Multi-source remote sensing-based forest fire monitoring

Abstract. Wildfires bring a significant threat to the ecological environment and the safety of human society. Therefore, a timely and accurate understanding of the situation in the fire area is crucial for reducing fire damage. This study takes the forest fire in Xichang City, China as a case, using multi-source remote sensing data to dynamically monitor and analyze forest fires, aiming to provide a scientific basis and technical support for fire prevention and firefighting. The study first uses temperature inversion technology based on multi-source remote sensing data to monitor the fire scene in real time and accurately extract fire points. In addition, the study extracts key factors affecting fire suppression, such as water resources, vegetation coverage, and terrain, and evaluates the safety factor of the burned area using spatial principal component analysis. To optimize rescue route planning, the study constructs a minimum resistance surface and uses GIS spatial analysis to extract the minimum cost path for rescue safety network construction. The results show that the burned area, dense vegetation, farther from water bodies, located in high-altitude and steep slope regions have a greater impact on the safety of the firefighting area. This study provides scientific and effective decision support for the prevention and firefighting of forest fires through multi-source remote sensing technology and GIS spatial analysis. The research findings not only improve the efficiency of fire emergency response but also offer new perspectives and methods for forest fire management.