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Application of Remote Sensing Methods to Monitor Coastal Zones

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A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 49618

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Special Issue Editors

Dr. Luis Pedro Almeida

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Guest Editor

Instituto de Oceanografia, Universidade Federal do Rio Grande (FURG), Rio Grande, Brazil
Interests: coastal processes; remote sensing

Dr. Rafael Almar

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Guest Editor

Institute of Research for Development, LEGOS, Toulouse, France
Interests: multi-scale coastal processes; optical-image processing
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Special Issue Information

Dear Colleagues,

Regional sea-level change, winds, waves, currents, extreme events, sediment supply, land-use change, and urbanization are all forcing agents that can change coastal zones. How coastal environments respond to natural and anthropogenic factors depends on the characteristics of the forcing agents, as well as on the characteristics of the coastal systems that remain poorly known and mostly unsurveyed. To better understand changes affecting coastal zones and to provide useful information to decision-makers, it is crucial to collect and analyze various types of observations covering local to global scales, and both short- and long-term time scales. In this context, remote-sensing methods, ranging from shore-based, aerial, or space platforms, including active and passive sensors, offer unique skills to overcome this challenge. In this Special Issue “Application of Remote Sensing Methods to Monitor Coastal Zones”, we invite authors to submit new innovative research that focuses on the application of remote-sensing methodologies (preferably new ones) to monitor coastal zones, including forcing agents (e.g., sea level, waves, wind, currents, etc.) and environmental responses (e.g. topographic and bathymetric response, land cover, etc.), from short to long-term time scales. New software tools or applications (e.g., Google Earth Engine, QGIS, etc.) that involve the use of remote-sensing datasets to monitor coastal processes are also encouraged for submission to this Special Issue.

Dr. Luis Pedro Almeida
Dr. Rafael Almar
Guest Editors

Manuscript Submission Information

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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. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

Remote sensing
Sediment transport
Coastal topography
Shallow-water bathymetry
Shallow-water hydrodynamics
Land-cover change
Google Earth Engine
Satellite altimetry
Satellite multispectral imagery
LiDAR
Video-monitoring

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Published Papers (10 papers)

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Editorial

Jump to: Research

2 pages, 167 KiB

Open AccessEditorial

Application of Remote Sensing Methods to Monitor Coastal Zones

by Luis Pedro Almeida and Rafael Almar

J. Mar. Sci. Eng. 2020, 8(6), 391; https://doi.org/10.3390/jmse8060391 - 29 May 2020

Cited by 2 | Viewed by 2229

Abstract

In this Special Issue “Application of Remote Sensing Methods to Monitor Coastal Zones” nine original research papers were published, with topics covering a wide range of ranging of remote sensing applications including coastal topography, bathymetry, land cover, and nearshore hydrodynamics [...] Full article

(This article belongs to the Special Issue Application of Remote Sensing Methods to Monitor Coastal Zones)

Research

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18 pages, 4297 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Lidar Observations of the Swash Zone of a Low-Tide Terraced Tropical Beach under Variable Wave Conditions: The Nha Trang (Vietnam) COASTVAR Experiment

by Luís Pedro Almeida, Rafael Almar, Chris Blenkinsopp, Nadia Senechal, Erwin Bergsma, France Floc’h, Charles Caulet, Melanie Biausque, Patrick Marchesiello, Philippe Grandjean, Jerome Ammann, Rachid Benshila, Duong Hai Thuan, Paula Gomes da Silva and Nguyen Trung Viet

J. Mar. Sci. Eng. 2020, 8(5), 302; https://doi.org/10.3390/jmse8050302 - 26 Apr 2020

Cited by 15 | Viewed by 4095

Abstract

A field experiment was conducted at a tropical microtidal intermediate sandy beach with a low tide terrace (Nha Trang, Vietnam) to investigate the short-term swash-zone hydrodynamics and morphodynamics under variable wave conditions. Continuous 2D Lidar scanner observations of wave height at the lower foreshore, subsequent run-up and swash-induced topographic changes were obtained. These data were complemented by detailed real-time kinematic GPS topographic surveys. Variable wave and tide conditions were experienced during the field experiment with relatively large swell waves (offshore significant wave height, H_s = 0.9 m to 1.3 m; peak wave period, T_p = 8 to 12 s) concomitant with spring tides at the beginning of the period, followed by mild wind waves (offshore H_s under 0.5 m and T_p 5 s) and neap tides. This resulted in the following morphological sequence: berm erosion followed by rapid neap berm reformation and beach recovery within a few days. New insights into the link between intra-tidal swash dynamics and daily beach profile evolution were found using the Lidar dataset. While waves directly cause morphology changes on a wave-by-wave basis, tidal levels were found to be a key factor in determining the morphological wave-effect (accretive or erosive) due to modulated interaction between surf and swash hydro-morphodynamics. Full article

