Dr. Hengameh R. Dehkordi, a researcher at the University of California, Berkeley, has developed a new approach to modeling wildfire spread using satellite data. This work, published in the International Journal of Wildland Fire, offers a promising tool for energy companies and utilities that operate in wildfire-prone areas, as it can help improve wildfire prediction and management strategies.
Wildfires present significant challenges to ecosystems, communities, and energy infrastructure. Accurately predicting their spread is crucial for effective management and mitigation, but this task is often hindered by a lack of detailed environmental and fuel data, particularly in remote or under-researched regions. Dr. Dehkordi’s study introduces a novel framework that simulates wildfire propagation and estimates the rate of spread using a hybrid geometric and data-driven approach, relying exclusively on multi-source satellite observations.
The framework integrates thermal fire-front detections, atmospheric conditions, and vegetation indices using two complementary geometric modeling strategies. The first strategy, based on the Huygens principle, expands generalized elliptical frames locally at every point along the fire perimeter, with their combined envelope forming the evolving wavefront. This method is particularly useful when environmental variables are available to refine the anisotropic spread function. The second strategy relies solely on the generalized elliptical frames, constructing an elliptical frame from the inferred head and back rates of spread and wind, and determining the burned area by enclosing the region defined by these curves. This approach is designed for data-limited conditions.
To demonstrate the applicability of the method, Dr. Dehkordi presents a case study based on the Eaton Fire, January 2025, using publicly available multi-day satellite imagery. Despite the absence of complete operational datasets for that event, the model driven only by satellite-derived parameters successfully reproduced key qualitative features of the observed propagation pattern. This underscores the flexibility and robustness of the proposed approach in data-limited contexts.
For the energy sector, this research offers a valuable tool for predicting wildfire spread in areas where detailed environmental data may be scarce. By leveraging satellite observations, energy companies can enhance their wildfire management strategies, better protect critical infrastructure, and improve overall safety and operational resilience. The study highlights the potential of satellite-derived parameters in providing reliable and adaptable wildfire spread models, even in challenging data-limited scenarios.
Source: International Journal of Wildland Fire
This article is based on research available at arXiv.