Multi-factor Fire Susceptibility Index (FSI) in Hungary: a data-driven approach using remotely sensed data for peri-urban and forested landscapes

Abstract Wildfire risk under climate change and urban expansion emphasizes the need for integrated risk mapping. This study develops a multi-factor Fire Susceptibility Index (FSI) based on vegetation, terrain and anthropogenic factors applied to the Pest County and Budapest region of Hungary. It combines satellite-derived vegetation, moisture, and thermal indices; topographic variables; and proximity to roads and settlements. We then compare FSI patterns between the extreme 2022 drought and a milder 2024, applying Principal Component Analysis (PCA) to identify dominant risk gradients. The model was quantitatively validated against historical fire occurrence data (2018–2024) from the NASA FIRMS dataset through Frequency Ratio (FR) analysis. The results of the PCA revealed that the first component (accounting for approximately 28.6% of the total variance) corresponded to a terrain-fuel/dryness gradient, while the second component (12%) reflected an elevation/access gradient. The validation results demonstrated that over 90% of historical ignitions occurred within FSI Class 2, yielding FR values > 1.2 across both study years. This finding serves to substantiate the model’s capacity to discern the “ignition shelf” within transitional peri-urban zones. While the 2022 drought produced widespread high-susceptibility zones in steep, forested uplands, the actual ignition frequency was highest in flatter, accessible areas, highlighting a disparity between landscape hazard and ignition probability. The mean FSI and the proportion of high-risk areas decreased from 2022 to 2024, therefore, the integrated FSI, PCA, and FR framework provides a robust, validated approach for wildfire risk assessment, which is effective in distinguishing between high-hazard fuel-terrain complexes and high-frequency ignition zones.