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

Journal Browser

► Journal Browser

Remote Sensing of the Oceans: Blue Economy and Marine Pollution

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

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 39716

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution book cover image

Share This Special Issue

Special Issue Editors

Dr. Andrea Buono

E-Mail Website
Guest Editor

Engineering Department, University of Naples “Parthenope”, 80143 Naples, Italy
Interests: electromagnetic modeling; SAR; polarimetry; ocean; coastal areas
Special Issues, Collections and Topics in MDPI journals

Dr. Rafael Lemos Paes

E-Mail Website
Guest Editor

PhD, Institute of Advanced Studies, Remote Sensing Department, Trevo Coronel Aviador José Alberto Albano do Amarante, 1 – Putim, 12.228-001, Sao Josè Dos Campos, Brazil
Interests: SAR; polarimetry; image processing; targets; machine learning

Dr. Yu Li

E-Mail Website
Guest Editor

Faculty of Information Technology, Beijing University of Technology, No. 100 PingLeYuan Road, Chaoyang District, Beijing 100124, China
Interests: SAR; polarimetry; electromagnetic; image processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of remote sensing tools to monitor the oceans is an enabling application in the fields of environment preservation and the blue economy. In fact, marine pollution due to anthropogenic activities, such as oil spills, plastic litter, and other debris, represents a dramatic threat to the marine ecosystem. In addition, there is a growing urgent need to exploit and explore new “green” sources and renewable energy, as well as a need to monitor all the critical infrastructures related to the blue economy. In this framework, a huge amount of multi-platform and multi-sensor remote sensing data is available across the scientific community that calls for the development of new methods and algorithms, as well as better assessment of state-of-art approaches. Hence, even though remote sensing tools for ocean monitoring are a well-established reality, we are still seeking for innovative methods and more accurate models.

The main goal of this Special Issue is to provide a reference framework for state-of-art remote sensing methods to observe marine pollution and to support the blue economy, as well as to promote and boost the most advanced methods and techniques in related fields. The topics of this Special Issue include, but are not limited to, the following subjects:

Target monitoring using multi-platform and multi-sensor data
New algorithms for marine resources exploration and exploitation
New algorithms for marine ecosystem preservation
Modeling and retrieval of geophysical ocean features
Oil/gas fields
Wind Farms
Aquacultures
Plastic litter
Marine debris
Oil spills

Dr. Andrea Buono
Dr. Rafael Lemos Paes
Dr. Yu Li
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

ocean
monitoring and modeling
pollution
resource exploration
renewable energy
critical infrastructures

Published Papers (12 papers)

Download All Papers

Editorial

Jump to: Research, Other

3 pages, 164 KiB

Open AccessEditorial

Editorial for the Special Issue “Remote Sensing of the Oceans: Blue Economy and Marine Pollution”

by Andrea Buono, Yu Li and Rafael Lemos Paes

Remote Sens. 2021, 13(8), 1522; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081522 - 15 Apr 2021

Cited by 2 | Viewed by 1723

Abstract

Oceans represent an extraordinary source of resources that needs to be preserved while being exploited [...] Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

Research

Jump to: Editorial, Other

17 pages, 1463 KiB

Open AccessArticle

A Sensitivity Analysis on the Spectral Signatures of Low-Backscattering Sea Areas in Sentinel-1 SAR Images

by Valeria Corcione, Andrea Buono, Ferdinando Nunziata and Maurizio Migliaccio

Remote Sens. 2021, 13(6), 1183; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061183 - 19 Mar 2021

Cited by 16 | Viewed by 2197

Abstract

Satellite synthetic aperture radar (SAR) is a unique tool to collect measurements over sea surface but the physical interpretation of such data is not always straightforward. Among the different sea targets of interest, low-backscattering areas are often associated to marine oil pollution even if several physical phenomena may also result in low-backscattering patches at sea. In this study, the effects of low-backscattering areas of anthropogenic and natural origin on the azimuth autocorrelation function (AACF) are analyzed using VV-polarized SAR measurements. Two objective metrics are introduced to quantify the deviation of the AACF evaluated over low-backscattering areas with reference to slick-free sea surface. Experiments, undertaken on six Sentinel-1 SAR scenes, collected in Interferometric Wide Swath VV+VH imaging mode over large low-backscattering areas of different origin under low-to-moderate wind conditions (speed ≤ 7 m/s), spanning a wide range of incidence angles (from about 30° up to 46°), demonstrated that the AACF evaluated within low-backscattering sea areas remarkably deviates from the slick-free sea surface one and the largest deviation is observed over oil slicks. Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

