The geometrical accuracy of georeferenced digital surface models (DTM) obtained from images captured by micro-UAVs and processed by using structure from motion (SfM) photogrammetry depends on several factors, including flight design, camera quality, camera calibration, SfM algorithms and georeferencing strategy. This paper focusses on the critical role of the number and location of ground control points (GCP) used during the georeferencing stage. A challenging case study involving an area of 1200+ ha, 100+ GCP and 2500+ photos was used. Three thousand, four hundred and sixty-five different combinations of control points were introduced in the bundle adjustment, whilst the accuracy of the model was evaluated using both control points and independent check points. The analysis demonstrates how much the accuracy improves as the number of GCP points increases, as well as the importance of an even distribution, how much the accuracy is overestimated when it is quantified only using control points rather than independent check points, and how the ground sample distance (GSD) of a project relates to the maximum accuracy that can be achieved.

We test the acquisition of high‐resolution topographic and terrain data using hand‐held smartphone technology, where the acquired images can be processed using technology freely available to the research community. This is achieved by evaluating the quality of digital terrain models (DTM) of a river bank and an Alpine alluvial fan generated with a fully automated, free‐to‐use, structure‐from‐motion package and a smartphone integrated camera (5 megapixels) with terrestrial laser scanning (TLS) data used to provide a benchmark. To evaluate this approach a 16.2‐megapixel digital camera and an established, commercial, close‐range and semi‐automated software are also employed, and the product of the four combinations of the two types of cameras and software are compared. Results for the river bank survey demonstrate that centimetre‐precision DTMs can be achieved at close range (10 m or less), using a smartphone camera and a fully automated package. Results improve to sub‐centimetre precision with either higher‐resolution images or by applying specific post‐processing techniques to the smartphone DTMs. Application to an entire Alpine alluvial fan system shows the degradation of precision scales linearly with image scale, but that (i) the expected level of precision remains and (ii) difficulties in separating vegetation and sediment cover within the results are similar to those typically found when using other photo‐based techniques and laser scanning systems. Copyright © 2014 John Wiley & Sons, Ltd.

Developments in digital photogrammetry have provided geomorphologists with an automated tool to generate digital elevation models (DEMs) at exceedingly high densities. Although such software tools are available at low cost and run on far cheaper hardware than previous generations of photogrammetric instrumentation, some expertise is still required to derive accurate data. Various recommendations are provided that should enable the inexperienced user to make effective use of digital photogrammetry. Key issues discussed include the role of photo-control, the significance of checkpoints in the object space, and the importance of camera calibration data. An overview of self-calibration methods is provided, which is valuable in situations when a non-photogrammetric camera has been used. Accurate camera modelling also affects DEM quality and this issue is examined through assessing the impact of DEM inaccuracies upon derived data used for geomorphological enquiry. Although the automated software packages are designed primarily for use with satellite imagery or vertical aerial photography, it is explained how such software can be used for both close range and oblique imagery. The procedure requires rotating the object coordinate system defined by the photo-control points and the mathematics required to achieve this is provided. The procedure is used to derive the morphology of a soil surface using oblique, close-range imagery and demonstrates the effectiveness of this particular approach. Copyright © 1999 John Wiley & Sons, Ltd.

This paper describes and tests a method for the automated extraction of grain-size data from digital imagery. It combines two basic image processing methods for this purpose: grey-scale thresholding to create a binary image and watershed segmentation to grow edges on the binary image to allow the identification of individual grains. The method is subject to rigorous testing in terms of edge detection and automatic measurement of grain-size information from the edge images, and is also compared with the results obtained from simple direct clast sampling. The edge detection methods are tested with respect to manually-identified edges. This suggests that simple thresholding of raw imagery produces grain-size estimates that are: (i) in excellent agreement with manual estimates, above a critical particle size defined by the scale of the photography; (ii) downgraded with the inclusion of additional edge information from analysis of high resolution digital elevation models (DEMs); and (iii) not affected by the use of raw imagery as opposed to imagery that has been rectified to deal with geometric, tilt and relief distortion effects. The automated ellipse-based measurement method is shown to produce a good estimate of two-dimensional a- and b- axes as they appear as long and short axes on the edge images. Thus, the research shows that it can be used to map and quantify very rapidly spatial variations in grain-size characteristics, although it cannot deal with the longrecognised problem of the relationship between two-dimensional planform grain-size estimates and actual a- and b- axes obtained by direct grain sampling.