Reproduction of Smaller Wildfire Perimeters Observed by Polar-Orbiting Satellites Using ROS Adjustment Factors and Wildfire Spread Simulators

While geostationary satellites can provide continuous near-real-time observations, their low spatial resolution makes it difficult to detect small wildfires. Conversely, polar-orbiting satellites are capable of observing small wildfires at high spatial resolution, but can operate only within restricted observation periods. To improve wildfire spread prediction accuracy using polar-orbiting satellite observations, this paper proposes a novel methodology to accurately reproduce wildfire perimeters observed at specific time points by these satellites. The approach employs a wildfire spread simulator combined with a rate of spread (ROS) adjustment factor. The proposed algorithm derives ROS adjustment factors for each fuel model based on differential evolution, achieving up to a 0.4 increase in the Sørensen index when reproducing wildfire perimeter data at given observation times. Incorporating these factors into simulator-based predictions allows comprehensive consideration of external factors affecting wildfire propagation, which have not been sufficiently accounted for in previous methods. Moreover, considering the frequent occurrence of small wildfires in Korea, this study establishes a mapping between major species of trees in Korea and corresponding Fire Behavior Fuel Models (FBFMs). This serves as an example of appropriately matching major species of trees to FBFMs for wildfire spread prediction in countries where FBFMs have not been previously applied. The methodology’s effectiveness is demonstrated using wildfire perimeter data from polar-orbiting satellite observations and ignition points of recent wildfires in Korea. The proposed algorithm is expected to significantly enhance wildfire response by swiftly providing critical information for accurate wildfire spread prediction. This will facilitate prompt and precise countermeasures for small wildfires independent of external conditions such as weather.