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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]

This paper presents the design, implementation, and evaluation of four filters for the estimation of angle of attack (AOA) and angle of sideslip (AOS) of small unmanned aerial vehicles (UAVs). Specifically, two novel filters (a complementary filter and an extended Kalman filter) are proposed and evaluated without using direct flow angle and Global Positioning System measurements; two existing AOA/AOS filters are also implemented and evaluated. All filters are designed with minimal inputs and states to ensure the ease of implementation, simplicity of tuning, and computation efficiency. Both simulation and UAV flight test results show the performance of the proposed filters. Especially, flight test results from two different UAVs (a T-tail UAV and a flying wing UAV) show that the root mean square errors of estimated inertial AOA and AOS are less than 1.5 $^\circ$ under nominal flight conditions and around 2 $^\circ$ under aggressive maneuvers compared with direct flow angle measurements. [ABSTRACT FROM AUTHOR]