(This article belongs to the Special Issue Application of Remote Sensing Methods to Monitor Coastal Zones)

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16 pages, 7226 KiB

Open AccessArticle

Real-Time Diagnosis of Island Landslides Based on GB-RAR

by Deming Ma, Yongsheng Li, Jianwei Cai, Bingquan Li, Yanxiong Liu and Xingguo Chen

J. Mar. Sci. Eng. 2020, 8(3), 192; https://doi.org/10.3390/jmse8030192 - 12 Mar 2020

Cited by 11 | Viewed by 3154

Abstract

Landslides are one of the most frequent and serious geological disasters that threaten people’s lives and property safety. In recent years, with the rapid development of the coastal economy and the increasingly strained spatial resources, the island development activities have become extremely rapid, resulting in the frequent occurrence of landslides on the island. We selected Beichangshan Island in the north of China as the research area. By using high-precision ground-based real aperture radar (GB-RAR) measurement technology, the displacement changes of potential landslides are monitored continuously and dynamically to realize the real-time diagnosis and early warning of island landslides. At the same time, the data interpretation method and key processing flow are described in detail. The results show that during the whole monitoring process, an area of obvious change is found, which is mainly located in the middle of the landslide mass. The mean velocity rate shows a nonlinear deformation trend. The maximum deformation of the landslide in the five selected points reaches 4.5 mm, which indicates that the area is in an unstable stage. The deformation monitoring ability of GB-RAR technology to identify the sub-millimeter level is demonstrated, and the monitoring method is verified. The validity and reliability of the method can be applied to real-time dynamic fine deformation diagnosis of island landslides. Its accuracy can meet the needs of dynamic change monitoring of island landslides, and it can become an important tool and means for early warning and treatment of landslides. The research is conducive to further enriching and improving the monitoring method system of island geological disasters in China, provides a scientific basis and technical support for early warning and disaster prevention and mitigation of island landslides, and can be popularized and applied in the monitoring of island landslides. Full article

(This article belongs to the Special Issue Application of Remote Sensing Methods to Monitor Coastal Zones)

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21 pages, 57165 KiB

Open AccessArticle

Assessment of a Smartphone-Based Camera System for Coastal Image Segmentation and Sargassum monitoring

by Nico Valentini and Yann Balouin

J. Mar. Sci. Eng. 2020, 8(1), 23; https://doi.org/10.3390/jmse8010023 - 4 Jan 2020

Cited by 30 | Viewed by 4667

Abstract

Coastal video monitoring has proven to be a valuable ground-based technique to investigate ocean processes. Presently, there is a growing need for automatic, technically efficient, and inexpensive solutions for image processing. Moreover, beach and coastal water quality problems are becoming significant and need attention. This study employs a methodological approach to exploit low-cost smartphone-based images for coastal image classification. The objective of this paper is to present a methodology useful for supervised classification for image semantic segmentation and its application for the development of an automatic warning system for Sargassum algae detection and monitoring. A pixel-wise convolutional neural network (CNN) has demonstrated optimal performance in the classification of natural images by using abstracted deep features. Conventional CNNs demand a great deal of resources in terms of processing time and disk space. Therefore, CNN classification with superpixels has recently become a field of interest. In this work, a CNN-based deep learning framework is proposed that combines sticky-edge adhesive superpixels. The results indicate that a cheap camera-based video monitoring system is a suitable data source for coastal image classification, with optimal accuracy in the range between 75% and 96%. Furthermore, an application of the method for an ongoing case study related to Sargassum monitoring in the French Antilles proved to be very effective for developing a warning system, aiming at evaluating floating algae and algae that had washed ashore, supporting municipalities in beach management. Full article

(This article belongs to the Special Issue Application of Remote Sensing Methods to Monitor Coastal Zones)

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16 pages, 8878 KiB

Open AccessArticle

Validating UAS-Based Photogrammetry with Traditional Topographic Methods for Surveying Dune Ecosystems in the Spanish Mediterranean Coast

by Luis Bañón, José Ignacio Pagán, Isabel López, Carlos Banon and Luis Aragonés

J. Mar. Sci. Eng. 2019, 7(9), 297; https://doi.org/10.3390/jmse7090297 - 30 Aug 2019

Cited by 12 | Viewed by 3437

Abstract

In the past few years, unmanned aerial systems (UAS) have achieved great popularity for civil uses. One of the present main uses of these devices is low-cost aerial photogrammetry, being especially useful in coastal environments. In this work, a high-resolution 3D model of a beach section in Guardamar del Segura (Spain) has been produced by employing a low maximum takeoff mass (MTOM) UAS, in combination with the use of structure-from-motion (SfM) techniques. An unprecedented extensive global navigation satellite system (GNSS) survey was simultaneously carried out to statistically validate the model by employing 1238 control points for that purpose. The results show good accuracy, obtaining a vertical root mean square error (RMSE) mean value of 0.121 m and a high point density, close to 30 pt/m², with similar or even higher quality than most coastal surveys performed with classical techniques. UAS technology permits the acquisition of topographic data with low time-consuming surveys at a high temporal frequency. Coastal managers can implement this methodology into their workflow to study the evolution of complex, highly anthropized dune-beach systems such as the one presented in this study, obtaining more accurate surveys at lower costs. Full article

