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Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (15 February 2020) | Viewed by 72996

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Special Issue Editors

Dr. Carl J. Legleiter

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Guest Editor

Geomorphology and Sediment Transport Laboratory, U.S. Geological Survey, 4620 Technology Drive, Suite #400, Golden, CO 80403, USA
Interests: remote sensing of rivers; fluvial geomorphology; hyperspectral; bathymetric LiDAR; depth retrieval; in-stream habitat; morphodynamics; channel change; geostatistics

Dr. Tamlin Pavelsky

E-Mail Website
Guest Editor

Department of Geological Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Interests: global hydrology; inundation extent; lakes; rivers; wetlands; new satellite missions

Dr. Michael Durand

E-Mail Website
Guest Editor

School of Earth Sciences, The Ohio State University, 125 South Oval Mall, Columbus, OH 43210, USA
Interests: remote sensing; hydrology; rivers; snow; water cycle

Dr. George Allen

E-Mail Website
Guest Editor

College of Geosciences, Texas A&M University, 400 Bizzell St, College Station, TX 77843, USA
Interests: remote sensing of rivers; fluvial geomorphology; surface hydrology; water resources; carbon cycling

Dr. Angelica Tarpanelli

E-Mail Website
Guest Editor

Research Institute for Geo-Hydrological Protection, National Research Council, Via della Madonna Alta 126, 06128 Perugia, Italy
Interests: remote sensing of rivers; hydrological and hydraulic processes; flooded area estimation; analysis of climate change effects on flood frequency
Special Issues, Collections and Topics in MDPI journals

Dr. Renato Frasson

E-Mail Website
Guest Editor

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Interests: remote sensing of rivers; discharge estimation; inverse problems; hydrology; computer modeling; remote sensing of floods
Special Issues, Collections and Topics in MDPI journals

Dr. Inci Guneralp

E-Mail Website
Guest Editor

The College of Geosciences, Texas A&M University, 810 Eller O&M Building, College Station, TX 77845, USA
Interests: remote sensing of rivers; fluvial geomorphology; flooding; river channel dynamics; river biogeomorphodynamics; floodplain landscape evolution; spatiotemporal modeling

Dr. Amy Woodget

E-Mail
Guest Editor

Department of Geography, Loughborough University, Epinal Way, Loughborough, Leicestershire LE11 3TU, UK
Interests: remote sensing of rivers; UAVs; structure-from-motion photogrammetry; grain size estimation; fluvial topography/bathymetry; refraction correction; fluvial geomorphology

Special Issue Information

Dear Colleagues,

River discharge is a fundamental hydrological quantity that summarizes how a watershed transforms the input of precipitation into output as streamflow. The accurate measurement of discharge is critical for numerous applications including water supply, navigation, recreation, management of in-stream habitat, and prediction and monitoring of floods and droughts. However, traditional, in situ stream gage networks providing such data are sparse and declining, even in developed nations, and absent in many parts of the world. Moreover, establishing and maintaining these gages is expensive, labor-intensive, and can place personnel at risk.

For all these reasons, remote sensing represents an appealing alternative means of obtaining streamflow information. Potential advantages of the remote sensing approach include greater efficiency, expanded coverage, increased measurement frequency, cost savings, reduced risk to field hydrographers, and the opportunity to examine not just isolated cross-sections but longer reaches of river channels in two or three dimensions. Realizing these objectives will require research focused on the remote measurement of river discharge and its components: flow velocity and channel geometry.

The purpose of this Special Issue is to stimulate progress toward an operational capacity for streamflow monitoring and advance novel methods for retrieving discharge and its components by encouraging studies on this topic and compiling high-quality, peer-reviewed articles in an issue of Remote Sensing dedicated to this theme. We encourage the submission of manuscripts concerned with all aspects of the remote measurement of streamflow, including estimation of flow velocity, channel bathymetry (or water depth), and discharge from various types of remotely-sensed data (active or passive) acquired from a range of platforms (manned or unmanned aircraft, satellites, or ground-based imaging systems). Papers describing past, present, or future missions devoted to fluvial remote sensing are welcome, with a submission deadline of 15 February 2020.

