Wildland Fire Rate of Spread Estimation Using Infrared Data from Aerial Systems

Abstract

This paper presents a comparative case study of wildland fire rate of spread (ROS) estimation, contrasting infrared data from an autonomous unmanned aerial system (UAS) with predictions from the Rothermel fire behavior model during a prescribed burn conducted in Zaleski State Forest, Ohio. In this study, we built and deployed a small UAS platform equipped with infrared sensing to measure the ROS of the fire front in a forest with significant topographic variation. Infrared data from the experiment were reprojected into the three-dimensional world frame, followed by a geometrical analysis of the observed fire intensity contours. Delaunay triangulation was performed to discretize the space around time-separated fire fronts, resulting in local ROS estimates based on fire intensity gradients. The infrared-derived ROS estimates were then compared with widely used Rothermel model predictions. Finally, a vector alignment analysis was conducted to investigate discrepancies between the methods by comparing ROS directionality with local terrain gradients. The results showed a small to negligible correlation, suggesting that the observed fire-front motion was not strongly aligned with terrain-gradient direction alone. These results underscore the complexities of fire behavior shaped by terrain, environmental factors, and the limitations of predictive models in natural fire conditions.