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Remote Sensing Techniques in Marine Environment
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Remote Sensing Techniques in Marine Environment

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

Deadline for manuscript submissions: closed (10 December 2023) | Viewed by 6559

Special Issue Editors

Dr. Ilaria Ferrando

E-Mail Website
Guest Editor
Department of Civil, Chemical and Environmental Engineering, University of Genoa, Via Montallegro 1, 16145 Genoa, Italy
Interests: surveying; remote sensing; geographic information system; spatial analysis; environmental engineering; GNSS meteorology; photogrammetry; topography; mapping
Dr. Andrea Lira Loarca

E-Mail Website1 Website2
Guest Editor
Department of Civil, Chemical and Environmental Engineering (DICCA), University of Genoa, Via Montallegro 1, 16145 Genoa, Italy
Interests: maritime climate: statistical characterization and simulation of maritime climate focusing on storm events; extreme sea level events: characterization of compound waves, tides, storm surge, run-up and sea-level rise events under climate change scenarios; climate-change coastal impacts: coastal flooding and erosion due to climate change extreme events

Special Issue Information

Dear Colleagues,

Remote sensing in marine and coastal environments has gained increasing popularity thanks to a variety of possible research fields and applications: the determination of water parameters (temperature, salinity, and turbidity), the mapping of shorelines, cliffs and seabed, and the study of marine phenomena impacts on natural and built environments. Several remote sensing techniques, such as photogrammetry, laser scanning, multibeam echosounders, Dopplers, and even satellite images, can be employed and integrated to effectively survey, detect, and measure marine and coastal features and their variations and evolution over time.

The purpose of the present Special Issue is to collect and publish the most exciting research with respect to the above subjects, with particular attention paid to both the remote sensed data themselves, i.e., accuracy, resolution, availability, processing strategies, and techniques, and their use for marine/coastal science purposes.

Dr. Ilaria Ferrando
Dr. Andrea Lira Loarca
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. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly 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 2600 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

  • marine/coastal survey and mapping
  • marine and coastal feature detection
  • geo-spatial data analysis
  • bathymetric and topographic survey
  • remote sensing techniques integration
  • digital 3D modelling

Published Papers (5 papers)

