Optimization of UAVs-SfM data collection in aeolian landform morphodynamics: a case study from the Gonghe Basin, China

UAVs-SfM (unmanned aerial vehicles-structure-from-motion) systems can generate high-resolution three-dimensional (3D) topographic models of aeolian landforms. To explore the optimization of UAVs-SfM for use in aeolian landform morphodynamics, this study tested flight parameters for two contrasting aeolian landform areas (free dune and blowout) to assess the 3D reconstruction accuracy of the UAV survey compared with field point measurements using differential RTK-GPS (real-time kinematic-global positioning system). The results reveal the optimum UAVs-SfM flight set-up at the free-dune site was: flying height = 74m, camera tilt angle = -90 degrees, photograph overlap ratio = 85%/70% (heading/sideways). The horizontal/vertical location error was around 0.028-0.055m and 0.053-0.069m, respectively, and a point cloud density of 463/m(3) was found to generate a clear texture using these flying parameters. For the < 20m deep blowout the optimum set-up with highest accuracy and the lowest cliff texture distortion was: flying height = 74m combined camera tilt angle = -90 degrees and -60 degrees, photograph overlap ratio = 85%/70% (heading/sideways), and an evenly distributed GCPs (ground control points) density of 42/km(2) using these flying parameters. When the depth of the blowouts exceeded 40m, the optimum flight/survey parameters changed slightly to account for more challenging cliff texture generation: flying height = 80m (with -90 degrees and -60 degrees combined camera tilt angle), GCPs density = 63/km(2) to generate horizontal and vertical location error of 0.024m and 0.050m, respectively, and point cloud density of 2597.11/m(3). The main external factors that affect the successful 3D reconstruction of aeolian landforms using UAVs-SfM are the weather conditions, manipulation errors, and instrument system errors. The UAVs-SfM topographic monitoring results demonstrate that UAVs provide a viable and robust means for aeolian landform morphodynamics monitoring. Importantly, the rapid and high precision 3D reconstruction processes were significantly advanced using the optimal flight parameters reported here. (C) 2020 John Wiley & Sons, Ltd.