Digital soil mapping of soil burn severity

Fire alters soil hydrologic properties leading to increased risk of catastrophic debris flows and post-fire flooding. As a result, US federal agencies map soil burn severity (SBS) via direct soil observation and adjustment of rasters of burned area reflectance. We developed a unique application of digital soil mapping (DSM) to map SBS in the Creek Fire which burned 154,000 ha in the Sierra Nevada. We utilized 169 ground-based observations of SBS in combination with raster proxies of soil forming factors, pre-fire fuel conditions, and fire effects to vegetation to build a digital soil mapping model of soil burn severity (DSMSBS) using a random forest algorithm and compared the DSMSBS map to the established SBS map. The DSMSBS model had a cross-validation accuracy of 48%. The established technique had 46% agreement between field observations and pixels. However, since the established technique is manual, it could not be compared to the DSMSBS model via cross-validation. We produced SBS class uncertainty maps, which showed high prediction probabilities around field observations, and low probabilities away from field observations. SBS prediction probabilities could aid post-fire assessment teams with sample prioritization. We report 107 km2 more area classified as high and moderate SBS compared to the established technique. We conclude that blending soil forming factors based mapping and vegetation burn severity mapping can improve SBS mapping. This represents a shift in SBS mapping away from validating remotely sensed reflectance imagery and toward a quantitative soil landscape model, which incorporates both fire and soils information to directly predict SBS. A digital soil mapping method to map wildfire soil burn severity (DSMSBS) was developed. Soil field observations were combined with rasters of environmental covariates and fire effects. Excellent fidelity between field observations of soil burn severity (SBS) and the final DSMSBS map was reported. Direct classification of SBS improves SBS mapping compared to validation of remotely sensed burn severity. Class probabilities generated for SBS may aid post-fire assessment.