Dr. Carl J. Legleiter
Dr. Tamlin Pavelsky
Dr. Michael Durand
Dr. George Allen
Dr. Angelica Tarpanelli
Dr. Renato Frasson
Dr. Inci Guneralp
Dr. Amy Woodget
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Remote sensing of rivers
discharge
flow velocity
channel bathymetry
hydrology
streamflow
fluvial geomorphology

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Published Papers (11 papers)

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Editorial

Jump to: Research, Review

7 pages, 227 KiB

Open AccessEditorial

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

by Carl J. Legleiter, Tamlin Pavelsky, Michael Durand, George H. Allen, Angelica Tarpanelli, Renato Frasson, Inci Guneralp and Amy Woodget

Remote Sens. 2020, 12(14), 2304; https://doi.org/10.3390/rs12142304 - 17 Jul 2020

Cited by 1 | Viewed by 2557

Abstract

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

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

Research

Jump to: Editorial, Review

16 pages, 2120 KiB

Open AccessArticle

Timing of Landsat Overpasses Effectively Captures Flow Conditions of Large Rivers

by George H. Allen, Xiao Yang, John Gardner, Joel Holliman, Cédric H. David and Matthew Ross

Remote Sens. 2020, 12(9), 1510; https://doi.org/10.3390/rs12091510 - 9 May 2020

Cited by 25 | Viewed by 5954

Abstract

Satellites provide a temporally discontinuous record of hydrological conditions along Earth’s rivers (e.g., river width, height, water quality). The degree to which archived satellite data effectively capture the overall population of river flow frequency is unknown. Here, we use the entire archives of Landsat 5, 7, and 8 to determine when a cloud-free image is available over the United States Geological Survey (USGS) river gauges located on Landsat-observable rivers. We compare the flow frequency distribution derived from the daily gauge record to the flow frequency distribution derived from ideally sampling gauged discharge based on the timing of cloud-free Landsat overpasses. Examining the patterns of flow frequency across multiple gauges, we find that there is not a statistically significant difference between the flow frequency distribution associated with observations contained within the Landsat archive and the flow frequency distribution derived from the daily gauge data (α = 0.05), except for hydrological extremes like maximum and minimum flow. At individual gauges, we find that Landsat observations span a wide range of hydrological conditions (97% of total flow variability observed in 90% of the study gauges) but the degree to which the Landsat sample can represent flow frequency distribution varies from location to location and depends on sample size. The results of this study indicate that the Landsat archive is, on average, representative of the temporal frequencies of hydrological conditions present along Earth’s large rivers with broad utility for hydrological, ecologic and biogeochemical evaluations of river systems. Full article

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

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21 pages, 15740 KiB

Open AccessArticle

Using Small Unmanned Aircraft Systems for Measuring Post-Flood High-Water Marks and Streambed Elevations

by Brandon T. Forbes, Geoffrey P. DeBenedetto, Jesse E. Dickinson, Claire E. Bunch and Faith A. Fitzpatrick

Remote Sens. 2020, 12(9), 1437; https://doi.org/10.3390/rs12091437 - 1 May 2020

Cited by 5 | Viewed by 3860

Abstract

Floods affected approximately two billion people around the world from 1998–2017, causing over 142,000 fatalities and over 656 billion U.S. dollars in economic losses. Flood data, such as the extent of inundation and peak flood stage, are needed to define the environmental, economic, and social impacts of significant flood events. Ground-based global positioning system (GPS) surveys of post-flood high-water marks (HWMs) and topography are commonly used to define flood inundation and stage, but can be time-consuming, difficult, and expensive to conduct. Here, we demonstrate and test the use of small unmanned aircraft systems (sUAS) and close-range remote sensing techniques to collect high-accuracy flood data to define peak flood stage elevations and river cross-sections. We evaluate the elevation accuracy of the HWMs from sUAS surveys by comparison with traditional GPS surveys, which have acceptable accuracy for many post-flood assessments, at two flood sites on two small streams in the U.S. Mean elevation errors for the sUAS surveys were 0.07 m and 0.14 m for the semiarid and temperate sites, respectively; those values are similar to typical errors when measuring HWM elevations with GPS surveys. Results demonstrate that sUAS surveys of HWMs and cross-sections can be an accurate and efficient alternative to GPS surveys; we provide insights that can be used to decide whether sUAS or GPS techniques will be most efficient for post-flood surveying. Full article

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

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29 pages, 3665 KiB

Open AccessArticle

Longitudinal, Lateral, Vertical, and Temporal Thermal Heterogeneity in a Large Impounded River: Implications for Cold-Water Refuges

by Francine H. Mejia, Christian E. Torgersen, Eric K. Berntsen, Joseph R. Maroney, Jason M. Connor, Aimee H. Fullerton, Joseph L. Ebersole and Mark S. Lorang

