Quantifying below-water fluvial geomorphic change: the implications of refraction correction, water surface elevations, and spatially variable error

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

Drones and digital photogrammetry: from classifications to continuums for monitoring river habitat and hydromorphology

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

The accuracy and reliability of traditional surface flow type mapping: Is it time for a new method of characterizing physical river habitat?

Surface flow types (SFTs) are advocated as ecologically relevant hydraulic units, often mapped visually from the bankside to characterize rapidly the physical habitat of rivers. SFT mapping is simple, non-invasive and cost-efficient. However, it is also qualitative, subjective and plagued by difficulties in recording accurately the spatial extent of SFT units. Quantitative validation of the underlying physical habitat parameters is often lacking and does not consistently differentiate between SFTs. Here, we investigate explicitly the accuracy, reliability and statistical separability of traditionally mapped SFTs as indicators of physical habitat, using independent, hydraulic and topographic data collected during three surveys of a c. 50 m reach of the River Arrow, Warwickshire, England. We also explore the potential of a novel remote sensing approach, comprising a small unmanned aerial system (sUAS) and structure-from-motion photogrammetry (SfM), as an alternative method of physical habitat characterization. Our key findings indicate that SFT mapping accuracy is highly variable, with overall mapping accuracy not exceeding 74%. Results from analysis of similarity tests found that strong differences did not exist between all SFT pairs. This leads us to question the suitability of SFTs for characterizing physical habitat for river science and management applications. In contrast, the sUAS–SfM approach provided high resolution, spatially continuous, spatially explicit, quantitative measurements of water depth and point cloud roughness at the microscale (spatial scales ≤1 m). Such data are acquired rapidly, inexpensively and provide new opportunities for examining the heterogeneity of physical habitat over a range of spatial and temporal scales. Whilst continued refinement of the sUAS–SfM approach is required, we propose that this method offers an opportunity to move away from broad, mesoscale classifications of physical habitat (spatial scales 10–100 m) and towards continuous, quantitative measurements of the continuum of hydraulic and geomorphic conditions, which actually exists at the microscale

Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry

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. Whilst 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. 0.02m) for two different river systems over channel lengths of 50- 100m. Errors in submerged areas range from 0.016m to 0.089m, which can be reduced to between 0.008m and 0.053m 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.10m to a few hundred metres)

Processes at the margins of supraglacial debris cover: quantifying dirty ice ablation and debris redistribution

Current glacier ablation models have difficulty simulating the high‐melt transition zone between clean and debris‐covered ice. In this zone, thin debris cover is thought to increase ablation compared to clean ice, but often this cover is patchy rather than continuous. There is a need to understand ablation and debris dynamics in this transition zone to improve the accuracy of ablation models and the predictions of future debris cover extent. To quantify the ablation of partially debris‐covered ice (or ‘dirty ice’), a high‐resolution, spatially continuous ablation map was created from repeat unmanned aerial systems surveys, corrected for glacier flow in a novel way using on‐glacier ablation stakes. Surprisingly, ablation is similar (range ~5 mm w.e. per day) across a wide range of percentage debris covers (~30–80%) due to the opposing effects of a positive correlation between percentage debris cover and clast size, countered by a negative correlation with albedo. Once debris cover becomes continuous, ablation is significantly reduced (by 61.6% compared to a partial debris cover), and there is some evidence that the cleanest ice (<~15% debris cover) has a lower ablation than dirty ice (by 3.7%). High‐resolution feature tracking of clast movement revealed a strong modal clast velocity where debris was continuous, indicating that debris moves by creep down moraine slopes, in turn promoting debris cover growth at the slope toe. However, not all slope margins gain debris due to the removal of clasts by supraglacial streams. Clast velocities in the dirty ice area were twice as fast as clasts within the continuously debris‐covered area, as clasts moved by sliding off their boulder tables. These new quantitative insights into the interplay between debris cover characteristics and ablation can be used to improve the treatment of dirty ice in ablation models, in turn improving estimates of glacial meltwater production

Editorial for the Special Issue “Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge”

River discharge is a fundamental hydrologic quantity that summarizes how a watershed transforms the input of precipitation into output as channelized streamflow [...