Recent wildfire prevalence and destruction have led to new initiatives in the search for better land management techniques and prescriptions for controlled burns. With limited data on low-intensity prescribed burns, finding models that can represent fire behavior is of great importance to learning how to control fires with more accuracy while also maintaining the purpose for the burn, be it reducing fuels or managing the ecosystem. Here we use a data set of infrared temperatures collected in the New Jersey Pine Barrens from 2017 through 2020 to develop a model for very fine-scale fire behavior ($\approx 0.05 {\mathrm{m}}^2$). The model uses distributions from the data set to define five stages in fire behavior in a cellular automata framework. For each cell, the transition between each stage is probabilistically driven based on the radiant temperature values of the cell and its immediate neighbors in a coupled map lattice. With five distinct initial conditions, we performed 100 simulations and used the parameters derived from the data set to develop metrics for model verification. To validate the model, we also expanded it to include variables not in the data set that are important for fire behavior, e.g., fuel moisture levels and spotting ignitions. The model matches several metrics compared to the observational data set and exhibits behavioral characteristics expected from low-intensity wildfire behavior including a long and varied burn time for each cell after initial ignition, and lingering embers in the burn zone.

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• Cellular automata
• Network diffusion
• Stochastic processes
• Thermodynamics
• Dynamical systems
• Data analysis