Airborne Infrared Remote Sensing for Characterizing Wildfire Behaviour in Prescribed Fire

Increasing wildfire impacts highlight the importance of studying wildfire behaviour and effects to develop effective management and mitigation strategies. Prescribed fire has demonstrated to be a powerful tool for land and fire risk management. However, optimizing its use requires detailed knowledge of the complex relationships between fire, vegetation, and atmospheric dynamics. In this work, we demonstrate how airborne remote sensing facilitates the analysis of fire behaviour and its effects on vegetation. We will present airborne long-wave and mid-wave infrared imagery  collected  over a prescribed fire during the Fort Stewart Integrated Research Campaign in 2024. 

The data consists of seven video sequences with a total duration of one hour and fifty-four minutes. The collected imagery was pre-processed and georeferenced by integrating inertial measurement unit data, then further stabilized using feature matching techniques to mitigate helicopter-induced jitter. This dataset will be utilized to investigate the relationship between essential fire behaviour metrics, such as  fire rate of spread and fire radiative power, and vegetative conditions before, during, and after combustion, thereby providing insight into fuel consumption and fire impacts. This analysis will be useful to validate fire behaviour models and improve the capacity to forecast fire behaviour during prescribed fires, which is bound to have a significant impact on wildfire management in the future.

Acknowledgements: This work was supported by the U.S. National Science Foundation under award number 2053619, the USDA Forest Service Fire and Smoke Model Evaluation Experiment (FASMEE), the US Strategic Environmental Research and Development Program (SERDP) under project RC20-1364, and the EU COST Action NERO (CA22164).