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.