ALBERT

Description: Unmanned Aerial Vehicles (UAVs) have revolutionised the availability of high resolution topographic data in many disciplines due to their relatively low-cost and ease of deployment. Consumer-grade Real Time Kinematic Global Navigation Satellite System (RTK-GNSS) equipped UAVs offer potential to reduce or eliminate ground control points (GCPs) from SfM photogrammetry surveys, removing time-consuming target deployment. Despite this, the removal of ground control can substantially reduce the georeferencing accuracy of SfM photogrammetry outputs. Here, a DJI Phantom 4 RTK UAV is deployed to survey a 2 × 0.5 km reach of the braided River Feshie, Scotland that has local channel-bar relief of c.1 m and median grain size c.60 mm. Five rectangular adjacent blocks were flown, with images collected at 20° from the nadir across a double grid, with strips flown in opposing directions to achieve locally convergent imagery geometry. Check point errors for seven scenarios with varying configurations of GCPs were tested. Results show that, contrary to some published Direct Georeferencing UAV investigations, GCPs are not essential for accurate kilometre-scale topographic modelling. Using no GCPs, 3300 independent spatially-distributed RTK-GNSS surveyed check points have mean z-axis error −0.010 m (RMSE = 0.066 m). Using 5 GCPs gave 0.016 m (RMSE = 0.072 m). Our check point results do not show vertical systematic errors, such as doming, using either 0 or 5 GCPs. However, acquiring spatially distributed independent check points to check for systematic errors is recommended. Our results imply that an RTK-GNSS UAV can produce acceptable errors with no ground control, alongside spatially distributed independent check points, demonstrating that the technique is versatile for rapid kilometre-scale topographic survey in a range of geomorphic environments.

Description: Quantifying sedimentary deposits is crucial to fully test generic trends cited within facies models. To date, few studies have quantified downstream trends alongside vertical and lateral variations within distributive fluvial systems (DFS), with most studies reporting qualitative trends. This study reports on the generation of a quantitative dataset on the Huesca DFS, Ebro Basin, Spain, in which downstream, vertical and lateral trends in channel characteristics are analyzed using a fusion of field data and virtual outcrop model derived data (VOM). Vertical trend analysis reveals that the exposed portion of the Huesca DFS does not show any systematic changes through time, which suggests autogenic-driven local variability. Proximal-to-distal trends from field data display a downstream decrease in average channel body thicknesses (13.1–0.7 m), channel deposit percentage (70–4%), and average storey thicknesses (5.2–0.7 m) and confirm trends observed on other DFS. The VOM dataset shows a similar downstream trend in all characteristics. The range in values are, however, larger due to the increase in amount of data that can be collected, and trends are thus less clear. This study therefore highlights that standard field techniques do not capture the variability that can be present in outcrops. Channel percentage was found to be most variable (37% variation) in the medial setting, whereas channel body thickness is most variable (∼15 m range) in the proximal setting. Storey thickness varied in both the proximal and medial settings (range of 9 and 11 m for field and VOM data respectively) becoming more consistent downstream. Downstream shifts in architecture are also noted from massive, highly amalgamated channel-body sandstones in proximal regions to isolated or offset-stacked channel-bodies dominating the distal region. Trends are explained by spatial variability in DFS processes and preservation potential. The overlap present indicates that no single value is representative of position within a DFS, which has important implications for interpreting the location that a data point sits within a DFS when using limited (i.e., single log) datasets. These comparative results contribute to improving the accuracy of system-scale downstream predictions for channel characteristic variability within subsurface deposits.