Remote Sens. 2020, 12(9), 1386; https://doi.org/10.3390/rs12091386 - 28 Apr 2020

Cited by 25 | Viewed by 4381

Abstract

Dam operations can affect mixing of the water column, thereby influencing thermal heterogeneity spatially and temporally. This occurs by restricting or eliminating connectivity in longitudinal, lateral, vertical, and temporal dimensions. We examined thermal heterogeneity across space and time and identified potential cold-water refuges for salmonids in a large impounded river in inland northwestern USA. To describe these patterns, we used thermal infrared (TIR) imagery, in situ thermographs, and high-resolution, 3-D hydraulic mapping. We explained the median water temperature and probability of occurrence of cool-water areas using generalized additive models (GAMs) at reach and subcatchment scales, and we evaluated potential cold-water refuge occurrence in relation to these patterns. We demonstrated that (1) lateral contributions from tributaries dominated thermal heterogeneity, (2) thermal variability at confluences was approximately an order of magnitude greater than of the main stem, (3) potential cold-water refuges were mostly found at confluences, and (4) the probability of occurrence of cool areas and median water temperature were associated with channel geomorphology and distance from dam. These findings highlight the importance of using multiple approaches to describe thermal heterogeneity in large, impounded rivers and the need to incorporate these types of rivers in the understanding of thermal riverscapes because of their limited representation in the literature. Full article

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

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28 pages, 4409 KiB

Open AccessArticle

Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages

by John W. Fulton, Christopher A. Mason, John R. Eggleston, Matthew J. Nicotra, Chao-Lin Chiu, Mark F. Henneberg, Heather R. Best, Jay R. Cederberg, Stephen R. Holnbeck, R. Russell Lotspeich, Christopher D. Laveau, Tommaso Moramarco, Mark E. Jones, Jonathan J. Gourley and Daniel Wasielewski

Remote Sens. 2020, 12(8), 1296; https://doi.org/10.3390/rs12081296 - 20 Apr 2020

Cited by 32 | Viewed by 7025

Abstract

Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m³/s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m³/s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge. Full article

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

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28 pages, 3855 KiB

Open AccessFeature PaperArticle

Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter

by Carl J. Legleiter and Paul J. Kinzel

Remote Sens. 2020, 12(8), 1282; https://doi.org/10.3390/rs12081282 - 18 Apr 2020

Cited by 22 | Viewed by 3433

Abstract

The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement (

R^{2} > 0.9

) between remotely sensed velocities and field measurements. Similarly, analysis of the sensitivity of PIV accuracy to image sequence duration showed that dwell times as short as 16 s would be sufficient at a frame rate of 1 Hz and could be cut in half if the frame rate were doubled. The results of this investigation indicate that helicopter-based remote sensing of velocities in sediment-laden rivers could contribute to noncontact streamgaging programs and enable reach-scale mapping of flow fields. Full article

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

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Graphical abstract

28 pages, 56492 KiB

Open AccessArticle

Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor

by Gottfried Mandlburger, Martin Pfennigbauer, Roland Schwarz, Sebastian Flöry and Lukas Nussbaumer

Remote Sens. 2020, 12(6), 986; https://doi.org/10.3390/rs12060986 - 19 Mar 2020

Cited by 92 | Viewed by 12065

Abstract

We present the sensor concept and first performance and accuracy assessment results of a novel lightweight topo-bathymetric laser scanner designed for integration on Unmanned Aerial Vehicles (UAVs), light aircraft, and helicopters. The instrument is particularly well suited for capturing river bathymetry in high spatial resolution as a consequence of (i) the low nominal flying altitude of 50–150 m above ground level resulting in a laser footprint diameter on the ground of typically 10–30 cm and (ii) the high pulse repetition rate of up to 200 kHz yielding a point density on the ground of approximately 20–50 points/m². The instrument features online waveform processing and additionally stores the full waveform within the entire range gate for waveform analysis in post-processing. The sensor was tested in a real-world environment by acquiring data from two freshwater ponds and a 500 m section of the pre-Alpine Pielach River (Lower Austria). The captured underwater points featured a maximum penetration of two times the Secchi depth. On dry land, the 3D point clouds exhibited (i) a measurement noise in the range of 1–3 mm; (ii) a fitting precision of redundantly captured flight strips of 1 cm; and (iii) an absolute accuracy of 2–3 cm compared to terrestrially surveyed checkerboard targets. A comparison of the refraction corrected LiDAR point cloud with independent underwater checkpoints exhibited a maximum deviation of 7.8 cm and revealed a systematic depth-dependent error when using a refraction coefficient of n = 1.36 for time-of-flight correction. The bias is attributed to multi-path effects in the turbid water column (Secchi depth: 1.1 m) caused by forward scattering of the laser signal at suspended particles. Due to the high spatial resolution, good depth performance, and accuracy, the sensor shows a high potential for applications in hydrology, fluvial morphology, and hydraulic engineering, including flood simulation, sediment transport modeling, and habitat mapping. Full article