(This article belongs to the Special Issue Application of Remote Sensing Methods to Monitor Coastal Zones)

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17 pages, 6433 KiB

Open AccessArticle

Video Sensing of Nearshore Bathymetry Evolution with Error Estimate

by Duong Hai Thuan, Rafael Almar, Patrick Marchesiello and Nguyen Trung Viet

J. Mar. Sci. Eng. 2019, 7(7), 233; https://doi.org/10.3390/jmse7070233 - 19 Jul 2019

Cited by 27 | Viewed by 4638

Abstract

Although coastal morphology results essentially from underwater sediment transports, the evolution of underwater beach profiles along the diverse coastlines of the world is still poorly documented. Bathymetry inversion from shore-based video cameras set forth a more systematic evaluation and is becoming more commonly used. However, there are limitations to this profiling method that are insufficiently assessed, undermining confidence in operational applications. In this paper, we investigate the daily evolution of a low tide terrace (LTT) in Nha Trang beach, Vietnam, under strong seasonal forcing: from weak wind waves during summer monsoon to moderate waves during winter monsoon. A new error estimation for depth inversion is presented based on tidal evaluation. The method compares video-based estimate and direct measurement of tidal amplitudes to provide a quality criterion. It reveals three types of errors, the main one being a deep water error associated with physical limits—loss of celerity-bathymetry relationship in deep water. This error is dependent on wave period and thus has a strong seasonal pattern in Vietnam. It is generally detrimental to depth inversion where wind waves are dominant (in summer here). On the contrary, the second error type is larger for larger waves and is located at breakpoint, altering wave detection. The last error type is due to nonlinear effects and wave setup in shallow water. After removing the faulty data, we finally present the first reliable three-year time-series of a beach profile in Nha Trang, Vietnam. A main result is the overall stability demonstrated for the LTT beach, with rapid exchange of sediment between the terrace and the upper beach during typhoons, monsoon events or seasonal cycles. These tropical environments may provide faster beach recovery compared with mid-latitude configurations. Full article

(This article belongs to the Special Issue Application of Remote Sensing Methods to Monitor Coastal Zones)

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18 pages, 9931 KiB

Open AccessArticle

Nearshore Wave Transformation Domains from Video Imagery

by Umberto Andriolo

J. Mar. Sci. Eng. 2019, 7(6), 186; https://doi.org/10.3390/jmse7060186 - 17 Jun 2019

Cited by 17 | Viewed by 7328

Abstract

Within the nearshore area, three wave transformation domains can be distinguished based on the wave properties: shoaling, surf, and swash zones. The identification of these distinct areas is relevant for understanding nearshore wave propagation properties and physical processes, as these zones can be related, for instance, to different types of sediment transport. This work presents a technique to automatically retrieve the nearshore wave transformation domains from images taken by coastal video monitoring stations. The technique exploits the pixel intensity variation of image acquisitions, and relates the pixel properties to the distinct wave characteristics. This allows the automated description of spatial and temporal extent of shoaling, surf, and swash zones. The methodology was proven to be robust, and capable of spotting the three distinct zones within the nearshore, both cross-shore and along-shore dimensions. The method can support a wide range of coastal studies, such as nearshore hydrodynamics and sediment transport. It can also allow a faster and improved application of existing video-based techniques for wave breaking height and depth-inversion, among others. Full article

(This article belongs to the Special Issue Application of Remote Sensing Methods to Monitor Coastal Zones)

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16 pages, 2780 KiB

Open AccessArticle

Seagrass Resource Assessment Using WorldView-2 Imagery in the Redfish Bay, Texas

by Lihong Su and Yuxia Huang

J. Mar. Sci. Eng. 2019, 7(4), 98; https://doi.org/10.3390/jmse7040098 - 10 Apr 2019

Cited by 18 | Viewed by 4233

Abstract

Seagrass meadows play important roles as habitats for many marine organisms, traps for sediment, and buffers against wave actions. The objective of this paper is to map seagrass meadows in the Redfish Bay, Texas from WorldView-2 imagery. Seagrass meadows grow in shallow and clear water areas in the Redfish Bay. The WorldView-2 satellite can acquire multispectral imagery from the bay bottom with 2 m spatial resolution 8 multispectral bands and 0.46 m panchromatic imagery. The top of atmosphere radiance was transformed to the bottom reflectance through the atmospheric correction and the water column correction. The object based image analysis was used to identify seagrass meadows distributions in the Redfish Bay. This investigation demonstrated that seagrass can be identified with 94% accuracy, although seagrass species cannot be satisfactorily recognized. The results implied that the WorldView-2 satellite imagery is a suitable data source for seagrass distribution mapping. Full article

