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

Abstract

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 setup at the free-dune site was: flying height ¼ 74m, camera tilt angle ¼ À90°, photograph overlap ratio ¼ 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 setup with highest accuracy and the lowest cliff texture distortion was: flying height ¼ 74m combined camera tilt angle ¼ À90°and À60°, photograph overlap ratio ¼ 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 ¼ 80m (with À90°and À60°combined camera tilt angle), GCPs density ¼ 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.

Evolution of blowouts in artificial foredunes on Pingtan Island, China

“…The morphological changes of blowouts are reflected in the expansion of their edges, the erosion of the bases of their deflation basins, and the deposition of downwind depositional lobes (Luo et al, 2020). Therefore, we selected blowouts at different stages to analyse axes and erosional walls (Figure 9), with the overall δ s values for each measuring point on the central axis of blowouts A, B, and C negatively correlated with the wind‐speed variation factor ( F s ) and the wind‐direction stability factor (SD wd ), while the δ s , SD wd , and F s values on both lateral walls of the blowouts fluctuated notably (Figure 9).…”

Blowout Morphometrics and Mass Balances

“…SfM optical drone mapping was combined with GPR surveys to provide a high-resolution, non-invasive approach that leaves the fragile landscape undisturbed. We extend the technical work of Luo et al (2020) to provide geomorphometry and geospatial analysis of the Gonghe Basin using a consistent suit of SfM data. The fieldwork for this study was conducted in July 2018.…”

“…The mega-blowouts of the QTP are some of the largest on Earth (Luo et al, 2020) and are themselves part of a wider sand flow system from the Gonghe Basin to the Yellow River valley. The Gonghe Basin (Figure 1) covers an area of 13,800 km 2 (Liu et al, 2013) and is a northeast to southwest aligned endorheic basin flanked by the Qinghai Nanshan mountains to the north and the Wahongshan Mountains to the south.…”

“…The larger, more complex blowouts D and E are close to the interface between the blowouts and the active sand dunes and appear to be developed within vegetation-stabilised parabolic dunes. Blowouts B, D, and E were previously studied by Luo et al (2019a), who looked at their development and growth from 1967 to 2015 with historical satellite imagery, and monitored the expansion of their erosional hollows with annual surveys from 2015 to 2018 as well as drone images (Luo et al, 2020). Here we present updated morphometrics for these blowouts using finer-resolution DEM reconstructions and imaging of the internal structures within depositional lobes.…”

Terrestrial and Airborne Structure from Motion Photogrammetry Applied for Change Detection within a Sinkhole in Thuringia, Germany

“…However, in densely vegetated areas (dense forest, bushes, or high grass), SfM-MVS can only provide points of the uppermost surface from the sensor's viewpoint, resulting in a surface model rather than a terrain model that is more easily derived from laser scanning data [4,12]. Finally, SfM has been applied at different scales, landscapes, and landforms such as volcanoes and lava movements, large planar regions, glaciers, rock glaciers, badlands, sinkholes, landslides, rivers, burned and aeolian landscapes, as well as at laboratory experiments to detect and analyze surface changes (e.g., [2,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]).…”