Land Cover–Land Use Changes (LULCC) and landscape fragmentation have been a common research topic for Geographic Information Systems (GIS) scientists since the middle of the 20th century; particularly, they have helped to make accessible the spatial characteristics of land management through time. We researched LULCC and landscape fragmentation in Arauco Province in Chile using satellite image analysis (1976–2016) and FRAGSTAT software. This area is in a constant struggle for land use between agroindustry, urban sprawl, and the expansion of exotic plantations (pine-eucalyptus) subsidized by Chilean government. The main results are: (1) we obtained the surface percentages for each land cover , (2) net changes for each cover by adding and losing surface (ha), (3) the transition map that enlightens the surface transformations of LULCC by its four processes substitution, abandonment, habilitation, regeneration and degradation, (4) the native forest loss in the first half of the period (1976–2001) was 1.85%/year, meanwhile for the second half (2001–2016) it was 6.5%/year, (5) landscape fragmentation processes occurred in patches and deforestation is its main driver, (6) aggregation changed the landscape since fragmentation and deforestation processes started the substitution of native forest, and (7) the habilitation of agricultural lands and degradation of wooded masses with exotic species increased their aggregation to 90%.

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.

Abstract. Wildfire risk is latent in Chilean metropolitan areas characterized by the strong presence of Wildland-Urban Interfaces (WUI). The Metropolitan Area of Concepción (CMA) constitutes one of the most representative samples of that dynamic. The wildfire risk in the CMA was addressed by establishing a model of 5 categories (Near Zero, Low, Medium, 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 spots 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 medium 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 to trigger more severe and large wildfires because of human activities.