(This article belongs to the Special Issue Application of Remote Sensing Methods to Monitor Coastal Zones)

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16 pages, 7058 KiB

Open AccessArticle

Low-Cost UAV for High-Resolution and Large-Scale Coastal Dune Change Monitoring Using Photogrammetry

by Quentin Laporte-Fauret, Vincent Marieu, Bruno Castelle, Richard Michalet, Stéphane Bujan and David Rosebery

J. Mar. Sci. Eng. 2019, 7(3), 63; https://doi.org/10.3390/jmse7030063 - 7 Mar 2019

Cited by 124 | Viewed by 9300

Abstract

In this paper, coastal dune data are collected at Truc Vert, SW France, using photogrammetry via Unmanned Aerial Vehicles (UAVs). A low-cost GoPro-equipped DJI Phantom 2 quadcopter and a 20 MPix camera-equipped DJI Phantom 4 Pro quadcopter UAVs were used to remotely sense the coastal dune morphology over large spatial scales (4 km alongshore, i.e., approximately 1 km² of beach-dune system), within a short time (less than 2 h of flight). The primary objective of this paper is to propose a low-cost and replicable approach which, combined with simple and efficient permanent Ground Control Point (GCP) set-up, can be applied to routinely survey upper beach and coastal dune morphological changes at high frequency (after each storm) and high resolution (0.1 m). Results show that a high-resolution and accurate Digital Surface Model (DSM) can be inferred with both UAVs if enough permanent GCPs are implemented. The more recent DJI Phantom 4 gives substantially more accurate DSM with a root-mean-square vertical error and bias of 0.05 m and −0.03 m, respectively, while the DSM inferred from the DJI Phantom 2 still largely meets the standard for coastal monitoring. The automatic flight plan procedure allows replicable surveys to address large-scale morphological evolution at high temporal resolution (e.g., weeks, months), providing unprecedented insight into the coastal dune evolution driven by marine and aeolian processes. The detailed morphological evolution of a 4-km section of beach-dune system is analyzed over a 6-month winter period, showing highly alongshore variable beach and incipient foredune wave-driven erosion, together with wind-driven inland migration of the established foredune by a few meters, and alongshore-variable sand deposition on the grey dune. In a context of widespread erosion, this photogrammetry approach via low-cost flexible and lightweight UAVs is well adapted for coastal research groups and coastal dune management stakeholders, including in developing countries where data are lacking. Full article

(This article belongs to the Special Issue Application of Remote Sensing Methods to Monitor Coastal Zones)

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21 pages, 5663 KiB

Open AccessArticle

Continuous Coastal Monitoring with an Automated Terrestrial Lidar Scanner

by Annika O’Dea, Katherine L. Brodie and Preston Hartzell

J. Mar. Sci. Eng. 2019, 7(2), 37; https://doi.org/10.3390/jmse7020037 - 7 Feb 2019

Cited by 55 | Viewed by 5833

Abstract

This paper details the collection, geo-referencing, and data processing algorithms for a fully-automated, permanently deployed terrestrial lidar system for coastal monitoring. The lidar is fixed on a 4-m structure located on a shore-backing dune in Duck, North Carolina. Each hour, the lidar collects a three-dimensional framescan of the nearshore region along with a 30-min two-dimensional linescan time series oriented directly offshore, with a linescan repetition rate of approximately 7 Hz. The data are geo-referenced each hour using a rigorous co-registration process that fits 11 fixed planes to a baseline scan to account for small platform movements, and the residual errors from the fit are used to assess the accuracy of the rectification. This process decreased the mean error (defined as the magnitude of the offset in three planes) over a two-year period by 24.41 cm relative to using a fixed rectification matrix. The automated data processing algorithm then filters and grids the data to generate a dry-beach digital elevation model (DEM) from the framescan along with hourly wave runup, hydrodynamic, and morphologic statistics from the linescan time series. The lidar has collected data semi-continuously since January 2015 (with gaps occurring while the lidar was malfunctioning or being serviced), resulting in an hourly data set spanning four years as of January 2019. Examples of data products and potential applications spanning a range of spatial and temporal scales relevant to coastal processes are discussed. Full article

(This article belongs to the Special Issue Application of Remote Sensing Methods to Monitor Coastal Zones)

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