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

Deadline for manuscript submissions: 1 December 2024 | Viewed by 13024

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Prof. Dr. Yuanzhi Zhang

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School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China
Interests: coastal environment; carbon neutrality; subsidence; erosion; coastline change
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Prof. Dr. Po Hu

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Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Interests: data modelling; upwelling, storms and waves; oceanic dynamics; climate change; typhoon and its impact
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Prof. Dr. Dongmei Chen

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Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Interests: remote sensing; GIS; change detection; feature extraction; environmental modeling
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Prof. Dr. Lin Li

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Guest Editor

Department of Earth Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
Interests: remote sensing; lunar and planetary geology; environmental sciences; data processing; radiative transfer models
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Special Issue Information

Dear Colleagues,

Remote sensing data are widely utilized to assess marine environments that have increasingly attracted public attention at global, regional and local scales. Water pollution caused by rapid urban development, sea level rise caused by the greenhouse effect, the melting of sea ice, storm surge, etc., have exacerbated issues induced by regional and global climate changes in the Earth sphere interactions, and these effects are often destructive to human development. Remote sensing, armed with recently emerging cloud computing, machine learning and AI technologies, is expected to play an unprecedent, yet important, role in assessing marine environments.

This Special Issue invites original research articles, as well as review articles that focus on ongoing efforts in using satellite or airborne remote sensing to understand the marine environment, land–ocean interactions, their response to global climate change and their interaction with human activities. The suggested topics are relevant, but not limited to, ocean data acquisition and pre-processing, data analysis and modeling, physical ocean parameters, sea level change, ocean–atmosphere interactions, coastal disasters, coastal ecosystem, water pollution, coastal (marine) engineering, coastal urbanization, as well as other coastal resilience themes.

Prof. Dr. Yuanzhi Zhang
Prof. Dr. Po Hu
Prof. Dr. Dongmei Chen
Prof. Dr. Lin 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. 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

application of machine learning methods in oceanography
data acquisition and pre-processing
coastal–ocean environments and ecosystems
coastal erosion and coastline change
estuarine engineering and coastal infrastructure
sea level rise and climate change
ocean-atmosphere interactions
typhoon impact and disaster
water pollution and red tide
wind field and wave estimation

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Published Papers (11 papers)

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Research

20 pages, 12257 KiB

Open AccessArticle

Study on Spatial Scale Selection Problem: Taking Port Spatial Expression as Example

by Yunzhuo Xu, Xinglu Xu, Wenyuan Wang and Zijian Guo

J. Mar. Sci. Eng. 2024, 12(11), 2057; https://doi.org/10.3390/jmse12112057 - 13 Nov 2024

Viewed by 376

Abstract

Spatial scale is a key factor, which affects the accuracy of spatial expression and further influences the spatial planning of a research area. In order to help improve the efficiency and accuracy of optimal scale selection for all sizes of research areas, a universal two-layer theoretical framework for optimal scale selection was proposed in this paper. Port area was taken as an example to systematically clarify the application of the proposed framework, and the scale selection model for port spatial expression was established. Least-squares-based mean change point analysis was introduced into the model, and the concept of a comprehensive change point was proposed to form the criterion for optimal port scale selection. First, an appropriate scale domain was preliminarily determined by the upper scale selection model. Then, the lower scale selection model determined the final appropriate scale domain and took the corresponding scale of a minimum comprehensive change point as the final optimal scale for port spatial expression. Finally, a port area in Qingdao in eastern China was taken to verify the feasibility of the proposed model, and the optimal scale was suggested to be 14 m. The proposed framework in this paper helps ensure the accuracy of spatial expression and reduce spatial data redundancy, and it can provide the methodological references for planners to better spatialize a research area, which will guarantee the subsequent spatial planning work. Full article

(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)