► Show Figures

Figure 1

18 pages, 4988 KiB

Open AccessArticle

Shoreline Extraction in SAR Image Based on Advanced Geometric Active Contour Model

by Xueyun Wei, Wei Zheng, Caiping Xi and Shang Shang

Remote Sens. 2021, 13(4), 642; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040642 - 10 Feb 2021

Cited by 19 | Viewed by 2356

Abstract

Rapid and accurate extraction of shoreline is of great significance for the use and management of sea area. Remote sensing has a strong ability to obtain data and has obvious advantages in shoreline survey. Compared with visible-light remote sensing, synthetic aperture radar (SAR) has the characteristics of all-weather and all-day working. It has been well-applied in shoreline extraction. However, due to the influence of natural conditions there is a problem of weak boundary in extracting shoreline from SAR images. In addition, the complex micro topography near the shoreline makes it difficult for traditional visual interpretation and image edge detection methods based on edge information to obtain a continuous and complete shoreline in SAR images. In order to solve these problems, this paper proposes a method to detect the land–sea boundary based on a geometric active contour model. In this method, a new symbolic pressure function is used to improve the geometric active-contour model, and the global regional smooth information is used as the convergence condition of curve evolution. Then, the influence of different initial contours on the number and time of iterations is studied. The experimental results show that this method has the advantages of fewer iteration times, good stability and high accuracy. Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

► Show Figures

Graphical abstract

21 pages, 6824 KiB

Open AccessArticle

Combining Segmentation Network and Nonsubsampled Contourlet Transform for Automatic Marine Raft Aquaculture Area Extraction from Sentinel-1 Images

by Yi Zhang, Chengyi Wang, Yuan Ji, Jingbo Chen, Yupeng Deng, Jing Chen and Yongshi Jie

Remote Sens. 2020, 12(24), 4182; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244182 - 21 Dec 2020

Cited by 28 | Viewed by 2719

Abstract

Marine raft aquaculture (MFA) plays an important role in the marine economy and ecosystem. With the characteristics of covering a large area and being sparsely distributed in sea area, MFA monitoring suffers from the low efficiency of field survey and poor data of optical satellite imagery. Synthetic aperture radar (SAR) satellite imagery is currently considered to be an effective data source, while the state-of-the-art methods require manual parameter tuning under the guidance of professional experience. To preclude the limitation, this paper proposes a segmentation network combined with nonsubsampled contourlet transform (NSCT) to extract MFA areas using Sentinel-1 images. The proposed method is highlighted by several improvements based on the feature analysis of MFA. First, the NSCT was applied to enhance the contour and orientation features. Second, multiscale and asymmetric convolutions were introduced to fit the multisize and strip-like features more effectively. Third, both channel and spatial attention modules were adopted in the network architecture to overcome the problems of boundary fuzziness and area incompleteness. Experiments showed that the method can effectively extract marine raft culture areas. Although further research is needed to overcome the problem of interference caused by excessive waves, this paper provides a promising approach for periodical monitoring MFA in a large area with high efficiency and acceptable accuracy. Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

► Show Figures

Graphical abstract

18 pages, 28613 KiB

Open AccessArticle

Sea Echoes for Airborne HF/VHF Radar: Mathematical Model and Simulation

by Fan Ding, Chen Zhao, Zezong Chen and Jian Li

Remote Sens. 2020, 12(22), 3755; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223755 - 15 Nov 2020

Cited by 3 | Viewed by 2084

Abstract

Currently, shore-based HF radars are widely used for coastal observations, and airborne radars are utilized for monitoring the ocean with a relatively large coverage offshore. In order to take the advantage of airborne radars, the theoretical mechanism of airborne HF/VHF radar for ocean surface observation has been studied in this paper. First, we describe the ocean surface wave height with the linear and nonlinear parts in a reasonable mathematical form and adopt the small perturbation method (SPM) to compute the HF/VHF radio scattered field induced by the sea surface. Second, the normalized radar cross section (NRCS) of the ocean surface is derived by tackling the field scattered from the random sea as a stochastic process. Third, the NRCS is simulated using the SPM under different sea states, at various radar operating frequencies and incident angles, and then the influences of these factors on radar sea echoes are investigated. At last, a comparison of NRCS using the SPM and the generalized function method (GFM) is done and analyzed. The mathematical model links the sea echoes and the ocean wave height spectrum, and it also offers a theoretical basis for designing a potential airborne HF/VHF radar for ocean surface remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

► Show Figures

Graphical abstract

23 pages, 5411 KiB

Open AccessArticle

Unmanned Aerial Vehicles for Debris Survey in Coastal Areas: Long-Term Monitoring Programme to Study Spatial and Temporal Accumulation of the Dynamics of Beached Marine Litter

by Silvia Merlino, Marco Paterni, Andrea Berton and Luciano Massetti

Remote Sens. 2020, 12(8), 1260; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081260 - 16 Apr 2020

