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Much of the geomorphic work of rivers occurs underwater. As a result, high resolutionquantification of geomorphic change in these submerged areas is important. Currently, to quantify thischange, multiple methods are required to get high resolution data for both the exposed and submergedareas. Remote sensing methods are often limited to the exposed areas due to the challenges imposedby the water, and those remote sensing methods for below the water surface require the collection ofextensive calibration data in-channel, which is time-consuming, labour-intensive, and sometimesprohibitive in dicult-to-access areas. Within this paper, we pioneer a novel approach for quantifyingabove- and below-water geomorphic change using Structure-from-Motion photogrammetry andinvestigate the implications of water surface elevations, refraction correction measures, and thespatial variability of topographic errors. We use two epochs of imagery from a site on the River Teme,Herefordshire, UK, collected using a remotely piloted aircraft system (RPAS) and processed usingStructure-from-Motion (SfM) photogrammetry. For the first time, we show that: (1) Quantification ofsubmerged geomorphic change to levels of accuracy commensurate with exposed areas is possiblewithout the need for calibration data or a dierent method from exposed areas; (2) there is minimaldierence in results produced by dierent refraction correction procedures using predominantlynadir imagery (small angle vs. multi-view), allowing users a choice of software packages/processingcomplexity; (3) improvements to our estimations of water surface elevations are critical for accuratetopographic estimation in submerged areas and can reduce mean elevation error by up to 73%;and (4) we can use machine learning, in the form of multiple linear regressions, and a Gaussian NaïveBayes classifier, based on the relationship between error and 11 independent variables, to generate ahigh resolution, spatially continuous model of geomorphic change in submerged areas, constrained byspatially variable error estimates. Our multiple regression model is capable of explaining up to 54%of magnitude and direction of topographic error, with accuracies of less than 0.04 m. With on-goingtesting and improvements, this machine learning approach has potential for routine application inspatially variable error estimation within the RPAS–SfM workflow.

[...]the traditional, in situ stream gage networks that provide such data are sparse and declining, even in developed nations, and absent in many parts of the world (e.g., [1]). [...]establishing and maintaining these gages is expensive, labor-intensive, and can place personnel at risk (e.g., [2]). Measuring surface flow velocities via various non-contact methods [3,7,8] Mapping water depth using both active and passive remote sensing approaches [4,8,9] Deriving estimates of river discharge from various types of remotely sensed data [7,8,10] Characterizing flow frequency and flooding using image-derived data products [11,12] Applying remote sensing techniques to characterize flow-related spatial and temporal heterogeneity of key river attributes [5,6] In addition to this special issue focused on discharge and its components, we also want to direct the reader’s attention to another special issue on “Remote Sensing of Large Rivers” published in Remote Sensing in 2020. Radar-based measurements of surface flow velocity were made at 10 U.S. Geological Survey (USGS) gaging stations and a probability concept used to estimate the cross-sectional mean velocity on the basis of a single surface velocity measurement at the cross-channel location where the maximum velocity occurs. Kinzel and Legleiter [8] used a small unmanned aircraft system (sUAS) equipped with a cooled, mid-wave infrared camera to acquire thermal image time series from which surface flow velocities were inferred via particle image velocimetry (PIV).

The starting point for effective environmental management is an appropriate level of understanding of the current state of the environment. This has typically required on-site assessment or investigation. Such activities, including walkover surveys, which provide vital information, can be very resource- and/or labour-intensive. This then reduces the resources available to implement measures to remediate, protect, and improve the environment. In this context, there are several current and potential applications of Unmanned Aerial Vehicles (UAVs) that will help in the management of river catchments to better protect and improve these environments for people and wildlife. These could be used routinely by practitioners to increase their effectiveness when carrying out their environmental assessment and management work. UAVs can also be used in combination with other remote sensing technologies, Geographical Information Systems (GIS), modelling, and visualisation approaches to further extend their usefulness. Within this chapter, we aim to give an insight into the use of UAVs for fluvial applications. We review current data collection and processing considerations and explore examples of UAV use for riverine feature detection and mapping. Finally, we demonstrate the quantification of physical river parameters from UAV data.