Quantifying the topography of rivers and their associated bedforms has been a fundamental concern of fluvial geomorphology for decades. Such data, acquired at high temporal and spatial resolutions, are increasingly in demand for process‐oriented investigations of flow hydraulics, sediment dynamics and in‐stream habitat. In these riverine environments, the most challenging region for topographic measurement is the wetted, submerged channel. Generally, dry bed topography and submerged bathymetry are measured using different methods and technology. This adds to the costs, logistical challenges and data processing requirements of comprehensive river surveys. However, some technologies are capable of measuring the submerged topography. Through‐water photogrammetry and bathymetric LiDAR are capable of reasonably accurate measurements of channel beds in clear water. While the cost of bathymetric LiDAR remains high and its resolution relatively coarse, the recent developments in photogrammetry using Structure from Motion (SfM) algorithms promise a fundamental shift in the accessibility of topographic data for a wide range of settings. Here we present results demonstrating the potential of so called SfM‐photogrammetry for quantifying both exposed and submerged fluvial topography at the mesohabitat scale. We show that imagery acquired from a rotary‐winged Unmanned Aerial System (UAS) can be processed in order to produce digital elevation models (DEMs) with hyperspatial resolutions (c. 0.02 m) for two different river systems over channel lengths of 50–100 m. Errors in submerged areas range from 0.016 m to 0.089 m, which can be reduced to between 0.008 m and 0.053 m with the application of a simple refraction correction. This work therefore demonstrates the potential of UAS platforms and SfM‐photogrammetry as a single technique for surveying fluvial topography at the mesoscale (defined as lengths of channel from c.10 m to a few hundred metres). Copyright © 2014 John Wiley & Sons, Ltd.

Summary 1. Physical habitat is the living space of instream biota; it is a spatially and temporally dynamic entity determined by the interaction of the structural features of the channel and the hydrological regime. 2. This paper reviews the need for physical habitat assessment and the range of physical habitat assessment methods that have been developed in recent years. These methods are needed for assessing improvements made by fishery enhancement and river restoration procedures, and as an intrinsic element of setting environmental flows using instream flow methods. Consequently, the assessment methods must be able to evaluate physical habitat over a range of scales varying from the broad river segment scale (up to hundreds of kilometres) down to the microhabitat level (a few centimetres). 3. Rapid assessment methods involve reconnaissance level surveys (such as the habitat mapping approach) identifying, mapping and measuring key habitat features over long stretches of river in a relatively short space of time. More complex appraisals, such as the Physical Habitat Simulation System (PHABSIM), require more detailed information on microhabitat variations with flow. 4. Key research issues relating to physical habitat evaluation lie in deciding which levels of detail are appropriate for worthwhile yet cost‐effective assessment, and in determining those features that are biologically important and hence can be considered habitat features rather than simple geomorphic features. 5. The development of new technologies particularly relating to survey methods should help improve the speed and level of detail attainable by physical habitat assessments. These methods will provide the necessary information required for the development of the two‐and three‐dimensional physical and hydraulic habitat models. 6. A better understanding of the ways in which the spatial and temporal dynamics of physical habitat determine stream health, and how these elements can be incorporated into assessment methods, remains a key research goal.

River bank erosion occurs primarily through a combination of three mechanisms: mass failure, fluvial entrainment, and subaerial weakening and weathering. Subaerial processes are often viewed as 'preparatory' processes, weakening the bank face prior to fluvial erosion. Within a river basin downstream process 'domains' occur, with subaerial processes dominating the upper reaches, fluvial erosion the middle, and mass failure the lower reaches of a river. The aim of this paper is to demonstrate that (a) subaerial processes may be underestimated as an erosive agent, and (b) process dominance has a temporal, as well as spatial, aspect.Bank erosion on the River Arrow, Warwickshire, UK, was monitored for 16 months (December 1996 to March 1998) using erosion pins. Variations in the rate and aerial extent of erosion are considered with reference to meteorological data. Throughout the first 15 months all erosion recorded was subaerial, resulting in up to 181 mm a À1 of bank retreat, compared with 13 to 27 mm a À1 reported by previous researchers. While the role of subaerial processes as 'preparatory' is not contended, it is suggested that such processes can also be erosive.The three bank erosion mechanisms operate at different levels of magnitude and frequency, and the River Arrow data demonstrate this. Thus the concept of process dominance has a temporal, as well as spatial aspect, particularly over the short time-periods often used for studying processes in the field. Perception of the relative efficacy of each erosive mechanism will therefore be influenced by the temporal scale at which the bank is considered. With the advent of global climate change, both these magnitude-frequency characteristics and the consequent interaction of bank erosion mechanisms may alter. It is therefore likely that recognition of this temporal aspect of process dominance will become increasingly important to studies of bank erosion processes.

Recently, we have gained the opportunity to obtain very high-resolution imagery and topographic data of rivers using drones and novel digital photogrammetric processing techniques. The high-resolution outputs from this method are unprecedented, and provide the opportunity to move beyond river habitat classification systems, and work directly with spatially explicit continuums of data. Traditionally, classification systems have formed the backbone of physical river habitat monitoring for their ease of use, rapidity, cost efficiency, and direct comparability. Yet such classifications fail to characterize the detailed heterogeneity of habitat, especially those features which are small or marginal. Drones and digital photogrammetry now provide an alternative approach for monitoring river habitat and hydromorphology, which we review here using two case studies. First, we demonstrate the classification of river habitat using drone imagery acquired in 2012 of a 120 m section of the San Pedro River in Chile, which was at the technological limits of what could be achieved at that time. Second, we review how continuums of data can be acquired, using drone imagery acquired in 2016 from the River Teme in Herefordshire, England. We investigate the precision and accuracy of these data continuums, highlight key current challenges, and review current best practices of data collection, processing, and management. We encourage further quantitative testing and field applications. If current difficulties can be overcome, these continuums of geomorphic and hydraulic information hold great potential for providing new opportunities for understanding river systems to the benefit of both river science and management. © 2017 The Authors. WIREs Water published by Wiley Periodicals, Inc. How to cite this article:WIREs Water 2017, 4:e1222. doi: 10.1002/wat2.1222 INTRODUCTIONM onitoring the spatial and temporal variation in physical river parameters is important for understanding and improving habitat quality and distribution, especially with respect to the potential impacts of climate change. [1][2][3] Remote sensing based methods have long played a role in surveying and This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.monitoring physical river habitat and hydromorphology. 4,5 A growing body of literature demonstrates the use of digital photogrammetry 6,7 and spectraldepth correlations [8][9][10] for quantifying fluvial topography and flow depth, the computation of image textural variables and roughness of terrestrial laser scanner point clouds for quantifying fluvial substrate size, [11][12][13][14][15] and the use of multispectral imagery for mapping hydrogeomorphic units. 16 These developments have made important contributions to our abilities to map and measure physical river habitat parameters. However, few of these approaches are ca...

Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s − 1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s − 1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s − 1 of the ADCP measurements, on average.