An operational method for mapping the composition of post-fire litter

Recent increase in the frequency and spatial extent of wildfires motivates the quick recognition of the affected soil properties over large areas. Digital Soil Mapping is a valuable approach to map soil attributes based on remote sensing and field observations. We predicted the spatial distribution of post-fire litter composition in a 40,600 ha basin burned on the 2017 wildfire of Chile. Remotely sensed data of topography, vegetation structure and spectral indices (SI) were used as predictors of random forest (RF) models. Litter sampled in 60 hillslopes after the fire provided training and validation data. Predictors selected by the Variable Selection Using Random Forests (VSURF) algorithm resulted in models for litter composition with acceptable accuracy (coefficient of determination, R ² = 0.51–0.64, Normalized Root Mean Square Error, NRMSE = 16.9–22.1, percentage bias, pbias = −0.35%-0.5%). Modelled litter parameters decrease in concentration respect to the degree of burn severity, and the pre-fire biomass. Because pre-fire vegetation was conditioned by land cover and by a previous (2 years old) wildfire event, our results highlight the cumulative effect of severe wildfires in the depletion of litter composition.