“Prescribed grass fire evolution mapping and rate of spread measurement using orthorectified thermal imagery from a fixed-wing UAS”
This article was published online March 24, 2022, in the International Journal of Remote Sensing. Access the article via the permanent web address (DOI). (https://doi.org/10.1080/01431161.2022.2044538)
Abstract
Fire metrics such as fire front location and rate of spread (ROS) are critical to understanding the behavior of prescribed fires and wildfires. This paper proposes a new method for prescribed grass fire evolution mapping and ROS measurement using multitemporal thermal orthomosaics collected by a small fixed-wing Unmanned Aircraft System (UAS) at low altitudes. The proposed method provides a low-cost, safe, and effective solution for active grass fire monitoring and fire metric measurement in areas that may be challenging for a typical rotor-wing UAS to cover due to endurance and size constraints.
The proposed method is demonstrated using a prescribed grass fire data set collected by the KHawk fixed-wing UAS over a 13 ha. Kansas tallgrass prairie field on 8 October 2019. Repeat-pass thermal images collected by the KHawk UAS during about 10 min of the burning were grouped and processed to produce multitemporal orthomosaics with a spatial resolution of about 0˜.23 m and a horizontal position error of about 1.5 m. The resulting orthomosaics were further processed for fire front extraction and the measurement of fire front location and ROS.
The head fire ROS of this grass burn was observed to be between 0.2 and 0.4 ms−1 with a mean value of 0.27 ms−1.
Keywords: Unmanned aircraft system (UAS); grass fire monitoring; prescribed grass fire rate of spread; thermal remote sensing; thermal imagery; hazard monitoring
Citation
Gowravaram, Saket, Haiyang Chao, Tiebiao Zhao, Sheena Parsons, Xiaolin Hu, Ming Xin, Harold Flanagan, and Pengzhi Tian. "Prescribed grass fire evolution mapping and rate of spread measurement using orthorectified thermal imagery from a fixed-wing UAS." International Journal of Remote Sensing 43, no. 7 (2022): 2357-2376.