Wildfire risk assessment using multi-source remote sensing data and GIS

This study estimated the spatiotemporal pattern changes of wildfire risk by timely remote sensing and in-situ data. The evaluation index system was constructed based on 14 wildfire-influencing factors (categorized into four classes: human activities, topography, meteorology, and vegetation), the Analytic Hierarchy Process (AHP), and the Entropy Weight Method. The results indicate that wildfires mainly occur in winter and spring in the study area, and the spatial differences in monthly wildfire risk are primarily influenced by the vegetation Leaf Area Index (LAI) and meteorological conditions, followed by human activities and topographical factors. High wildfire risk values in Beijing are distributed near the urban built-up areas in the southeast and east, while high-risk areas are located in the mountainous regions of the west and north. The higher static wildfire risk indicates the temperature has more impact on wildfire risk than that of the LAI. Spatially, the wildfire risk is relatively high in Shunyi District, Tongzhou District, Daxing District, and Fangshan District of the study area, while it is lower in Miyun District, Huairou District, and Changping District. This paper leveraged the latest remote sensing and meteorological observation data to conduct a spatiotemporal pattern assessment of wildfire risk, which can provide a scientific basis for wildfire prevention and management in the study area.