Wildland Fire Rate of Spread Estimation Using an Autonomous Unmanned Aerial System: A Case Study

Résumé fourni par la source

This paper presents a case study of wildland fire rate of spread estimation using an autonomous unmanned aerial system (UAS), deployed in a prescribed burn conducted in the Zaleski State Forest in South Ohio. In recent years, UAS have seen increasing use to measure the fire perimeter in active wildland burns. In this case study, we build and deploy a small UAS platform equipped with infrared sensing to measure the rate of spread of the fire front in a prescribed burn conducted in a forest environment with significant topographic expression. Infrared data retrieved from the experiment is re-projected into the three-dimensional world frame, followed by a novel geometrical analysis of the observed fire intensity contours. A Delaunay triangulation is conducted to discretize the space around time-separated fire fronts, resulting in local rate of spread estimates based on fire intensity gradients. The infrared data-based rate of spread estimates are compared against the output of a widely used fire behavior model called the Rothermel model. While the data-based calculations show a good match with the Rothermel model's mean predictions, the latter is shown to exhibit large sensitivity to topographic and environmental parameters, especially slope steepness of the terrain.

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