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11 pages, 6977 KiB

Open AccessArticle

Modal Decomposition of Internal Tides in the Luzon Strait through Two-Dimensional Fourier Bandpass Filtering

by Botao Xie, Qi Zhang, Feilong Lin, Weifang Jin and Zijian Cui

J. Mar. Sci. Eng. 2024, 12(9), 1477; https://doi.org/10.3390/jmse12091477 - 25 Aug 2024

Viewed by 816

Abstract

Internal tides are pivotal dynamic processes enhancing the mixing of oceanic waters and facilitating energy transfer across various scales within the ocean. In recent years, the proliferation of satellite altimetry observations has enabled global predictions of the elevation and phase of internal tides. This study, leveraging the advanced global internal tide prediction model known as the Multivariate Inversion of Ocean Surface Topography-Internal Tide Model (MIOST-IT), employs a two-dimensional Fourier bandpass filtering approach to decompose the internal tides in the Luzon Strait, thereby addressing the east–west directional blind zones inherent in along-track satellite altimetry-based modal decomposition. To further elucidate the propagation trajectories of individual tidal modes in different directions, we introduce the directional Fourier filter method to characterize the spatial distribution features of each modal internal tide in the vicinity of the Luzon Strait. This work significantly enhances the accuracy and reliability of extracting parameters for distinct modal internal tides, furnishing a scientific basis for subsequent studies on internal tide dynamics and model refinement. Full article

(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)

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27 pages, 32699 KiB

Open AccessArticle

Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types around Cheetham Wetlands, Port Phillip Bay, Australia

by Polina Lemenkova

J. Mar. Sci. Eng. 2024, 12(8), 1279; https://doi.org/10.3390/jmse12081279 - 29 Jul 2024

Cited by 2 | Viewed by 1016

Abstract

This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised classification. Technical approaches are based on using the ‘r.learn.train’ module, which employs the scikit-learn library of Python. The methodology includes the following algorithms: (1) random forest (RF), (2) support vector machine (SVM), (3) an ANN-based approach using a multi-layer perceptron (MLP) classifier, and (4) a decision tree classifier (DTC). The tested methods using AI demonstrated robust results for image classification, with the highest overall accuracy exceeding 98% and reached by the SVM and RF models. The presented scripting approach for GRASS GIS accurately detected changes in land cover types in southern Victoria over the period of 2013–2024. From our findings, the use of AI and ML algorithms offers effective solutions for coastal monitoring by analysis of change detection using multi-temporal RS data. The demonstrated methods have potential applications in coastal and wetland monitoring, environmental analysis and urban planning based on Earth observation data. Full article

(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)

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16 pages, 18398 KiB

Open AccessArticle

Remote-Sensing Estimation of Upwelling-Frequent Areas in the Adjacent Waters of Zhoushan (China)

by Teng Xiao, Jiajun Feng, Zhongfeng Qiu, Rong Tang, Aibo Zhao, Kapo Wong, Jin Yeu Tsou and Yuanzhi Zhang

J. Mar. Sci. Eng. 2024, 12(7), 1085; https://doi.org/10.3390/jmse12071085 - 27 Jun 2024

Cited by 1 | Viewed by 833

Abstract

Upwelling, which mixes deep and surface waters, significantly enhances the productivity of surface waters and plays a critical role in marine ecosystems, especially in key fishing areas like Zhoushan. This study utilized merged sea surface temperature data and an upwelling edge detection algorithm based on temperature gradients to analyze the characteristics of upwelling in Zhoushan and the Yangtze River Estuary over the past 28 years. The results indicate that upwelling in Zhoushan begins in April, peaks in July, gradually weakens, and disappears by October. The phenomenon is most pronounced during the summer months (June to August), with significant spatial distribution differences in April and September. Notably, high probability values of upwelling centers and core areas are mainly concentrated near Ma’an Island, Zhongjieshan Island, and Taohua Island. In these areas, upwelling remains stable during the summer, forming a unique “footprint” distribution pattern, and these are also the locations of the Zhoushan National Marine Ranch. From April to August, the extent of the upwelling area gradually decreases and stabilizes. These findings emphasize the frequent upwelling activity around Zhoushan and its significant contribution to the formation of local fisheries. Additionally, considering that the formation of natural upwelling in the East China Sea depends on the southern monsoon, the study suggests establishing artificial upwelling systems during periods unfavorable for natural upwelling, based on high probability areas, to enhance fishery yields and support the development of local fisheries. Full article

(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)