Cited by 61 | Viewed by 6809

Abstract

Unmanned aerial vehicles (UAVs) are becoming increasingly accessible tools with widespread use as environmental monitoring systems. They can be used for anthropogenic marine debris survey, a recently growing research field. In fact, while the increasing efforts for offshore investigations lead to a considerable collection of data on this type of pollution in the open sea, there is still little knowledge of the materials deposited along the coasts and the mechanism that leads to their accumulation pattern. UAVs can be effective in bridging this gap by increasing the amount of data acquired to study coastal deposits, while also limiting the anthropogenic impact in protected areas. In this study, UAVs have been used to acquire geo-referenced RGB images in a selected zone of a protected marine area (the Migliarino, Massacciuccoli, and San Rossore park near Pisa, Italy), during a long-term (ten months) monitoring programme. A post processing system based on visual interpretation of the images allows the localization and identification of the anthropogenic marine debris within the scanned area, and the estimation of their spatial and temporal distribution in different zones of the beach. These results provide an opportunity to investigate the dynamics of accumulation over time, suggesting that our approach might be appropriate for monitoring and collecting such data in isolated, and especially in protected, areas with significant benefits for different types of stakeholders. Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

► Show Figures

Graphical abstract

26 pages, 5160 KiB

Open AccessArticle

Oil Spill Detection in Quad-Polarimetric SAR Images Using an Advanced Convolutional Neural Network Based on SuperPixel Model

by Jin Zhang, Hao Feng, Qingli Luo, Yu Li, Jujie Wei and Jian Li

Remote Sens. 2020, 12(6), 944; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12060944 - 14 Mar 2020

Cited by 42 | Viewed by 4303

Abstract

Oil spill detection plays an important role in marine environment protection. Quad-polarimetric Synthetic Aperture Radar (SAR) has been proved to have great potential for this task, and different SAR polarimetric features have the advantages to recognize oil spill areas from other look-alikes. In this paper we proposed an oil spill detection method based on convolutional neural network (CNN) and Simple Linear Iterative Clustering (SLIC) superpixel. Experiments were conducted on three Single Look Complex (SLC) quad-polarimetric SAR images obtained by Radarsat-2 and Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). Several groups of polarized parameters, including H/A/Alpha decomposition, Single-Bounce Eigenvalue Relative Difference (SERD), correlation coefficients, conformity coefficients, Freeman 3-component decomposition, Yamaguchi 4-component decomposition were extracted as feature sets. Among all considered polarimetric features, Yamaguchi parameters achieved the highest performance with total Mean Intersection over Union (MIoU) of 90.5%. It is proved that the SLIC superpixel method significantly improved the oil spill classification accuracy on all the polarimetric feature sets. The classification accuracy of all kinds of targets types were improved, and the largest increase on mean MIoU of all features sets was on emulsions by 21.9%. Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

► Show Figures

Graphical abstract

16 pages, 7641 KiB

Open AccessArticle

Oil Spill Discrimination by Using General Compact Polarimetric SAR Features

by Junjun Yin, Jian Yang, Liangjiang Zhou and Liying Xu

Remote Sens. 2020, 12(3), 479; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030479 - 03 Feb 2020

Cited by 7 | Viewed by 2803

Abstract

Ocean surveillance is one of the important applications of synthetic aperture radar (SAR). Polarimetric SAR provides multi-channel information and shows great potential for monitoring ocean dynamic environments. Oil spills are a form of pollution that can seriously affect the marine ecosystem. Dual-polarimetric SAR systems are usually used for routine ocean surface monitoring. The hybrid dual-pol SAR imaging mode, known as compact polarimetry, can provide more information than the conventional dual-pol imaging modes. However, backscatter measurements of the hybrid dual-pol mode depend on the transmit wave polarization, which results in lacking consistent interpretation for various compact polarimetric (CP) images. In this study, we will explore the capability of different CP modes for oil spill detection and discrimination. Firstly, we introduce the general CP formalism method to formulate an arbitrary CP backscattered wave, such that the target scattering vector is characterized in the same framework for all CP modes. Then, a recently proposed CP decomposition method is investigated to reveal the backscattering properties of oil spills and their look-alikes. Both intensity and polarimetric features are studied to analyze the optimal CP mode for oil spill observation. Spaceborne polarimetric SAR data sets collected over natural oil slicks and experimental biogenic slicks are used to demonstrate the capability of the general CP mode for ocean surface surveillance. Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

