Abstract. Photogrammetry and geosciences have been closely linked since the late 19th century due to the acquisition of high-quality 3-D data sets of the environment, but it has so far been restricted to a limited range of remote sensing specialists because of the considerable cost of metric systems for the acquisition and treatment of airborne imagery. Today, a wide range of commercial and open-source software tools enable the generation of 3-D and 4-D models of complex geomorphological features by geoscientists and other non-experts users. In addition, very recent rapid developments in unmanned aerial vehicle (UAV) technology allow for the flexible generation of high-quality aerial surveying and ortho-photography at a relatively low cost.The increasing computing capabilities during the last decade, together with the development of highperformance digital sensors and the important software innovations developed by computer-based vision and visual perception research fields, have extended the rigorous processing of stereoscopic image data to a 3-D point cloud generation from a series of non-calibrated images. Structure-from-motion (SfM) workflows are based upon algorithms for efficient and automatic orientation of large image sets without further data acquisition information, examples including robust feature detectors like the scale-invariant feature transform for 2-D imagery. Nevertheless, the importance of carrying out well-established fieldwork strategies, using proper camera settings, ground control points and ground truth for understanding the different sources of errors, still needs to be adapted in the common scientific practice.This review intends not only to summarise the current state of the art on using SfM workflows in geomorphometry but also to give an overview of terms and fields of application. Furthermore, this article aims to quantify already achieved accuracies and used scales, using different strategies in order to evaluate possible stagnations of current developments and to identify key future challenges. It is our belief that some lessons learned from former articles, scientific reports and book chapters concerning the identification of common errors or "bad practices" and some other valuable information may help in guiding the future use of SfM photogrammetry in geosciences.

The fragile landscape of the north European loess belt is prone to soil erosion due to soil properties and intense land use of the fertile region. Exact measurement of surface changes with high temporal and spatial resolution over large areas is necessary to quantify and understand rill and interrill erosion processes. High resolution aerial imagery, acquired by an unmanned aerial vehicle (UAV), is used to automatically generate precise digital surface models (DSMs) of high spatial resolution by applying structure‐from‐motion image processing tools. During an investigation period of ten months, a 600 m2 field plot is observed during four field campaigns. A stable reference system is established for multi‐temporal comparison. The overall accuracy of the DSMs generated from UAV images is less than 1 cm, verified by comparison with terrestrial laser scanner (TLS) data. Furthermore, a method for automatic rill extraction and rill parameter calculation is developed, which enables objective rill description with cm‐accuracy and ‐resolution. Soil surface roughness and rill development as well as volumetric quantifications are analysed for multi‐temporal change detection. Surface changes during winter season are controlled by soil consolidation, crusting and sheet erosion. During rainy spring season sheet erosion and rill incision occur. Two thunderstorms in summer season cause dominant rill erosion. Erosion rills are more dominantly deepening than widening (from to 2 to 4 cm depth and from 17 to 23 cm width), resulting in average per rill erosion values of 0.03 and 0.07 m3 respectively. An orientation dependent lateral rill shift is revealed, implying rill widening in eastern direction due to dominant winds from the West. Volumetric quantifications indicate high erosion volumes, reaching up to 121 tha‐1 during the summer events. Highest erosion volumes are due to rill erosion rather than interrill erosion. Copyright © 2014 John Wiley & Sons, Ltd.

As a topographic modelling technique, structure‐from‐motion (SfM) photogrammetry combines the utility of digital photogrammetry with a flexibility and ease of use derived from multi‐view computer vision methods. In conjunction with the rapidly increasing availability of imagery, particularly from unmanned aerial vehicles, SfM photogrammetry represents a powerful tool for geomorphological research. However, to fully realize this potential, its application must be carefully underpinned by photogrammetric considerations, surveys should be reported in sufficient detail to be repeatable (if practical) and results appropriately assessed to understand fully the potential errors involved. To deliver these goals, robust survey and reporting must be supported through (i) using appropriate survey design, (ii) applying suitable statistics to identify systematic error (bias) and to estimate precision within results, and (iii) propagating uncertainty estimates into the final data products. © 2019 John Wiley & Sons, Ltd.

Recent advances in structure from motion (SfM) and dense matching algorithms enable surface reconstruction from unmanned aerial vehicle (UAV) images with high spatial resolution, allowing for new insights into earth surface processes. However, accuracy issues are inherent in parallel-axes UAV image configurations. In this study, the quality of digital elevation models (DEMs) is assessed using images from a simulated UAV flight. Five different SfM tools and three different cameras are compared. If ground control points (GCPs) are not integrated into the adjustment process with parallel-axes image configurations, significant dome-effect systematic errors are observed, which can be reduced based on calibration parameters retrieved from a testfield captured with convergent images immediately before or after the UAV flight. A comparison between DEMs of a soil surface generated from UAV images and terrestrial laserscanning data show that natural surfaces can be very accurately reconstructed from UAV images, even when GCPs are missing and simple geometric camera models are considered.

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