Abstract
Wildfires pose a major environmental and societal challenge, due to their link with anthropogenic activities and changing climatic conditions. This study aimed to enhance our understanding of the drivers of wildfire occurrence across continental Chile by developing robust predictive models incorporating climatic, land cover, and anthropogenic variables. We leveraged geospatial data on historical fire events, infrastructure, fuels and weather, coupled with historical fire records through Random Forest binary models to ascertain the key drivers of ignition across four distinct ecological zones: North, Central Chile, South, and the Andes. Our analysis explored potential differences between arson and unintended fires within these regions. Model validation, assessed using the Area Under the Curve (AUC), revealed significant regional variations in predictive performance. The southern and northern zones exhibited higher predictive capacity, potentially due to less complex landscapes and fewer ignition sources compared to the densely populated and infrastructure- prone central zone, which showed the lowest AUC. The Andes region displayed intermediate performance. Our results indicated that anthropogenic factors, particularly the distance to power lines, roads, and the wildland-urban interface (WUI), were consistently among the most important predictors of wildfire ignition across the majority of the studied regions. This highlights the significant impact of human accessibility and infrastructure on fire incidence in Chile. In contrast, fuel-related and climatic variables, such as Dry Fuel Moisture Content (DFMC) and its anomaly, showed generally lower importance, although their influence increased notably in the southern zone. Partial dependence plots further elucidated the distinct ways in which these key variables influenced ignition probability across different regions and between arson and unintended fires. The findings emphasize the necessity of adopting region-specific approaches in wildfire modeling and prevention strategies, acknowledging the different interactions between natural and anthropogenic factors across Chile. This research provides a fundamental understanding for future advanced modeling and targeted risk management efforts. Future research should aim to incorporate more detailed socioeconomic data to further refine predictive models and inform effective risk mitigation strategies.
Marcos Rodrigues
Professor of Geographic Information Science
My research interests include forest fires, spatial analysis and forestry.