Characterizing Forest Fires Risks Using Free Multi-Source Data: Identifying High-Risk Road Sections

Abstract

Transport infrastructures (TIs) play a crucial role for facilitating transportation and enhancing mobility. Nevertheless, they face significant risks from forest fires mainly due to climate change. Developing accurate fire risk maps is essential for planning vegetation maintenance, predicting fire-prone areas, and assessing potential damage levels. Risk maps often rely on field data, which are difficult to update promptly for effective management. This paper introduces an approach to identify and continuously monitor road sections at high risk of forest fires using Random Forest (RF) models along with satellite images. Multiple models were trained using different risk predictors, all achieving test accuracies exceeding 79% and good agreement in the kappa coefficient. Additionally, the key predictors in each model were analyzed. As a result, these models offer a dynamic updating mechanism for risk maps over time. The methodology also enables the integration and fusion of multi-source datasets to pinpoint areas with the highest risk associated with TIs and forest fires. These insights facilitate the delineation and timely update of critical TI areas, thereby enhancing overall risk management.