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The importance of understanding the impact of wildfires on natural ecosystems has given rise to the development of realistic computer models for the simulation of wildfires. Stochastic models based on simplified equations and local interactions, such as Cellular Automata (CA) models, are particularly popular as an alternative to more computationally demanding deterministic models. However, the challenges associated with observing wildfires under natural conditions, and the highly non-linear nature of fire spread makes it extremely difficult to parameterize them. In this work we present a method for adjusting the behaviour of one such CA model from the statistical analysis of satellite data of more than 750,000 African wildfires detected in 2003. Statistical metrics are developed to characterize agreement between model and satellite observations. The average probability of fire transmission amongst cells and the spatial scale of the model are adjusted so that maximum agreement is found between model output and the observed extension and statistical distribution of the real fires. While the results obtained are only valid for the particular CA model used and within the geographical limits of the region studied, we believe the process could be adapted to fine-tune and validate other CA models in regions where enough fire observations are available.

Statistical parameter estimation for a cellular automata wildfire model based on satellite observations