RdNBR), and the relativized burn ratio (RBR). Our methods do not rely on time-consuming a priori scene selection but instead use a mean compositing approach in which all valid pixels (e.g., cloud-free) over a pre-specified date range (pre- and post-fire) are stacked and the mean value for each pixel over each stack is used to produce the resulting fire severity datasets. This approach demonstrates that fire severity datasets can be produced with relative ease and speed compared to the standard approach in which one pre-fire and one post-fire scene are judiciously identified and used to produce fire severity datasets. We also validate the GEE-derived fire severity metrics using field-based fire severity plots for 18 fires in the western United States. These validations are compared to Landsat-based fire severity datasets produced using only one pre- and post-fire scene, which has been the standard approach in producing such datasets since their inception. Results indicate that the GEE-derived fire severity datasets generally show improved validation statistics compared to parallel versions in which only one pre-fire and one post-fire scene are used, though some of the improvements in some validations are more or less negligible. We provide code and a sample geospatial fire history layer to produce dNBR, RdNBR, and RBR for the 18 fires we evaluated. Although our approach requires that a geospatial fire history layer (i.e., fire perimeters) be produced independently and prior to applying our methods, we suggest that our GEE methodology can reasonably be implemented on hundreds to thousands of fires, thereby increasing opportunities for fire severity monitoring and research across the globe.

Climate changes and associated shifts in ecosystems and fire regimes present enormous challenges for the management of landscapes in the Southwestern US. A central question is whether management strategies can maintain or promote desired ecological conditions under projected future climates. We modeled wildfire and forest responses to climate changes and management activities using two ecosystem process models: FireBGCv2, simulated for the Jemez Mountains, New Mexico, and LANDIS-II, simulated for the Kaibab Plateau, Arizona. We modeled contemporary and two future climates—“Warm-Dry” (CCSM4 RCP 4.5) and “Hot-Arid” (HadGEM2ES RCP 8.5)—and four levels of management including fire suppression alone, a current treatment strategy, and two intensified treatment strategies. We found that Hot-Arid future climate resulted in a fundamental, persistent reorganization of ecosystems in both study areas, including biomass reduction, compositional shifts, and altered forest structure. Climate changes increased the potential for high-severity fire in the Jemez study area, but did not impact fire regime characteristics in the Kaibab. Intensified management treatments somewhat reduced wildfire frequency and severity; however, management strategies did not prevent the reorganization of forest ecosystems in either landscape. Our results suggest that novel approaches may be required to manage future forests for desired conditions.