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.

中文翻译：

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地中海生态系统基于数据的野火风险模型。智利中部康塞普西翁大都会区研究案例

摘要。智利大都市地区的野火风险很高，其特征是野地-城市界面（WUI）的强大存在。康塞普西翁大都会区（CMA）是该动态最有代表性的样本之一。通过使用描述人类活动的生物地理因素和地理空间数据和卫星图像，建立了5个类别（接近零，低，中，高和非常高）的模型来表示火灾发生的可分辨阈值，从而解决了CMA中的野火风险火灾。这些被用于使用机器学习算法（包括主成分分析和Kohonen自组织图）在两个实验场景中提供火灾隐患模型：仅原生森林和仅林业种植园。使用从林业政府组织获得的火点对模型进行了验证。结果表明，CMA表面积的12.3％具有很高和非常高的森林火灾风险，29.4％具有中等风险，58.3％具有低和非常低风险。最后，讨论了观察到的加剧这种风险的主要动因：首先，日益增长的城市地区与异国林业种植园之间的明显距离，其次，气候变化可能由于人类活动而引发更严重和更大的野火。