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Prescribed fire is an important tool for wildfire management and land management. Simulation of prescribed fires holds great potential in supporting planning of prescribed burn events. This paper presents a simulation-based study of a prescribed fire using data from Unmanned Aircraft System (UAS)-based sensing. A systematic approach for modelling and simulating prescribed fires with dynamic ignitions is developed. The developed approach is applied to a real prescribed fire where a UAS was used to monitor and collect data about the fire. The dynamic ignition process from multiple fire setting teams is specified, and simulation results are compared to real measurement data from UAS-based sensing. The results demonstrate the effectiveness of the developed modelling approach as well as the utility of using UAS-based fire measurements for prescribed fire simulations. [ABSTRACT FROM AUTHOR]

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 m s − 1 with a mean value of 0.27 m s − 1 . [ABSTRACT FROM AUTHOR]

This paper introduces a satellite-based cross-calibration (SCC) method for spectral reflectance estimation of unmanned aircraft system (UAS) multispectral imagery. The SCC method provides a low-cost and feasible solution to convert high-resolution UAS images in digital numbers (DN) to reflectance when satellite data is available. The proposed method is evaluated using a multispectral data set, including orthorectified KHawk UAS DN imagery and Landsat 8 Operational Land Imager Level-2 surface reflectance (SR) data over a forest/grassland area. The estimated UAS reflectance images are compared with the National Ecological Observatory Network's imaging spectrometer (NIS) SR data for validation. The UAS reflectance showed high similarities with the NIS data for the near-infrared and red bands with Pearson's r values being 97 and 95.74, and root-mean-square errors being 0.0239 and 0.0096 over a 32-subplot hayfield. [ABSTRACT FROM AUTHOR]