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Research

13 pages, 5080 KiB  
Article
Joint Inversion of Sea Surface Wind and Current Velocity Based on Sentinel-1 Synthetic Aperture Radar Observations
by Jingbei Sun, Huimin Li, Wenming Lin and Yijun He
J. Mar. Sci. Eng. 2024, 12(3), 450; https://doi.org/10.3390/jmse12030450 - 02 Mar 2024
Viewed by 721
Abstract
Spaceborne synthetic aperture radar (SAR) has been proven to be a useful technique for observing the sea surface wind and current over the open ocean given its all-weather data-gathering capability and high spatial resolution. In addition to the commonly used radar return magnitude [...] Read more.
Spaceborne synthetic aperture radar (SAR) has been proven to be a useful technique for observing the sea surface wind and current over the open ocean given its all-weather data-gathering capability and high spatial resolution. In addition to the commonly used radar return magnitude quantified by normalized radar cross section (NRCS), the Doppler centroid anomaly (DCA) has added another dimension of information. In this study, we combine the NRCS and DCA for a joint inversion of wind and surface current information using a Bayesian method. SAR-estimated Doppler is corrected by a series of steps, including the removal of scalloping effect and land correction. The cost function of this inversion scheme is constructed based on NRCS, DCA, and a background model wind. The retrieved wind results show the quality of performance through comparison with the in situ buoy measurements, showing a mean bias and a root-mean-square error (RMSE) of 0.33 m/s and 1.45 m/s for wind speed and 6.94° and 35.74° for wind direction, respectively. The correlation coefficients for wind speed and direction reach 0.931 and 0.661, respectively. Based on the obtained wind field, the line-of-sight velocity of the sea surface current is then derived by removing the wind contribution using the empirical model. The results show a consistent spatial pattern relative to the high-frequency radars, with the comparison relative to the drifter-measured current velocity exhibiting a mean bias of 0.02 m/s and RMSE of 0.32 m/s, demonstrating the reliability of the proposed inversion scheme. Such results will serve as a prototype for future spaceborne sensors to combine the radar return and Doppler information for the joint retrieval of wind vector and surface current velocity. This technique could be readily extended to the radar configuration of rotating beams for monitoring winds and current vectors. Full article
(This article belongs to the Special Issue Remote Sensing Techniques in Marine Environment)
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21 pages, 7330 KiB  
Article
Spatial–Temporal Variations in Regional Sea Level Change in the South China Sea over the Altimeter Era
by Lujie Xiong, Yanping Jiao, Fengwei Wang and Shijian Zhou
J. Mar. Sci. Eng. 2023, 11(12), 2360; https://doi.org/10.3390/jmse11122360 - 14 Dec 2023
Viewed by 741
Abstract
This study utilizes 27 years of sea level anomaly (SLA) data obtained from satellite altimetry to investigate spatial–temporal variations in the South China Sea (SCS). The local mean decomposition (LMD) method is applied to decompose the sea level data into three components: high-frequency, [...] Read more.
This study utilizes 27 years of sea level anomaly (SLA) data obtained from satellite altimetry to investigate spatial–temporal variations in the South China Sea (SCS). The local mean decomposition (LMD) method is applied to decompose the sea level data into three components: high-frequency, low-frequency, and trend components. By removing the influence of high-frequency components, multiple time series of regular sea level changes with significant physical significance are obtained. The results indicate that the average multi-year SLA is 50.16 mm, with a linear trend of 3.91 ± 0.12 mm/a. The wavelet analysis method was employed to examine the significant annual and 1.5-year periodic signals in the SCS SLA series. At the seasonal scale, the sea level rise in coastal areas during autumn and winter surpasses that of spring and summer. Moreover, there are generally opposing spatial distributions between spring and autumn, as well as between summer and winter. The linear trends in multi-year SLA for the four seasons are 3.70 ± 0.13 mm/a, 3.66 ± 0.16 mm/a, 3.49 ± 0.16 mm/a, and 3.74 ± 0.33 mm/a, respectively. The causes of SCS sea level change are examined in relation to phenomena such as monsoons, the Kuroshio Current, and El Niño–Southern Oscillation (ENSO). Based on the empirical orthogonal function (EOF) analysis of SCS SLA, the contributions of the first three modes of variance are determined to be 34.09%, 28.84%, and 8.40%, respectively. The temporal coefficients and spatial distribution characteristics of these modes confirm their associations with ENSO, monsoons, and the double-gyre structure of SCS sea surface temperature. For instance, ENSO impacts SCS sea level change through atmospheric circulation, predominantly affecting the region between 116° E and 120° E longitude, and 14° N and 20° N latitude. Full article
(This article belongs to the Special Issue Remote Sensing Techniques in Marine Environment)
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24 pages, 50912 KiB  
Article
Estimation of Water Quality Parameters in Oligotrophic Coastal Waters Using Uncrewed-Aerial-Vehicle-Obtained Hyperspectral Data
by Morena Galešić Divić, Marija Kvesić Ivanković, Vladimir Divić, Mak Kišević, Marko Panić, Predrag Lugonja, Vladimir Crnojević and Roko Andričević
J. Mar. Sci. Eng. 2023, 11(10), 2026; https://doi.org/10.3390/jmse11102026 - 22 Oct 2023
Cited by 1 | Viewed by 1010
Abstract
Water quality monitoring in coastal areas and estuaries poses significant challenges due to the intricate interplay of hydrodynamic, chemical, and biological processes, regardless of the chosen monitoring methods. In this study, we analyzed the applicability of different monitoring sources using in situ data, [...] Read more.
