Forest fire rescue path network optimization combining multi-source remote sensing data and hierarchical analysis

Forest fires have exhibited an escalating trend in both frequency and intensity globally, with major and catastrophic events causing significant damage to forest resources, human lives, and property. Timely and accurate monitoring using multi-source remote sensing data presents an effective technological approach, substantially enhancing fire management efficiency and providing critical technical support for proactive prevention. Traditional manual field surveys are often inefficient and costly. Multi-source remote sensing data, characterized by their broad coverage, rapid update capabilities, and rich spectral information, hold immense potential for comprehensive forest fire monitoring. To facilitate prompt and precise rescue operations, this study utilizes Landsat 8 satellite imagery to analyze a specific fire event in Yuxi City, Yunnan Province, China. The methodology involves extracting active fire locations and simulating potential fire spread scenarios. We further integrate eight critical real-world influencing factors: slope, elevation, aspect, curvature, the Burn Index, vegetation index, proximity to roads, and distance to urban centers. Employing the Analytic Hierarchy Process (AHP), these factors were weighted and synthesized to assess the regional safety coefficient, generating a safety level map that functions as a minimum resistance surface. Subsequently, GIS-based spatial analysis techniques were applied to derive the minimum cost paths for evacuation and rescue access within the fire-affected area, stratified by the calculated safety levels. This process enabled the construction of an intelligent rescue path network. The results provide a robust theoretical framework and practical technical protocols for optimizing emergency response routing during forest fire incidents, enhancing both the safety and efficiency of rescue operations.