Impact of three-dimensional attitude variations of an unmanned aerial vehicle magnetometry system on magnetic data quality

Optically pumped vapour magnetometers have an orientation dependency in measuring the scalar component of the ambient magnetic field which leads to challenges for integration with mobile platforms. Quantifying the three-dimensional attitude variations (yaw, pitch and roll) of an optically pumped vapour magnetometer, while in-flight and suspended underneath a rotary unmanned aerial vehicle, aids in the successful development of reliable, high-resolution unmanned aerial vehicle magnetometry surveys. This study investigates the in-flight three-dimensional attitude characteristics of a GEM Systems Inc. GSMP-35U potassium vapour magnetometer suspended 3 m underneath a Dà-Jiāng Innovations S900 multi-rotor unmanned aerial vehicle. A series of unmanned aerial vehicle-borne attitude surveys quantified the three-dimensional attitude variations that a simulated magnetometer payload experienced while freely (or semi-rigidly) suspended underneath the unmanned aerial vehicle in fair weather. Analysis of the compiled yaw, pitch and roll data resulted in the design of a specialized semi-rigid magnetometer mount, implemented to limit magnetometer rotation about the yaw axis. A subsequent unmanned aerial vehicle-borne magnetic survey applying this specialized mount resulted in more than 99% of gathered GSMP-35U magnetic data being within industry standards. Overall, this study validates that maintaining magnetometer attitude variations within quantified limits (±5° yaw, ±10° pitch and roll) during flight can yield reliable, continuous and high-resolution unmanned aerial vehicle-borne magnetic measurements.     

Recording inter-annual changes on a boulder beach in Galicia,NW Spain using an unmanned aerial vehicle

An unmanned aerial vehicle (UAV) was flown over a boulder beach (area 20 000 m²) on the southern coast of Galicia (northwestern Spain) in May 2016, continuing earlier work based on flights over the same beach in July 2012, May 2013, and late March 2014. Digital surface models (DSMs) with 1.8 cm resolution were constructed from the 2014 and 2016 data to identify changes in beach morphology over the intervening period. Analyses were conducted using a Limit of Detection (LoD) of 0 cm and 3.71 cm. In both cases, the analyses showed that erosion dominated over 19% of the beach area. Accretion occurred over the rest of the beach, which acquired an additional 1500 m³ of material over the study period. Re-analysis of the data from earlier flights suggested that erosion dominated on the beach in 2012–2013 and deposition in 2013–2014. Without any clear relationship between beach behaviour and storm severity during each winter period, it is proposed that gravitationally induced erosion and storm-wave induced deposition are the result of perturbations about an equilibrium beach gradient. The UAV data also suggested that an essentially random component modulates regional patterns of movement. © 2018 John Wiley & Sons, Ltd.     

Quantifying snow water equivalent using terrestrial ground penetrating radar and unmanned aerial vehicle photogrammetry

This study demonstrates the potential value of a combined unmanned aerial vehicle (UAV) Photogrammetry and ground penetrating radar (GPR) approach to map snow water equivalent (SWE) over large scales. SWE estimation requires two different physical parameters (snow depth and density), which are currently difficult to measure with the spatial and temporal resolution desired for basin-wide studies. UAV photogrammetry can provide very high-resolution spatially continuous snow depths (SD) at the basin scale, but does not measure snow densities. GPR allows nondestructive quantitative snow investigation if the radar velocity is known. Using photogrammetric snow depths and GPR two-way travel times (TWT) of reflections at the snow-ground interface, radar velocities in snowpack can be determined. Snow density (RSN) is then estimated from the radar propagation velocity (which is related to electrical permittivity of snow) via empirical formulas. A Phantom-4 Pro UAV and a MALA GX450 HDR model GPR mounted on a ski mobile were used to determine snow parameters. A snow-free digital surface model (DSM) was obtained from the photogrammetric survey conducted in September 2017. Then, another survey in synchronization with a GPR survey was conducted in February 2019 whilst the snowpack was approximately at its maximum thickness. Spatially continuous snow depths were calculated by subtracting the snow-free DSM from the snow-covered DSM. Radar velocities in the snowpack along GPR survey lines were computed by using UAV-based snow depths and GPR reflections to obtain snow densities and SWEs. The root mean square error of the obtained SWEs (384 mm average) is 63 mm, indicating good agreement with independent SWE observations and the error lies within acceptable uncertainty limits.