Foreword 1. Introduction 2. Runoff, erosion and delivery to the coastal ocean 3. Temporal variations 4. Human impacts Appendices. Global River Database: Appendix A: North and Central America Appendix B: South America Appendix C: Europe Appendix D: Africa Appendix E: Eurasia Appendix F: Asia Appendix G: Oceania References Index.

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River Discharge to the Coastal Ocean: A Global Synthesis

Accurate, precise and rapid acquisition of topographic data is fundamental to many sub-disciplines of physical geography. Technological developments over the past few decades have made fully distributed data sets of centimetric resolution and accuracy commonplace, yet the emergence of Structure from Motion (SfM) with Multi-View Stereo (MVS) in recent years has revolutionised three-dimensional topographic surveys in physical geography by democratising data collection and processing. SfM-MVS originates from the fields of computer vision and photogrammetry, requires minimal expensive equipment or specialist expertise and, under certain conditions, can produce point clouds of comparable quality to existing survey methods (e.g. Terrestrial Laser Scanning). Consequently, applications of SfM-MVS in physical geography have multiplied rapidly. There are many practical options available to physical geographers when planning a SfM-MVS survey (e.g. platforms, cameras, software), yet, many SfM-MVS end-users are uncert...

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Structure from Motion Photogrammetry in Physical Geography

Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment

. Photogrammetry and geosciences have been closely linked since the late 19th century due to the acquisition of high-quality 3-D data sets of the environment, but it has so far been restricted to a limited range of remote sensing specialists because of the considerable cost of metric systems for the acquisition and treatment of airborne imagery. Today, a wide range of commercial and open-source software tools enable the generation of 3-D and 4-D models of complex geomorphological features by geoscientists and other non-experts users. In addition, very recent rapid developments in unmanned aerial vehicle (UAV) technology allow for the flexible generation of high-quality aerial surveying and ortho-photography at a relatively low cost. The increasing computing capabilities during the last decade, together with the development of high-performance digital sensors and the important software innovations developed by computer-based vision and visual perception research fields, have extended the rigorous processing of stereoscopic image data to a 3-D point cloud generation from a series of non-calibrated images. Structure-from-motion (SfM) workflows are based upon algorithms for efficient and automatic orientation of large image sets without further data acquisition information, examples including robust feature detectors like the scale-invariant feature transform for 2-D imagery. Nevertheless, the importance of carrying out well-established fieldwork strategies, using proper camera settings, ground control points and ground truth for understanding the different sources of errors, still needs to be adapted in the common scientific practice. This review intends not only to summarise the current state of the art on using SfM workflows in geomorphometry but also to give an overview of terms and fields of application. Furthermore, this article aims to quantify already achieved accuracies and used scales, using different strategies in order to evaluate possible stagnations of current developments and to identify key future challenges. It is our belief that some lessons learned from former articles, scientific reports and book chapters concerning the identification of common errors or "bad practices" and some other valuable information may help in guiding the future use of SfM photogrammetry in geosciences.

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Image-based surface reconstruction in geomorphometry - merits, limits and developments

Structure-from-motion (SfM) photogrammetry is revolutionising the collection of detailed topographic data, but insight into geomorphological processes is currently restricted by our limited understanding of SfM survey uncertainties. Here, we present an approach that, for the first time, specifically accounts for the spatially variable precision inherent to photo-based surveys, and enables confidence-bounded quantification of 3-D topographic change. The method uses novel 3-D precision maps that describe the 3-D photogrammetric and georeferencing uncertainty, and determines change through an adapted state-of-the-art fully 3-D point-cloud comparison (M3C2; Lague, et al., 2013), which is particularly valuable for complex topography. We introduce this method by: (1) using simulated UAV surveys, processed in photogrammetric software, to illustrate the spatial variability of precision and the relative influences of photogrammetric (e.g. image network geometry, tie point quality) and georeferencing (e.g. control measurement) considerations; (2) we then present a new Monte Carlo procedure for deriving this information using standard SfM software and integrate it into confidence-bounded change detection; before (3) demonstrating geomorphological application in which we use benchmark TLS data for validation and then estimate sediment budgets through differencing annual SfM surveys of an eroding badland. We show how 3-D precision maps enable more probable erosion patterns to be identified than existing analyses, and how a similar overall survey precision could have been achieved with direct survey georeferencing for camera position data with precision half as good as the GCPs’. Where precision is limited by weak georeferencing (e.g. camera positions with multi-metre precision, such as from a consumer UAV), then overall survey precision can scale as n-½ of the control precision (n = number of images). Our method also provides variance-covariance information for all parameters. Thus, we now open the door for SfM practitioners to use the comprehensive analyses that have underpinned rigorous photogrammetric approaches over the last half-century.

