Wildfire Propagation Modeling using Satellite-Derived Parameters and Generalized Elliptical Frames

Wildfires pose significant threats to ecosystems and communities, yet accurately modeling fire spread remains challenging, particularly in regions where environmental and fuel data are scarce or unavailable. This study introduces an innovative conceptual and methodological framework for simulating wildfire propagation and estimating the rate of spread using a hybrid geometric and data-driven approach that relies 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 applies the Huygens principle, where generalized elliptical frames are expanded locally at every point along the fire perimeter, and their combined envelope forms the evolving wavefront. This method is best suited for situations in which environmental variables are available and can be incorporated to refine the anisotropic spread function. The second strategy relies solely on the generalized elliptical frames themselves; for each time step, an elliptical frame is constructed from the inferred head and back rates of spread and wind, and the burned area is obtained by enclosing the region determined by these curves. Together, these two methods provide a flexible toolkit that adapts to both data-rich and data-limited conditions while retaining a unified geometric interpretation of wildfire spread. To demonstrate the applicability of the method, we present 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 reproduces key qualitative features of the observed propagation pattern, underscoring the flexibility and robustness of the proposed approach in data-limited contexts.

💡 Research Summary

This paper presents a novel conceptual and methodological framework for simulating wildfire propagation that relies exclusively on multi-source satellite observations, eliminating the dependency on detailed, ground-based fuel models or terrain measurements. The primary motivation is to provide a practical and scalable alternative for regions where such in-situ data are scarce or unavailable.

The core innovation lies in a hybrid geometric modeling approach that integrates thermal fire-front detections, atmospheric conditions (wind), and vegetation indices. The framework is built around two complementary strategies based on the concept of “Generalized Elliptical Frames.” The first strategy applies the Huygens principle, where local elliptical frames are expanded from every point along the fire perimeter. The evolving firefront is then formed by the envelope of these combined local expansions. This method is most effective when environmental variables like wind fields or live fuel moisture content (LFMC) are available to refine the anisotropic spread function. The second, more streamlined strategy constructs a single elliptical frame for each time step based on inferred head-fire and back-fire rates of spread derived from satellite thermal imagery and wind data. The burned area is simply the region enclosed by this curve. This pair of strategies offers a flexible toolkit adaptable to both data-rich and data-limited scenarios while maintaining a unified geometric interpretation of fire spread.

The model inputs are sourced entirely from satellites. Thermal infrared sensors (e.g., MODIS, VIIRS) provide active fire front locations and Fire Radiative Power (FRP). Atmospheric satellites supply wind speed and direction. Vegetation health and moisture are assessed through indices like NDVI/EVI and Land Surface Temperature (LST). The authors propose an empirical formula combining NDVI, LST, and Day of Year (DOY) using sine/cosine terms to estimate the crucial parameter of Live Fuel Moisture Content (LFMC). They provide practical guidance for estimating the coefficients in this formula, outlining scenarios for both field-based calibration (when in-situ LFMC data exists) and proxy-based estimation (using literature values or data from similar regions when field data is absent).

To demonstrate applicability, the authors conduct a case study on the Eaton Fire of January 2025. Despite the absence of complete operational datasets for this event, the model, driven solely by satellite-derived parameters, successfully reproduced key qualitative features of the observed fire propagation pattern. This underscores the framework’s robustness and flexibility in data-limited contexts.

The paper intentionally focuses on establishing the theoretical and computational foundations rather than performing extensive numerical validation against specific historical fires. The proposed methodology is designed to be conceptually clear, computationally efficient, and highly adaptable. It bridges advanced geometric modeling with modern satellite-based fire monitoring, establishing a foundational structure for future developments. Potential applications range from local operational fire management support to continental-scale environmental assessment and risk analysis.