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

Huajian Zhuang; Naian Liu; Xiaodong Xie; Xuan Xu; Mengmeng Li; Yang Zhang; Rui Wang

doi:10.1071/WF24130

RESEARCH ARTICLE (Open Access)

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

Huajian Zhuang ^A ^B , Naian Liu ^A ^B ^* , Xiaodong Xie ^A ^B ^* , Xuan Xu ^A ^B , Mengmeng Li ^A ^B , Yang Zhang ^A ^B and Rui Wang ^C

+ Author Affiliations

- Author Affiliations

^A State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, Anhui, 230026, China.

^B Ministry of Emergency Management Key Laboratory of Forest Fire Monitoring and Warning, University of Science and Technology of China, Hefei, 230026, China.

^C AVIC General Aircraft Huanan Industry Co., Ltd., Zhuhai, Guangdong, 519040, China.

^* Correspondence to: liunai@ustc.edu.cn, xiexd@ustc.edu.cn

International Journal of Wildland Fire 34, WF24130 https://doi.org/10.1071/WF24130

Submitted: 9 August 2024 Accepted: 13 December 2024 Published: 16 January 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

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.

Keywords: cellular automata model, continuous-time remote sensing images, coordinates of fireline, rate of spread, simulation of wildfire propagation, simulation software, vegetation inversion, wildfire spread rules.

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