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Unmanned aerial vehicles (UAVs) and structure‐from‐motion photogrammetry enable detailed quantification of geomorphic change. However, rigorous precision‐based change detection can be compromised by survey accuracy problems producing systematic topographic error (e.g. 'doming'), with error magnitudes greatly exceeding precision estimates. Here, we assess survey sensitivity to systematic error, directly correcting topographic data so that error magnitudes align more closely with precision estimates. By simulating conventional grid‐style photogrammetric aerial surveys, we quantify the underlying relationships between survey accuracy, camera model parameters, camera inclination, tie point matching precision and topographic relief, and demonstrate a relative insensitivity to image overlap. We show that a current doming‐mitigation strategy of using a gently inclined (<15°) camera can reduce accuracy by promoting a previously unconsidered correlation between decentring camera lens distortion parameters and the radial terms known to be responsible for systematic topographic error. This issue is particularly relevant for the wide‐angle cameras often integrated into current‐generation, accessible UAV systems, frequently used in geomorphic research. Such systems usually perform on‐board image pre‐processing, including applying generic lens distortion corrections, that subsequently alter parameter interrelationships in photogrammetric processing (e.g. partially correcting radial distortion, which increases the relative importance of decentring distortion in output images). Surveys from two proglacial forefields (Arolla region, Switzerland) showed that results from lower‐relief topography with a 10°‐inclined camera developed vertical systematic doming errors > 0·3 m, representing accuracy issues an order of magnitude greater than precision‐based error estimates. For higher‐relief topography, and for nadir‐imaging surveys of the lower‐relief topography, systematic error was < 0·09 m. Modelling and subtracting the systematic error directly from the topographic data successfully reduced error magnitudes to values consistent with twice the estimated precision. Thus, topographic correction can provide a more robust approach to uncertainty‐based detection of event‐scale geomorphic change than designing surveys with small off‐nadir camera inclinations and, furthermore, can substantially reduce ground control requirements. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd [ABSTRACT FROM AUTHOR]

Relict landforms provide a wealth of information on the evolution of the modern landscape and climate change in the past. To improve understanding of the origin and development of these landforms we need better spatial measurements across a variety of scales. This can be challenging using conventional surveying techniques due to difficulties in landform recognition on the ground (e.g. weak visual/topographic expression) and spatially variable areas of interest. Here we explore the appropriateness of existing remote sensing datasets (aerial LiDAR and aerial photography) and newly acquired unmanned aerial vehicle (UAV) imagery of a test site on the upland of Dartmoor in SW England (Leeden Tor) for the recognition and automated mapping of relict patterned ground composed of stripes and polygons. We find that the recognition of these landforms is greatly enhanced by automated mapping using spectral two-dimensional imagery. Image resolution is important, with the recognition of elements (boulders) of <1 m maximised from the highest resolution imagery (UAV red-green-blue (RGB)) and recognition of landforms (10–100 m scale) maximised on coarser resolution aerial imagery. Topographic metrics of these low relief (0.5 m) landforms are best extracted from structure-from-motion (SfM) processed UAV true-colour imagery, and in this context the airborne LiDAR data proved less effective. Integrating automated mapping using spectral attributes and SfM-derived digital surface models from UAV RGB imagery provides a powerful tool for rapid reconnaissance of field sites to facilitate the extraction of meaningful topographic and spatial metrics that can inform on the origin of relict landform features. Care should be given to match the scale of features under consideration to the appropriate scale of datasets available. [ABSTRACT FROM AUTHOR]

Structure-from-motion (SfM) photogrammetry is revolutionising the collection of detailed topographic data, but insight into geomorphological processes is currently restricted by our limited understanding of SfM survey uncertainties. Here, we present an approach that, for the first time, specifically accounts for the spatially variable precision inherent to photo-based surveys, and enables confidence-bounded quantification of 3D topographic change. The method uses novel 3D precision maps that describe the 3D photogrammetric and georeferencing uncertainty, and determines change through an adapted state-of-the-art fully 3D point-cloud comparison (M3C2), which is particularly valuable for complex topography. We introduce this method by: (1) using simulated UAV surveys, processed in photogrammetric software, to illustrate the spatial variability of precision and the relative influences of photogrammetric (e.g. image network geometry, tie point quality) and georeferencing (e.g. control measurement) considerations; (2) we then present a new Monte Carlo procedure for deriving this information using standard SfM software and integrate it into confidence-bounded change detection; before (3) demonstrating geomorphological application in which we use benchmark TLS data for validation and then estimate sediment budgets through differencing annual SfM surveys of an eroding badland. We show how 3D precision maps enable more probable erosion patterns to be identified than existing analyses, and how a similar overall survey precision could have been achieved with direct survey georeferencing for camera position data with precision half as good as the GCPs'. Where precision is limited by weak georeferencing (e.g. camera positions with multi-metre precision, such as from a consumer UAV), then overall survey precision can scale as n -½ of the control precision ( n = number of images). Our method also provides variance-covariance information for all parameters. Thus, we now open the door for SfM practitioners to use the comprehensive analyses that have underpinned rigorous photogrammetric approaches over the last half-century. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT High resolution digital elevation models (DEMs) are increasingly produced from photographs acquired with consumer cameras, both from the ground and from unmanned aerial vehicles (UAVs). However, although such DEMs may achieve centimetric detail, they can also display systematic broad-scale error that restricts their wider use. Such errors which, in typical UAV data are expressed as a vertical 'doming' of the surface, result from a combination of near-parallel imaging directions and inaccurate correction of radial lens distortion. Using simulations of multi-image networks with near-parallel viewing directions, we show that enabling camera self-calibration as part of the bundle adjustment process inherently leads to erroneous radial distortion estimates and associated DEM error. This effect is relevant whether a traditional photogrammetric or newer structure-from-motion (SfM) approach is used, but errors are expected to be more pronounced in SfM-based DEMs, for which use of control and check point measurements are typically more limited. Systematic DEM error can be significantly reduced by the additional capture and inclusion of oblique images in the image network; we provide practical flight plan solutions for fixed wing or rotor-based UAVs that, in the absence of control points, can reduce DEM error by up to two orders of magnitude. The magnitude of doming error shows a linear relationship with radial distortion and we show how characterization of this relationship allows an improved distortion estimate and, hence, existing datasets to be optimally reprocessed. Although focussed on UAV surveying, our results are also relevant to ground-based image capture. Copyright © 2014 John Wiley & Sons, Ltd. [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]