Abstract

Wildfire risk is latent in Chilean metropolitan areas characterized by the strong presence of wildland–urban interfaces (WUIs). The Concepción metropolitan area (CMA) constitutes one of the most representative samples of that dynamic. The wildfire risk in the CMA was addressed by establishing a model of five categories (near zero, low, moderate, high, and very high) that represent discernible thresholds in fire occurrence, using geospatial data and satellite images describing anthropic–biophysical factors that trigger fires. Those were used to deliver a model of fire hazard using machine learning algorithms, including principal component analysis and Kohonen self-organizing maps in two experimental scenarios: only native forest and only forestry plantation. The model was validated using fire hotspots obtained from the forestry government organization. The results indicated that 12.3 % of the CMA's surface area has a high and very high risk of a forest fire, 29.4 % has a moderate risk, and 58.3 % has a low and very low risk. Lastly, the observed main drivers that have deepened this risk were discussed: first, the evident proximity between the increasing urban areas with exotic forestry plantations and, second, climate change that threatens triggering more severe and large wildfires because of human activities.