Clever Wood Use Could Mitigate Wildfires and Climate Change

California plans to use forest thinning to reduce wildfire risk. New research suggests the state could also see a climate benefit by repurposing waste wood produced by thinning.

“Us versus Them;” Local Social Fragmentation and Its Potential Effects on Building Pathways to Adapting to Wildfire

As the need for wildfire adaptation for human populations in the wildland-urban interface (WUI) intensifies in the face of changes that have increased the number of wildfires that exceed 100 thousand acres, it is becoming more important to come to a better understanding of social complexity on the WUI landscape. It is just as important to further our understanding of the social characteristics of the individual human settlements that inhabit that landscape and attempt to craft strategies to improve wildfire adaptation that are commensurate with local values, management preferences, and local capabilities. The case study research presented in this article evaluates social characteristics present in a WUI community that faces extreme wildfire risk to both people and property. It explores social processes that impede the ability of community members to work together collectively to solve problems (e.g., wildfire risk) and offers an alternative perspective about the nature of residency status (i.e., full-time and non-full-time) and its role in influencing wildfire mitigation efforts. This article closes with recommendations intended to facilitate collective action and foster community development.

Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile

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.