► Show Figures

Graphical abstract

18 pages, 1073 KiB

Open AccessArticle

Eddy Detection in HF Radar-Derived Surface Currents in the Gulf of Naples

by Leonardo Bagaglini, Pierpaolo Falco and Enrico Zambianchi

Remote Sens. 2020, 12(1), 97; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010097 - 27 Dec 2019

Cited by 10 | Viewed by 3503

Abstract

Submesoscale eddies play an important role in the energy transfer from the mesoscale down to the dissipative range, as well as in tracer transport. They carry inorganic matter, nutrients and biomass; in addition, they may act as pollutant conveyors. However, synoptic observations of these features need high resolution sampling, in both time and space, making their identification challenging. Therefore, HF coastal radar were and are successfully used to accurately identify, track and describe them. In this paper we tested two already existing algorithms for the automated detection of submesoscale eddies. We applied these algorithms to HF radar velocity fields measured by a network of three radar systems operating in the Gulf of Naples. Both methods showed shortcomings, due to the high non-geostrophy of the observed currents. For this reason we developed a third, novel algorithm that proved to be able to detect highly asymmetrical eddies, often not properly identified by the previous ones. We used the results of the application of this algorithm to estimate the eddy boundary profiles and the eddy spatial distribution. Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

► Show Figures

Graphical abstract

19 pages, 6815 KiB

Open AccessArticle

Influence of Tropical Cyclone Intensity and Size on Storm Surge in the Northern East China Sea

by Jian Li, Yijun Hou, Dongxue Mo, Qingrong Liu and Yuanzhi Zhang

Remote Sens. 2019, 11(24), 3033; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11243033 - 16 Dec 2019

Cited by 14 | Viewed by 5043

Abstract

Typhoon storm surge research has always been very important and worthy of attention. Less is studied about the impact of tropical cyclone size (TC size) on storm surge, especially in semi-enclosed areas such as the northern East China Sea (NECS). Observational data for Typhoon Winnie (TY9711) and Typhoon Damrey (TY1210) from satellite and tide stations, as well as simulation results from a finite-volume coastal ocean model (FVCOM), were developed to study the effect of TC size on storm surge. Using the maximum wind speed (MXW) to represent the intensity of the tropical cyclone and seven-level wind circle range (R7) to represent the size of the tropical cyclone, an ideal simulation test was conducted. The results indicate that the highest storm surge occurs when the MXW is 40–45 m/s, that storm surge does not undergo significant change with the RWM except for the area near the center of typhoon and that the peak surge values are approximately a linear function of R7. Therefore, the TC size should be considered when estimating storm surge, particularly when predicting marine-economic effects and assessing the risk. Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

► Show Figures

Graphical abstract

18 pages, 5524 KiB

Open AccessArticle

Vertical Migration of the Along-Slope Counter-Flow and Its Relation with the Kuroshio Intrusion off Northeastern Taiwan

by Yuanshou He, Po Hu, Yuqi Yin, Ze Liu, Yahao Liu, Yijun Hou and Yuanzhi Zhang

Remote Sens. 2019, 11(22), 2624; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11222624 - 09 Nov 2019

Cited by 10 | Viewed by 2351

Abstract

Based on satellite and analysis data and in situ observations acquired during May 23, 2017 to May 19, 2018, the spatiotemporal variations of the along-slope counter-flow off northeastern Taiwan were investigated. It was observed that the along-slope counter-flow in the subsurface layer was uplifted and lowered significantly during the study period. The counter-flow was significantly uplifted (lowered) while the sea surface was during an interval of positive (negative) geostrophic velocity anomaly (GVA) curl. The vertical migration of the counter-flow was also found closely linked with the Kuroshio intrusion (KI) to the northeast of Taiwan. The depths of both the upper boundary and the axis of the counter-flow were found proportional to the KI variance along the western continental slope off northeastern Taiwan. More importantly, it was established that the variation of the KI to the northeast of Taiwan had better correlation with the counter-flow than the Kuroshio derived from altimetry data. Thus, further study of the variation and mechanism of the along-slope counter-flow is needed to improve the understanding and prediction of the KI in the area of northeastern Taiwan, as well as the biochemical systems and marine economy in the East China Sea in the future. Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

► Show Figures

Figure 1

Other

Jump to: Editorial, Research

12 pages, 1587 KiB

Open AccessTechnical Note

Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization

by Lei Yu, Chunsheng Li, Jie Chen, Pengbo Wang and Zhirong Men

Remote Sens. 2020, 12(20), 3302; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203302 - 11 Oct 2020

Cited by 5 | Viewed by 1973

Abstract

To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO. Full article

(This article belongs to the Special Issue Remote Sensing of the Oceans: Blue Economy and Marine Pollution)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Remote Sensing of the Oceans: Blue Economy and Marine Pollution

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (12 papers)

Editorial

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI