Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Advances in Remote Sensing in Coastal and Hydraulic Engineering

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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 (30 September 2021) | Viewed by 29415

Share This Special Issue

Special Issue Editors

Dr. Kristen Splinter

E-Mail Website
Guest Editor

Senior Lecturer, Water Research Laboratory, School of Civil and Environmental Engineering, UNSW, Sydney, Australia
Interests: coastal sediment transport; shoreline change observations and modelling at timescales of storms to decades; coastal dune erosion

Dr. Stefan Felder

E-Mail Website
Guest Editor

Senior Lecturer, Water Research Laboratory, School of Civil and Environmental Engineering, UNSW, Sydney, Australia
Interests: turbulent free-surface flows; air–water flows at the lab and field scales; design optimisation for hydraulic structures; fish passage; remote sensing technology in hydraulics

Prof. Dr. Nadia Senechal

E-Mail Website
Guest Editor

U.M.R. 5805 EPOC, Avenue des Facultés, University of Bordeaux, 33405 Talence, France
Interests: shoreline change observations at timescales of storms to seasons; seasonal recovery; runup processes; open wave dominated beaches; tidal and mixed inlets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote Sensing technologies are providing vast levels of information to enable a better understanding of the key processes in geophysical flows and complex environments where, previously, in situ data were prohibitive to collect over large spatial and temporal scales or in remote locations. This includes both episodic events (such as the impact of a single storm/flood) up to the chronic multi-decadal monitoring of environmental change. Remote sensing is also routinely applied to better elucidate the fundamental flow processes at the free surface of flowing waters both at the laboratory and field scales.

This Special Issue invites new and original papers discussing new technology and the benefits of remote sensing studies in complex environmental systems within the field of Coastal and Hydraulic Engineering. Recent advances in the approaches to the collection of hydraulic and coastal data (in both the field and lab); advances in the use of remote sensing technology for improved accuracy and stability in complex systems; future developments; and studies including, but not limited to, the following aspects will be considered:

Routine monitoring of coastal morphology;
Routine monitoring of coastal structures;
Routine monitoring of hydraulic structures;
Routine monitoring of estuary and coastal wetland environments;
Remote sensing tools used for the rapid assessment of hydraulic flows (floods, dam breaks, etc.);
Remote sensing tools used for the assessment of coastal land use change;
Remote sensing innovations in physical modelling;
Remote sensing of fundamental processes in free surface flows.

Dr. Kristen Splinter
Dr. Stefan Felder
Prof. Dr. Nadia Senechal
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

coastal
hydraulics
video imaging
lidar
satellite
unmanned aerial vehicle
rivers
dams
estuaries
shoreline
engineering
fluid mechanics
physical modelling
free-surface flows
air–water flows

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

17 pages, 7448 KiB

Open AccessArticle

Development of a Fully Convolutional Neural Network to Derive Surf-Zone Bathymetry from Close-Range Imagery of Waves in Duck, NC

by Adam M. Collins, Matthew P. Geheran, Tyler J. Hesser, Andrew Spicer Bak, Katherine L. Brodie and Matthew W. Farthing

Remote Sens. 2021, 13(23), 4907; https://doi.org/10.3390/rs13234907 - 03 Dec 2021

Cited by 9 | Viewed by 2137

Abstract

Timely observations of nearshore water depths are important for a variety of coastal research and management topics, yet this information is expensive to collect using in situ survey methods. Remote methods to estimate bathymetry from imagery include using either ratios of multi-spectral reflectance bands or inversions from wave processes. Multi-spectral methods work best in waters with low turbidity, and wave-speed-based methods work best when wave breaking is minimal. In this work, we build on the wave-based inversion approaches, by exploring the use of a fully convolutional neural network (FCNN) to infer nearshore bathymetry from imagery of the sea surface and local wave statistics. We apply transfer learning to adapt a CNN originally trained on synthetic imagery generated from a Boussinesq numerical wave model to utilize tower-based imagery collected in Duck, North Carolina, at the U.S. Army Engineer Research and Development Center’s Field Research Facility. We train the model on sea-surface imagery, wave conditions, and associated surveyed bathymetry using three years of observations, including times with significant wave breaking in the surf zone. This is the first time, to the authors’ knowledge, an FCNN has been successfully applied to infer bathymetry from surf-zone sea-surface imagery. Model results from a separate one-year test period generally show good agreement with survey-derived bathymetry (0.37 m root-mean-squared error, with a max depth of 6.7 m) under diverse wave conditions with wave heights up to 3.5 m. Bathymetry results quantify nearshore bathymetric evolution including bar migration and transitions between single- and double-barred morphologies. We observe that bathymetry estimates are most accurate when time-averaged input images feature visible wave breaking and/or individual images display wave crests. An investigation of activation maps, which show neuron activity on a layer-by-layer basis, suggests that the model is responsive to visible coherent wave structures in the input images. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal and Hydraulic Engineering)

► Show Figures

Figure 1

25 pages, 9187 KiB

Open AccessArticle

Updates to and Performance of the cBathy Algorithm for Estimating Nearshore Bathymetry from Remote Sensing Imagery

by Rob Holman and Erwin W. J. Bergsma

Remote Sens. 2021, 13(19), 3996; https://doi.org/10.3390/rs13193996 - 06 Oct 2021

Cited by 14 | Viewed by 2320

Abstract

This manuscript describes and tests a set of improvements to the cBathy algorithm, published in 2013 by Holman et al. [hereafter HPH13], for the estimation of bathymetry based on optical observations of propagating nearshore waves. Three versions are considered, the original HPH13 algorithm (now labeled V1.0), an intermediate version that has seen moderate use but limited testing (V1.2), and a substantially updated version (V2.0). Important improvements from V1.0 include a new deep-water weighting scheme, removal of a spurious variable in the nonlinear fitting, an adaptive scheme for determining the optimum tile size based on the approximate wavelength, and a much-improved search seed algorithm. While V1.2 was tested and results listed, the primary interest is in comparing V1.0, the original code, with the new version V2.0. The three versions were tested against an updated dataset of 39 ground-truth surveys collected from 2015 to 2019 at the Field Research Facility in Duck, NC. In all, 624 cBathy collections were processed spanning a four-day period up to and including each survey date. Both the unfiltered phase 2 and the Kalman-filtered phase 3 bathymetry estimates were tested. For the Kalman-filtered estimates, only the estimate from mid-afternoon on the survey date was used for statistical measures. Of those 39 Kalman products, the bias, rms error, and 95% exceedance for V1.0 were 0.15, 0.47, and 0.96 m, respectively, while for V2.0, they were 0.08, 0.38, and 0.78 m. The mean observed coverage, the percentage of successful estimate locations in the map, were 99.1% for V1.0 and 99.9% for V2.0. Phase 2 (unfiltered) bathymetry estimates were also compared to ground truth for the 624 available data runs. The mean bias, rms error, and 95% exceedance statistics for V1.0 were 0.19, 0.64, and 1.27 m, respectively, and for V2.0 were 0.16, 0.56, and 1.19 m, an improvement in all cases. The coverage also increased from 78.8% for V1.0 to 84.7% for V2.0, about a 27% reduction in the number of failed estimates. The largest errors were associated with both large waves and poor imaging conditions such as fog, rain, or darkness that greatly reduced the percentage of successful coverage. As a practical mitigation of large errors, data runs for which the significant wave height was greater than 1.2 m or the coverage was less than 50% were omitted from the analysis, reducing the number of runs from 624 to 563. For this reduced dataset, the bias, rms error, and 95% exceedance errors for V1.0 were 0.15, 0.58, and 1.16 m and for V2.0 were 0.09, 0.41, and 0.85 m, respectively. Successful coverage for V1.0 was 82.8%, while for V2.0, it was 90.0%, a roughly 42% reduction in the number of failed estimates. Performance for V2.0 individual (non-filtered) estimates is slightly better than the Kalman results in the original HPH13 paper, and it is recommended that version 2.0 becomes the new standard algorithm. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal and Hydraulic Engineering)

► Show Figures

Figure 1

17 pages, 4433 KiB

Open AccessArticle

LIDAR Scanning as an Advanced Technology in Physical Hydraulic Modelling: The Stilling Basin Example

by Rui Li, Kristen D. Splinter and Stefan Felder

Remote Sens. 2021, 13(18), 3599; https://doi.org/10.3390/rs13183599 - 09 Sep 2021

Cited by 6 | Viewed by 2250

Abstract

In hydraulic engineering, stilling basin design is traditionally carried out using physical models, conducting visual flow observations as well as point-source measurements of pressure, flow depth, and velocity at locations of design relevance. Point measurements often fail to capture the strongly varying three-dimensionality of the flows within the stilling basin that are important for the best possible design of the structure. This study introduced fixed scanning 2D LIDAR technology for laboratory-scale physical hydraulic modelling of stilling basins. The free-surface motions were successfully captured along both longitudinal and transverse directions, providing a detailed free-surface map. LIDAR-derived free-surface elevations were compared with typical point-source measurements using air–water conductivity probes, showing that the elevations measured with LIDAR consistently corresponded to locations of strongest air–water flow interactions at local void fractions of approximately 50%. The comparison of LIDAR-derived free-surface elevations with static and dynamic pressure sensors confirmed differences between the two measurement devices in the most energetic parts of the jump roller. The present study demonstrates that LIDAR technology can play an important role in physical hydraulic modelling, enabling design improvement through detailed free-surface characterization of complex air–water flow motions beyond the current practice of point measurements and visual flow observations. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal and Hydraulic Engineering)

► Show Figures

Figure 1

21 pages, 8207 KiB

Open AccessArticle

Wave Peel Tracking: A New Approach for Assessing Surf Amenity and Analysis of Breaking Waves

by Michael Thompson, Ivan Zelich, Evan Watterson and Tom E. Baldock

Remote Sens. 2021, 13(17), 3372; https://doi.org/10.3390/rs13173372 - 25 Aug 2021

Cited by 7 | Viewed by 3158

Abstract

The creation and protection of surfing breaks along populated coastlines have become a consideration for many councils and governments as surfing breaks are a major driver of tourism. To assess the surf amenity of surfing breaks, a quantitative and objective assessment method is required. A new wave peel tracking (WPT) method has been developed using a shore-based camera to assess surf amenity by measuring and quantifying potential surfing ride rate, length, duration, speed and direction on a wave-by-wave basis. The wave peel (or “curl” below the wave peak) is the optimal surfing region on a wave, and each wave peel track represents a surfable ride. Wave peel regions are identified, classified and tracked using traditional and machine learning-based computer vision techniques. The methodology is validated by comparing the rectified wave peel tracks with GPS-measured tracks from surfers in the wave peel regions. The WPT methodology is evaluated with data from a reef and adjacent natural beach at the Gold Coast, Australia. The reef produced longer ride lengths than the nearshore region and showed a consistent breaking location along the reef crest. Spatial maps of the wave peel tracks show the influence of tides on the wave breaking patterns and intensity. The WPT algorithm provides a robust, automated method for quantifying surf amenity to provide baseline data for surf break conservation. The methodology has potential uses to verify numerical modelling of surf breaks and to assess the impact of coastal development on surf breaks. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal and Hydraulic Engineering)

► Show Figures

Graphical abstract

20 pages, 10194 KiB

Open AccessArticle

Remote Sensing of Aerated Flows at Large Dams: Proof of Concept

by Matthias Kramer and Stefan Felder

Remote Sens. 2021, 13(14), 2836; https://doi.org/10.3390/rs13142836 - 19 Jul 2021

Cited by 6 | Viewed by 2593

Abstract

Dams are important for flood mitigation, water supply, and hydroelectricity. Every dam has a water conveyance structure, such as a spillway, to safely release extreme floods when needed. The flows down spillways are often self-aerated and spillway design has typically been investigated in laboratory experiments, which is due to limitations in suitable full scale flow measurement instrumentation and safety considerations. Prototype measurements of aerated flows are urgently needed to quantify potential scale effects and to provide missing validation data for design guidelines and numerical simulations. Herein, an image-based analysis of free-surface flows on a stepped spillway was conducted from a top-view perspective at laboratory scale (fixed camera installation) and prototype scale (drone footage). The drone videos were obtained from citizen science data. Analyses allowed to remotely estimate the location of the inception point of free-surface aeration, air–water surface velocities, and their fluctuations, as well as the residual energy at the downstream end of the chute. The laboratory results were successfully validated against intrusive phase-detection probe data, while the prototype observations provided proof of concept at full scale. This study highlights the feasibility of image-based measurements at prototype spillways. It demonstrates how citizen science data can be used to advance our understanding of real world air–water flow processes and lays the foundations for the remote collection of long-missing prototype data. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal and Hydraulic Engineering)

