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Structure-from-motion (SfM) photogrammetry techniques are now widely available to generate digital terrain models (DTMs) from optical imagery, providing an alternative to costlier options such as LiDAR or satellite surveys. SfM could be a useful tool in hazard studies because its minimal cost makes it accessible even in developing regions and its speed of use can provide updated data rapidly in hazard-prone regions. Our study is designed to assess whether crowd-sourced SfM data is comparable to an industry standard LiDAR dataset, demonstrating potential real-world use of SfM if employed for disaster risk reduction purposes. Three groups with variable SfM knowledge utilized 16 different camera models, including four camera phones, to collect 1001 total photos in one hour of data collection. Datasets collected by each group were processed using VisualSFM, and the point densities, accuracies and distributions of points in the resultant point clouds (DTM skeletons) were compared. Our results show that the point clouds are resilient to inconsistency in users' SfM knowledge: crowd-sourced data collected by a moderately informed general public yields topography results comparable in data density and accuracy to those produced with data collected by highly-informed SfM users or experts using LiDAR. This means that in a real-world scenario involving participants with a diverse range of expertise, topography models could be produced from crowd-sourced data quite rapidly and to a very high standard. This could be beneficial to disaster risk reduction as a relatively quick, simple and low-cost method to attain rapidly updated knowledge of terrain attributes, useful for the prediction and mitigation of many natural hazards. [ABSTRACT FROM AUTHOR]

• UAV-SfM soil loss measurements were validated by direct sediment collection. • Photogrammetric precision estimates defined spatially variable levels of detection. • DEMs of difference illustrated the diffuse erosion patterns through time. • Accurate 3-D soil surface models were constructed from UAV-SfM data. Sheet erosion is common on agricultural lands, and understanding the dynamics of the erosive process as well as the quantification of soil loss is important for both soil scientists and managers. However, measuring rates of soil loss from sheet erosion has proved difficult due to requiring the detection of relatively small surface changes over extended areas. Consequently, such measurements have relied on the use of erosion plots, which have limited spatial coverage and have high operating costs. For measuring the larger erosion rates characteristic of rill and gully erosion, structure-from-motion (SfM) photogrammetry has been demonstrated to be a valuable tool. Here, we demonstrate the first direct validation of UAV-SfM measurements of sheet erosion using sediment collection data collected from erosion plots. Three erosion plots (12 m × 4 m) located at Lavras, Brazil, with bare soil exposed to natural rainfall from which event sediment and runoff was monitored, were mapped during two hydrological years (2016 and 2017), using a UAV equipped with a RGB camera. DEMs of difference (DoD) were calculated to detect spatial changes in the soil surface topography over time and to quantify the volumes of sediments lost or gained. Precision maps were generated to enable precision estimates for both DEMs to be propagated into the DoD as spatially variable vertical uncertainties. The point clouds generated from SfM gave mean errors of ~2.4 mm horizontally (xy) and ~1.9 mm vertically (z) on control and independent check points, and the level of detection (LoD) along the plots ranged from 1.4 mm to 7.4 mm. The soil loss values obtained by SfM were significantly (p < 0.001) correlated (r2 = 95.55%) with those derived from the sediment collection. These results open up the possibility to use SfM for erosion studies where channelized erosion is not the principal mechanism, offering a cost-effective method for gaining new insights into sheet, and interrill, erosion processes. [ABSTRACT FROM AUTHOR]