Water quality monitoring in coastal areas and estuaries poses significant challenges due to the intricate interplay of hydrodynamic, chemical, and biological processes, regardless of the chosen monitoring methods. In this study, we analyzed the applicability of different monitoring sources using in situ data, uncrewed-aerial-vehicle (UAV)-mounted hyperspectral sensing, and Sentinel-2-based multispectral imagery. In the first part of the study, we evaluated the applicability of existing empirical algorithms for water quality (WQ) parameter retrieval using hyperspectral, simulated multispectral, and satellite multispectral datasets and in situ measurements. In particular, we focused on three optically active WQ parameters: chlorophyll a (Chl,a), turbidity (TUR), and colored dissolved organic matter (CDOM) in oligotrophic coastal waters. We observed that most existing algorithms performed poorly when applied to different reflectance datasets, similar to previous findings in small and optically complex water bodies. Hence, we proposed a novel set of locally based empirical algorithms tailored for determining water quality parameters, which constituted the second part of our study. The newly developed regression-based algorithms utilized all possible combinations of spectral bands derived from UAV-generated hyperspectral data and exhibited coefficients of determination exceeding 0.9 for the three considered WQ parameters. The presented two-part approach was demonstrated in the semi-enclosed area of Kaštela Bay and the Jadro River estuary in the Central Eastern Adriatic Sea. This study introduces a promising and efficient screening method for UAV-based water quality monitoring in coastal areas worldwide. Such an approach may support decision-making processes related to coastal management and ultimately contribute to the conservation of coastal water ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing Techniques in Marine Environment)
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32 pages, 27098 KiB  
Article
Deriving Coastal Shallow Bathymetry from Sentinel 2-, Aircraft- and UAV-Derived Orthophotos: A Case Study in Ligurian Marinas
by Lorenza Apicella, Monica De Martino, Ilaria Ferrando, Alfonso Quarati and Bianca Federici
J. Mar. Sci. Eng. 2023, 11(3), 671; https://doi.org/10.3390/jmse11030671 - 22 Mar 2023
Cited by 4 | Viewed by 1525
Abstract
Bathymetric surveys of shallow waters are increasingly necessary for navigational safety and environmental studies. In situ surveys with floating acoustic sensors allow the collection of high-accuracy bathymetric data. However, such surveys are often unfeasible in very shallow waters in addition to being expensive [...] Read more.
Bathymetric surveys of shallow waters are increasingly necessary for navigational safety and environmental studies. In situ surveys with floating acoustic sensors allow the collection of high-accuracy bathymetric data. However, such surveys are often unfeasible in very shallow waters in addition to being expensive and requiring specific sectorial skills for the acquisition and processing of raw data. The increasing availability of optical images from Uncrewed Aerial Vehicles, aircrafts and satellites allows for bathymetric reconstruction from images thanks to the application of state-of-the-art algorithms. In this paper, we illustrate a bathymetric reconstruction procedure involving the classification of the seabed, the calibration of the algorithm for each class and the subsequent validation. We applied this procedure to high-resolution, UAV-derived orthophotos, aircraft orthophotos and Sentinel-2 Level-2A images of two marinas along the western Ligurian coastline in the Mediterranean Sea and validated the results with bathymetric data derived from echo-sounder surveys. Our findings showed that the aircraft-derived bathymetry is generally more accurate than the UAV-derived and Sentinel-2 bathymetry in all analyzed scenarios due to the smooth color of the aircraft orthophotos and their ability to reproduce the seafloor with a considerable level of detail. Full article
(This article belongs to the Special Issue Remote Sensing Techniques in Marine Environment)
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16 pages, 8794 KiB  
Article
Marine Radar Oil Spill Extraction Based on Texture Features and BP Neural Network
by Rong Chen, Baozhu Jia, Long Ma, Jin Xu, Bo Li and Haixia Wang
J. Mar. Sci. Eng. 2022, 10(12), 1904; https://doi.org/10.3390/jmse10121904 - 05 Dec 2022
Cited by 2 | Viewed by 1679
Abstract
Marine oil spills are one of the major threats to marine ecological safety, and the rapid identification of oil films is of great significance to the emergency response. Marine radar can provide data for marine oil spill detection; however, to date, it has [...] Read more.
Marine oil spills are one of the major threats to marine ecological safety, and the rapid identification of oil films is of great significance to the emergency response. Marine radar can provide data for marine oil spill detection; however, to date, it has not been commonly reported. Traditional marine radar oil spill research is mostly based on grayscale segmentation, and its accuracy depends entirely on the selection of the threshold. With the development of algorithm technology, marine radar oil spill extraction has gradually come to focus on artificial intelligence, and the study of oil spills based on machine learning has begun to develop. Based on X-band marine radar images collected from the Dalian 716 incident, this study used image texture features, the BP neural network classifier, and threshold segmentation for oil spill extraction. Firstly, the original image was pre-processed, to eliminate co-channel interference noise. Secondly, texture features were extracted and analyzed by the gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA); then, the BP neural work was used to obtain the effective wave region. Finally, threshold segmentation was performed, to extract the marine oil slicks. The constructed BP neural network could achieve 93.75% classification accuracy, with the oil film remaining intact and the segmentation range being small; the extraction results were almost free of false positive targets, and the actual area of the oil film was calculated to be 42,629.12 m2. The method proposed in this paper can provide a reference for real-time monitoring of oil spill incidents. Full article
(This article belongs to the Special Issue Remote Sensing Techniques in Marine Environment)
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