► Show Figures

Figure 1

20 pages, 10550 KiB

Open AccessArticle

Quantifying Optically Derived Two-Dimensional Wave-Averaged Currents in the Surf Zone

by Dylan Anderson, A. Spicer Bak, Katherine L. Brodie, Nicholas Cohn, Rob A. Holman and John Stanley

Remote Sens. 2021, 13(4), 690; https://doi.org/10.3390/rs13040690 - 13 Feb 2021

Cited by 18 | Viewed by 3730

Abstract

Complex two-dimensional nearshore current patterns are generated by feedbacks between sub-aqueous morphology and momentum imparted on the water column by breaking waves, winds, and tides. These non-stationary features, such as rip currents and circulation cells, respond to changing environmental conditions and underlying morphology. However, using fixed instruments to observe nearshore currents is limiting due to the high costs and logistics necessary to achieve adequate spatial sampling resolution. A new technique for processing surf-zone imagery, WAMFlow, quantifies fluid velocities to reveal complex, multi-scale (10 s–1000 s meters) nearshore surface circulation patterns. We apply the concept of a wave-averaged movie (WAM) to measure surf-zone circulation patterns on spatial scales of kilometers in the alongshore and 100 s of meters in the cross-shore. The approach uses a rolling average of 2 Hz optical imagery, removing the dominant optical clutter of incident waves, to leave the residual foam or water turbidity features carried by the flow. These residual features are tracked as quasi-passive tracers in space and time using optical flow, which solves for u and v as a function of image intensity gradients in x, y, and t. Surf zone drifters were deployed over multiple days with varying nearshore circulations to validate the optically derived flow patterns. Root mean square error are reduced to 0.1 m per second after filtering based on image attributes. The optically derived patterns captured longshore currents, rip currents, and gyres within the surf zone. Quantifying nearshore circulation patterns using low-cost image platforms and open-source computer vision algorithms presents the potential to further our understanding of fundamental surf zone dynamics. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal and Hydraulic Engineering)

► Show Figures

Graphical abstract

20 pages, 2191 KiB

Open AccessArticle

Beach State Recognition Using Argus Imagery and Convolutional Neural Networks

by Ashley N. Ellenson, Joshua A. Simmons, Greg W. Wilson, Tyler J. Hesser and Kristen D. Splinter

Remote Sens. 2020, 12(23), 3953; https://doi.org/10.3390/rs12233953 - 03 Dec 2020

Cited by 20 | Viewed by 3835

Abstract

Nearshore morphology is a key driver in wave breaking and the resulting nearshore circulation, recreational safety, and nutrient dispersion. Morphology persists within the nearshore in specific shapes that can be classified into equilibrium states. Equilibrium states convey qualitative information about bathymetry and relevant physical processes. While nearshore bathymetry is a challenge to collect, much information about the underlying bathymetry can be gained from remote sensing of the surfzone. This study presents a new method to automatically classify beach state from Argus daytimexposure imagery using a machine learning technique called convolutional neural networks (CNNs). The CNN processed imagery from two locations: Narrabeen, New South Wales, Australia and Duck, North Carolina, USA. Three different CNN models are examined, one trained at Narrabeen, one at Duck, and one trained at both locations. Each model was tested at the location where it was trained in a self-test, and the single-beach models were tested at the location where it was not trained in a transfer-test. For the self-tests, skill (as measured by the F-score) was comparable to expert agreement (CNN F-values at Duck = 0.80 and Narrabeen = 0.59). For the transfer-tests, the CNN model skill was reduced by 24–48%, suggesting the algorithm requires additional local data to improve transferability performance. Transferability tests showed that comparable F-scores (within 10%) to the self-trained cases can be achieved at both locations when at least 25% of the training data is from each site. This suggests that if applied to additional locations, a CNN model trained at one location may be skillful at new sites with limited new imagery data needed. Finally, a CNN visualization technique (Guided-Grad-CAM) confirmed that the CNN determined classifications using image regions (e.g., incised rip channels, terraces) that were consistent with beach state labelling rules. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal and Hydraulic Engineering)

► Show Figures

Graphical abstract

22 pages, 9976 KiB

Open AccessArticle

A Deep Learning-Based Method for Quantifying and Mapping the Grain Size on Pebble Beaches

by Antoine Soloy, Imen Turki, Matthieu Fournier, Stéphane Costa, Bastien Peuziat and Nicolas Lecoq

Remote Sens. 2020, 12(21), 3659; https://doi.org/10.3390/rs12213659 - 08 Nov 2020

Cited by 26 | Viewed by 5281

Abstract

This article proposes a new methodological approach to measure and map the size of coarse clasts on a land surface from photographs. This method is based on the use of the Mask Regional Convolutional Neural Network (R-CNN) deep learning algorithm, which allows the instance segmentation of objects after an initial training on manually labeled data. The algorithm is capable of identifying and classifying objects present in an image at the pixel scale, without human intervention, in a matter of seconds. This work demonstrates that it is possible to train the model to detect non-overlapping coarse sediments on scaled images, in order to extract their individual size and morphological characteristics with high efficiency (R² = 0.98; Root Mean Square Error (RMSE) = 3.9 mm). It is then possible to measure element size profiles over a sedimentary body, as it was done on the pebble beach of Etretat (Normandy, France) in order to monitor the granulometric spatial variability before and after a storm. Applied at a larger scale using Unmanned Aerial Vehicle (UAV) derived ortho-images, the method allows the accurate characterization and high-resolution mapping of the surface coarse sediment size, as it was performed on the two pebble beaches of Etretat (D50 = 5.99 cm) and Hautot-sur-Mer (D50 = 7.44 cm) (Normandy, France). Validation results show a very satisfying overall representativity (R² = 0.45 and 0.75; RMSE = 6.8 mm and 9.3 mm at Etretat and Hautot-sur-Mer, respectively), while the method remains fast, easy to apply and low-cost, although the method remains limited by the image resolution (objects need to be longer than 4 cm), and could still be improved in several ways, for instance by adding more manually labeled data to the training dataset, and by considering more accurate methods than the ellipse fitting for measuring the particle sizes. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal and Hydraulic Engineering)

► Show Figures

Graphical abstract

Review

Jump to: Research

18 pages, 4397 KiB

Open AccessReview

The Coastal Imaging Research Network (CIRN)

by Margaret L. Palmsten and Katherine L. Brodie

Remote Sens. 2022, 14(3), 453; https://doi.org/10.3390/rs14030453 - 18 Jan 2022

Cited by 12 | Viewed by 2694

Abstract

The Coastal Imaging Research Network (CIRN) is an international group of researchers who exploit signatures of phenomena in imagery of coastal, estuarine, and riverine environments. CIRN participants develop and implement new coastal imaging methodologies. The research objective of the group is to use imagery to gain a better fundamental understanding of the processes shaping those environments. Coastal imaging data may also be used to derive inputs for model boundary and initial conditions through assimilation, to validate models, and to make management decisions. CIRN was officially formed in 2016 to provide an integrative, multi-institutional group to collaborate on remotely sensed data techniques. As of 2021, the network is a collaboration between researchers from approximately 16 countries and includes investigators from universities, government laboratories and agencies, non-profits, and private companies. CIRN has a strong emphasis on education, exemplified by hosting annual “boot camps” to teach photogrammetry fundamentals and toolboxes from the CIRN code repository, as well as hosting an annual meeting for its members to present coastal imaging research. In this review article, we provide context for the development of CIRN as well as describe the goals and accomplishments of the CIRN community. We highlight components of CIRN’s resources for researchers worldwide including an open-source GitHub repository and coding boot camps. Finally, we provide CIRN’s perspective on the future of coastal imaging. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal and Hydraulic Engineering)

► Show Figures

Journal Menu

Journal Browser

Advances in Remote Sensing in Coastal and Hydraulic Engineering

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI