Simulating wildfire spread based on continuous time series remote sensing images and cellular automata

Background Acquiring behaviour parameters of wildfire propagation and developing firefighting strategies necessitate a precise and efficient simulation method; fireline coordinates and rate of spread (ROS) are two crucial parameters closely associated with simulation precision. Aims This study proposes an adaptive simulation method for wildfire propagation by integrating continuous time series remote sensing images with a cellular automata (CA) model. Methods The ROS in each direction is calculated using continuous time fireline coordinates derived from multi-source remote sensing images. A time-adaptive propagation algorithm is developed based on the CA model (time-adaptive cellular automata, TCA). Vegetation distribution information is derived to establish a simulation system for verification experiments. Key results The TCA model demonstrated satisfactory simulation performance, with a prediction accuracy of 92.2% for burned area and 87.3% for fireline length within local regions in the MuLi Forest Fire and effectively characterised gradual spread based on low ROS. Conclusion The adaptive simulation method produces fairly precise results and demonstrated its capability to achieve localised and gradual propagation. This software serves as a powerful tool for wildland surface fire prevention and control. Implications The adaptive simulation method based on continuous time remote sensing images and the CA model are essential for accurately predicting wildfire propagation.