Data for: Fuel treatment effectiveness in the context of landform, vegetation and large, wind-driven wildfires

This data publication represents source data and R scripts for a manuscript entitled "Fuel treatment effectiveness in the context of landform, vegetation and large, wind-driven wildfires" (Prichard et al. 2020). This study was conducted on the 2014 Carlton Complex study area in north-central Washington State. The R scripts include code needed to run the random forest analysis (RF), simultaneous autoregression (SAR) and final plots for the publication. The source data tables include 30-meter (m) resolution point datasets with location x and y representing UTM northing and easting locations, respectively, of every 30 m pixel within the Carlton Complex study areas (A = South, B = North), fire severity indices (dNBR, RBR, RdNBR) as response variables, and predictor variables including past fuel treatments, vegetation type, landform, and weather. Source predictor variables were taken from datasets that preceded the 2014 wildfires.Large wildfires (>50,000 hectares) are becoming increasingly common in semi-arid landscapes of the western United States. Although fuel reduction treatments are used to mitigate potential wildfire effects, they can be overwhelmed in wind-driven wildfire events with extreme fire behavior. We evaluated drivers of fire severity and fuel treatment effectiveness in the 2014 Carlton Complex, a record-setting complex of wildfires in north-central Washington State.These data were published on 02/19/2020. On 03/22/2024, minor metadata updates were made.