The production of topographic datasets is of increasing interest and application throughout the geomorphic sciences, and river science is no exception. Consequently, a wide range of topographic measurement methods have evolved. Despite the range of available methods, the production of high resolution, high quality digital elevation models (DEMs) requires a significant investment in personnel time, hardware and/or software. However, image-based methods such as digital photogrammetry have been decreasing in costs. Developed for the purpose of rapid, inexpensive and easy three-dimensional surveys of buildings or small objects, the ‘structure from motion’ photogrammetric approach (SfM) is an image-based method which could deliver a methodological leap if transferred to geomorphic applications, requires little training and is extremely inexpensive. Using an online SfM program, we created high-resolution digital elevation models of a river environment from ordinary photographs produced from a workflow that takes advantage of free and open source software. This process reconstructs real world scenes from SfM algorithms based on the derived positions of the photographs in three-dimensional space. The basic product of the SfM process is a point cloud of identifiable features present in the input photographs. This point cloud can be georeferenced from a small number of ground control points collected in the field or from measurements of camera positions at the time of image acquisition. The georeferenced point cloud can then be used to create a variety of digital elevation products. We examine the applicability of SfM in the Pedernales River in Texas (USA), where several hundred images taken from a hand-held helikite are used to produce DEMs of the fluvial topographic environment. This test shows that SfM and low-altitude platforms can produce point clouds with point densities comparable with airborne LiDAR, with horizontal and vertical precision in the centimeter range, and with very low capital and labor costs and low expertise levels. Copyright © 2012 John Wiley & Sons, Ltd.

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Topographic structure from motion: a new development in photogrammetric measurement

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 estricts 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 characterisation 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.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/esp.3609

Mitigating systematic error in topographic models derived from UAV and ground-based image networks

The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV) The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy) The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys Such a comparison served to highlight an average difference in the vertical values of 005 m (RMS = 019 m) However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0011 m) The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0015 m, with RMS = 0220 m Larger values are encountered in areas where sudden changes in topography are present The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology

https://www.mdpi.com/2072-4292/5/12/6880/pdf

Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments

In this study, we present a flexible, cost-effective, and accurate method to monitor landslides using a small unmanned aerial vehicle (UAV) to collect aerial photography. In the first part, we apply a Structure from Motion (SfM) workflow to derive a 3D model of a landslide in southeast Tasmania from multi-view UAV photography. The geometric accuracy of the 3D model and resulting DEMs and orthophoto mosaics was tested with ground control points coordinated with geodetic GPS receivers. A horizontal accuracy of 7 cm and vertical accuracy of 6 cm was achieved. In the second part, two DEMs and orthophoto mosaics acquired on 16 July 2011 and 10 November 2011 were compared to study landslide dynamics. The COSI-Corr image correlation technique was evaluated to quantify and map terrain displacements. The magnitude and direction of the displacement vectors derived from correlating two hillshaded DEM layers corresponded to a visual interpretation of landslide change. Results show that the algorithm can accurately map displacements of the toes, chunks of soil, and vegetation patches on top of the landslide, but is not capable of mapping the retreat of the main scarp. The conclusion is that UAV-based imagery in combination with 3D scene reconstruction and image correlation algorithms provide flexible and effective tools to map and monitor landslide dynamics.

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Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography:

Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry

Stream bathymetry is a critical variable in a number of river science applications. In larger rivers, bathymetry can be measured with instruments such as sonar (single or multi-beam), bathymetric airborne LiDAR (light detection and ranging), or acoustic Doppler current profilers. However, in smaller streams with depths less than 2 m, bathymetry is one of the more difficult variables to map at high-resolution. Optical remote sensing techniques offer several potential solutions for collecting high-resolution bathymetry. In this research, I focus on direct photogrammetric measurements of bathymetry using multi-view stereo photogrammetry, specifically Structure-from-Motion (SfM). The main barrier to accurate bathymetric mapping with any photogrammetric technique is correcting for the refraction of light as it passes between the two different media (air and water), which causes water depths to appear shallower than they are. I propose and test an iterative approach that calculates a series of refraction correction equations for every point/camera combination in a SfM point cloud. This new method is meant to address shortcomings of other correction techniques and works within the current preferred method for SfM data collection, oblique and highly convergent photographs. The multi-camera refraction correction presented here produces bathymetric datasets with accuracies of ~0.02% of the flying height and precisions of ~0.1% of the flying height. This methodology, like many fluvial remote sensing methods, will only work under ideal conditions (e.g. clear water), but it provides an additional tool for collecting high-resolution bathymetric datasets for a variety of river, coastal, and estuary systems. Copyright © 2016 John Wiley & Sons, Ltd.

Bathymetric Structure-from-Motion: extracting shallow stream bathymetry from multi-view stereo photogrammetry

The declining costs of small Unmanned Aerial Systems (sUAS), in combination with Structure-from-Motion (SfM) photogrammetry have triggered renewed interest in image-based topography reconstruction. However, the potential uptake of sUAS-based topography is limited by the need for ground control acquired with expensive survey equipment. Direct georeferencing (DG) is a workflow that obviates ground control and uses only the camera positions to georeference the SfM results. However, the absence of ground control poses significant challenges in terms of the data quality of the final geospatial outputs. Notably, it is generally accepted that ground control is required to georeference, refine the camera calibration parameters, and remove any artefacts of optical distortion from the topographic model. Here, we present an examination of DG carried out with low-cost consumer-grade sUAS. We begin with a study of surface deformations resulting from systematic perturbations of the radial lens distortion parameters. We then test a number of flight patterns and develop a novel error quantification method to assess the outcomes. Our perturbation analysis shows that there exists families of predictable equifinal solutions of K1-K2 which minimize doming in the output model. The equifinal solutions can be expressed as K2 = f (K1) and they have been observed for both the DJI Inspire 1 and Phantom 3 sUAS platforms. This equifinality relationship can be used as an external reliability check of the self-calibration and allow a DG workflow to produce topography exempt of non-affine deformations and with random errors of 0.1% of the flying height, linear offsets below 10 m and off-vertical tilts below 1°. Whilst not yet of survey-grade quality, these results demonstrate that low-cost sUAS are capable of producing reliable topography products without recourse to expensive survey equipment and we argue that direct georeferencing and low-cost sUAS could transform survey practices in both academic and commercial disciplines.

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Cost-effective non-metric photogrammetry from consumer-grade sUAS: implications for direct georeferencing of structure from motion photogrammetry

Riverscape mapping with helicopter-based Structure-from-Motion photogrammetry

https://eartharxiv.org/repository/object/77/download/139/

James T. Dietrich

Papers

Topographic structure from motion: a new development in photogrammetric measurement

Bathymetric Structure-from-Motion: extracting shallow stream bathymetry from multi-view stereo photogrammetry

Cost-effective non-metric photogrammetry from consumer-grade sUAS: implications for direct georeferencing of structure from motion photogrammetry

Riverscape mapping with helicopter-based Structure-from-Motion photogrammetry

Adopting deep learning methods for airborne RGB fluvial scene classification.