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

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33 pages, 50115 KiB

Open AccessArticle

Quantifying Below-Water Fluvial Geomorphic Change: The Implications of Refraction Correction, Water Surface Elevations, and Spatially Variable Error

by Amy S. Woodget, James T. Dietrich and Robin T. Wilson

Remote Sens. 2019, 11(20), 2415; https://doi.org/10.3390/rs11202415 - 18 Oct 2019

Cited by 27 | Viewed by 4382

Abstract

Much of the geomorphic work of rivers occurs underwater. As a result, high resolution
quantification of geomorphic change in these submerged areas is important. Currently, to quantify this
change, multiple methods are required to get high resolution data for both the exposed and submerged
areas. Remote sensing methods are often limited to the exposed areas due to the challenges imposed
by the water, and those remote sensing methods for below the water surface require the collection of
extensive calibration data in-channel, which is time-consuming, labour-intensive, and sometimes
prohibitive in dicult-to-access areas. Within this paper, we pioneer a novel approach for quantifying
above- and below-water geomorphic change using Structure-from-Motion photogrammetry and
investigate the implications of water surface elevations, refraction correction measures, and the
spatial 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 using
Structure-from-Motion (SfM) photogrammetry. For the first time, we show that: (1) Quantification of
submerged geomorphic change to levels of accuracy commensurate with exposed areas is possible
without the need for calibration data or a dierent method from exposed areas; (2) there is minimal
dierence in results produced by dierent refraction correction procedures using predominantly
nadir imagery (small angle vs. multi-view), allowing users a choice of software packages/processing
complexity; (3) improvements to our estimations of water surface elevations are critical for accurate
topographic 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 Naïve
Bayes classifier, based on the relationship between error and 11 independent variables, to generate a
high resolution, spatially continuous model of geomorphic change in submerged areas, constrained by
spatially 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-going
testing and improvements, this machine learning approach has potential for routine application in
spatially variable error estimation within the RPAS–SfM workflow. Full article

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

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19 pages, 854 KiB

Open AccessArticle

sUAS-Based Remote Sensing of River Discharge Using Thermal Particle Image Velocimetry and Bathymetric Lidar

by Paul J. Kinzel and Carl J. Legleiter

Remote Sens. 2019, 11(19), 2317; https://doi.org/10.3390/rs11192317 - 5 Oct 2019

Cited by 45 | Viewed by 7100

Abstract

This paper describes a non-contact methodology for computing river discharge based on data collected from small Unmanned Aerial Systems (sUAS). The approach is complete in that both surface velocity and channel geometry are measured directly under field conditions. The technique does not require introducing artificial tracer particles for computing surface velocity, nor does it rely upon the presence of naturally occurring floating material. Moreover, no prior knowledge of river bathymetry is necessary. Due to the weight of the sensors and limited payload capacities of the commercially available sUAS used in the study, two sUAS were required. The first sUAS included mid-wave thermal infrared and visible cameras. For the field evaluation described herein, a thermal image time series was acquired and a particle image velocimetry (PIV) algorithm used to track the motion of structures expressed at the water surface as small differences in temperature. The ability to detect these thermal features was significant because the water surface lacked floating material (e.g., foam, debris) that could have been detected with a visible camera and used to perform conventional Large-Scale Particle Image Velocimetry (LSPIV). The second sUAS was devoted to measuring bathymetry with a novel scanning polarizing lidar. We collected field measurements along two channel transects to assess the accuracy of the remotely sensed velocities, depths, and discharges. Thermal PIV provided velocities that agreed closely (

R^{2}

= 0.82 and 0.64) with in situ velocity measurements from an acoustic Doppler current profiler (ADCP). Depths inferred from the lidar closely matched those surveyed by wading in the shallower of the two cross sections (

R^{2}

= 0.95), but the agreement was not as strong for the transect with greater depths (

R^{2}

= 0.61). Incremental discharges computed with the remotely sensed velocities and depths were greater than corresponding ADCP measurements by 22% at the first cross section and <1% at the second. Full article

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

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Graphical abstract

32 pages, 15085 KiB

Open AccessArticle

Empirical Assessment Tool for Bathymetry, Flow Velocity and Salinity in Estuaries Based on Tidal Amplitude and Remotely-Sensed Imagery

by Jasper R. F. W. Leuven, Steye L. Verhoeve, Wout M. Van Dijk, Sanja Selaković and Maarten G. Kleinhans

Remote Sens. 2018, 10(12), 1915; https://doi.org/10.3390/rs10121915 - 30 Nov 2018

Cited by 10 | Viewed by 6558

Abstract

Hydromorphological data for many estuaries worldwide is scarce and usually limited to offshore tidal amplitude and remotely-sensed imagery. In many projects, information about morphology and intertidal area is needed to assess the effects of human interventions and rising sea-level on the natural depth distribution and on changing habitats. Habitat area depends on the spatial pattern of intertidal area, inundation time, peak flow velocities and salinity. While numerical models can reproduce these spatial patterns fairly well, their data need and computational costs are high and for each case a new model must be developed. Here, we present a Python tool that includes a comprehensive set of relations that predicts the hydrodynamics, bed elevation and the patterns of channels and bars in mere seconds. Predictions are based on a combination of empirical relations derived from natural estuaries, including a novel predictor for cross-sectional depth distributions, which is dependent on the along-channel width profile. Flow velocity, an important habitat characteristic, is calculated with a new correlation between depth below high water level and peak tidal flow velocity, which was based on spatial numerical modelling. Salinity is calculated from estuarine geometry and flow conditions. The tool only requires an along-channel width profile and tidal amplitude, making it useful for quick assessments, for example of potential habitat in ecology, when only remotely-sensed imagery is available. Full article

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

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Review

Jump to: Editorial, Research

28 pages, 2087 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Remote Sensing of River Discharge: A Review and a Framing for the Discipline

by Colin J Gleason and Michael T Durand

Remote Sens. 2020, 12(7), 1107; https://doi.org/10.3390/rs12071107 - 31 Mar 2020

Cited by 106 | Viewed by 13431

Abstract

Remote sensing of river discharge (RSQ) is a burgeoning field rife with innovation. This innovation has resulted in a highly non-cohesive subfield of hydrology advancing at a rapid pace, and as a result misconceptions, mis-citations, and confusion are apparent among authors, readers, editors, and reviewers. While the intellectually diverse subfield of RSQ practitioners can parse this confusion, the broader hydrology community views RSQ as a monolith and such confusion can be damaging. RSQ has not been comprehensively summarized over the past decade, and we believe that a summary of the recent literature has a potential to provide clarity to practitioners and general hydrologists alike. Therefore, we here summarize a broad swath of the literature, and find after our reading that the most appropriate way to summarize this literature is first by application area (into methods appropriate for gauged, semi-gauged, regionally gauged, politically ungauged, and totally ungauged basins) and next by methodology. We do not find categorizing by sensor useful, and everything from un-crewed aerial vehicles (UAVs) to satellites are considered here. Perhaps the most cogent theme to emerge from our reading is the need for context. All RSQ is employed in the service of furthering hydrologic understanding, and we argue that nearly all RSQ is useful in this pursuit provided it is properly contextualized. We argue that if authors place each new work into the correct application context, much confusion can be avoided, and we suggest a framework for such context here. Specifically, we define which RSQ techniques are and are not appropriate for ungauged basins, and further define what it means to be ‘ungauged’ in the context of RSQ. We also include political and economic realities of RSQ, as the objective of the field is sometimes to provide data purposefully cloistered by specific political decisions. This framing can enable RSQ to respond to hydrology at large with confidence and cohesion even in the face of methodological and application diversity evident within the literature. Finally, we embrace the intellectual diversity of RSQ and suggest the field is best served by a continuation of methodological proliferation rather than by a move toward orthodoxy and standardization. Full article

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

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