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3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry: precision maps for ground control and directly georeferenced surveys

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. 0.02 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). This article is protected by copyright. All rights reserved.

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

Optical remote sensing of submerged aquatic vegetation: Opportunities for shallow clearwater streams

A framework for evaluating the spatial configuration and temporal dynamics of hydraulic patches was tested in a UK lowland river. Detailed hydraulic assessment was carried out at four discharges between 0.303m3/s and 1.410m3/s in a 56m reach. Five hydraulic patches, as combinations of depth and mean column velocity, and the transitional zones between them were delineated using fuzzy cluster analysis. The composition and configuration of the hydraulic patch mosaic was quantified using spatial metrics. Results showed that the proportion, size, shape and relative location of hydraulic patches all varied with discharge, however intermediate flows appeared to support the most diverse hydraulic patch composition and configuration. This suggests the spatial influence of mesoscale bedforms on hydraulic patches is mediated by temporal variations in discharge. Transitional areas between hydraulic patches occupied a significant proportion of the reach at all flows (33–38%) and may function as in-stream ecotones. The framework addresses the need for a quantitative, spatially explicit approach to hydraulic assessment which could be used to assess the implications of flow regulation and hydromorphological alteration on hydraulic diversity.

A framework for evaluating the spatial configuration and temporal dynamics of hydraulic patches

Surface flow types (SFT) are advocated as ecologically relevant hydraulic units, often mapped visually from the bankside to characterise 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. 50m 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 characterisation. Our key findings indicate that SFT mapping accuracy is highly variable, with overall mapping accuracy not exceeding 74%. Results from analysis of similarity (ANOSIM) tests found that strong differences did not exist between all SFT pairs. This leads us to question the suitability of SFTs for characterising 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 ≤1m). 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 10-100m), and towards continuous, quantitative measurements of the continuum of hydraulic and geomorphic conditions which actually exists at the microscale.

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The Accuracy and Reliability of Traditional Surface Flow Type Mapping: Is it Time for a New Method of Characterizing Physical River Habitat?

UAVs and other low-altitude remote sensing platforms are proving very useful tools for remote sensing of river systems. Currently consumer grade cameras are still the most commonly used sensors for this purpose. In particular, progress is being made to obtain river bathymetry from the optical image data collected with such cameras, using the strong attenuation of light in water. No studies have yet applied this method to map submergence depth of aquatic vegetation, which has rather different reflectance characteristics from river bed substrate. This study therefore looked at the possibilities to use the optical image data to map submerged aquatic vegetation (SAV) depth in shallow clear water streams. We first applied the Optimal Band Ratio Analysis method (OBRA) of Legleiter et al. (2009) to a dataset of spectral signatures from three macrophyte species in a clear water stream. The results showed that for each species the ratio of certain wavelengths were strongly associated with depth. A combined assessment of all species resulted in equally strong associations, indicating that the effect of spectral variation in vegetation is subsidiary to spectral variation due to depth changes. Strongest associations (R2-values ranging from 0.67 to 0.90 for different species) were found for combinations including one band in the near infrared (NIR) region between 825 and 925 nm and one band in the visible light region. Currently data of both high spatial and spectral resolution is not commonly available to apply the OBRA results directly to image data for SAV depth mapping. Instead a novel, low-cost data acquisition method was used to obtain six-band high spatial resolution image composites using a NIR sensitive DSLR camera. A field dataset of SAV submergence depths was used to develop regression models for the mapping of submergence depth from image pixel values. Band (combinations) providing the best performing models (R2-values up to 0.77) corresponded with the OBRA findings. A 10% error was achieved under sub-optimal data collection conditions, which indicates that the method could be suitable for many SAV mapping applications.

https://www.mdpi.com/1424-8220/15/10/25287/pdf

Fleur Visser

Papers

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

Optical remote sensing of submerged aquatic vegetation: Opportunities for shallow clearwater streams

A framework for evaluating the spatial configuration and temporal dynamics of hydraulic patches

The Accuracy and Reliability of Traditional Surface Flow Type Mapping: Is it Time for a New Method of Characterizing Physical River Habitat?

Depth Estimation of Submerged Aquatic Vegetation in Clear Water Streams Using Low-Altitude Optical Remote Sensing