A predictive model of burn severity based on 20-year satellite-inferred burn severity data in a large southwestern US wilderness area
The authors used the Random Forest algorithm in R to analyze the comparative strength of a suite of topographic variables and Potential Vegetation Type (PVT) to predict high severity fire occurrence from 1984 to 2004 in the Gila Wilderness.
This article found strong relationships between topographic variables and high severity fire occurrence. Severe fire was more likely to occur on north-facing slopes at high elevations due to the interaction of biomass production and fuel accumulation which in turn influences burn severity. Without the influence of humans on this landscape, climate, topography, and vegetation interact strongly with each other to control burn severity in a region dominated by semi-arid forest, where moisture limits vegetation production.