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20 pages, 8807 KiB

Open AccessArticle

Coral Shoals Detection from Optical Satellite Imagery Using Deep Belief Network Algorithm: A Case Study for the Xisha Islands, South China Sea

by Xiaomin Li, Yi Ma and Jie Zhang

J. Mar. Sci. Eng. 2024, 12(6), 922; https://doi.org/10.3390/jmse12060922 - 31 May 2024

Viewed by 695

Abstract

Coral islands and reefs are formed by the cementation of the remains of shallow water reef-building coral polyps and other reef dwelling organisms in tropical oceans. They can be divided into coral islands, coral sandbanks, coral reefs, and coral shoals, of which, Coral shoals are located below the depth datum and are not exposed even at low tide, and sometimes are distributed at water depths exceeding 30 m. Satellite images with wide spatial–temporal coverage have played a crucial role in coral island and reef monitoring, and remote sensing data with multiple platforms, sensors, and spatial and spectral resolutions are employed. However, the accurate detection of coral shoals remains challenging mainly due to the depth effect, that is, coral shoals, especially deeper ones, have very similar spectral characteristics to the sea in optical images. Here, an optical remote sensing detection method is proposed to rapidly and accurately detect the coral shoals using a deep belief network (DBN) from optical satellite imagery. The median filter is used to filter the DBN classification results, and the appropriate filtering window is selected according to the spatial resolution of the optical images. The proposed method demonstrated outstanding performance by validating and comparing the detection results of the Yinli Shoal. Moreover, the expected results are obtained by applying this method to other coral shoals in the Xisha Islands, including the Binmei Shoal, Beibianlang, Zhanhan Shoal, Shanhudong Shoal, and Yongnan Shoal. This detection method is expected to provide the coral shoals’ information rapidly once optical satellite images are available and cloud cover and tropical cyclones are satisfactory. The further integration of the detection results of coral shoals with water depth and other information can effectively ensure the safe navigation of ships. Full article

(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)

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18 pages, 13258 KiB

Open AccessArticle

The Characteristics of Submesoscale Eddies near the Coastal Regions of Eastern Japan: Insights from Sentinel-1 Imagery

by Gang Li, Yijun He, Jinghan Wen, Guoqiang Liu, Vladimir Kudryavtsev, Xiaojie Lu and William Perrie

J. Mar. Sci. Eng. 2024, 12(5), 761; https://doi.org/10.3390/jmse12050761 - 30 Apr 2024

Viewed by 1104

Abstract

A long-term time series of 319 Sentinel-1 SAR Imagery with Interferometric Wide Swath (IW) mode was used to study the characteristics of submesoscale eddies over Japanese coastal regions from 2015 to 2021, including spatiotemporal eddy properties and possible mechanisms of their formation. The [...] Read more.

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km) with a ratio of around 78% cyclones to around 22% anticyclones. Around 8% of the submesoscale eddies were found in these SAR images in winter since the submesoscale current-induced signals are masked by the stronger wind speed, compared with other seasons. Typical features of submesoscale eddies are summarized, providing a preliminary qualitative analysis of potential generation mechanisms specific to the eddy characteristics in this region. This study suggests that Sentinel-1 images are capable of providing insights into the observed submesoscale eddies near the coastal regions of eastern Japan, thereby contributing to the improved understanding of the generation of submesoscale eddies. Full article

(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)

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17 pages, 32322 KiB

Open AccessArticle

Automatic Detection of Floating Ulva prolifera Bloom from Optical Satellite Imagery

by Hailong Zhang, Quan Qin, Deyong Sun, Xiaomin Ye, Shengqiang Wang and Zhixin Zong

J. Mar. Sci. Eng. 2024, 12(4), 680; https://doi.org/10.3390/jmse12040680 - 19 Apr 2024

Cited by 1 | Viewed by 1258

Abstract

Annual outbreaks of floating Ulva prolifera blooms in the Yellow Sea have caused serious local environmental and economic problems. Rapid and effective monitoring of Ulva blooms from satellite observations with wide spatial-temporal coverage can greatly enhance disaster response efforts. Various satellite sensors and remote sensing methods have been employed for Ulva detection, yet automatic and rapid Ulva detection remains challenging mainly due to complex observation scenarios present in different satellite images, and even within a single satellite image. Here, a reliable and fully automatic method was proposed for the rapid extraction of Ulva features using the Tasseled-Cap Greenness (TCG) index from satellite top-of-atmosphere reflectance (R_TOA) data. Based on the TCG characteristics of Ulva and Ulva-free targets, a local adaptive threshold (LAT) approach was utilized to automatically select a TCG threshold for moving pixel windows. When tested on HY1C/D-Coastal Zone Imager (CZI) images, the proposed method, termed the TCG-LAT method, achieved over 95% Ulva detection accuracy though cross-comparison with the TCG and VBFAH indexes with a visually determined threshold. It exhibited robust performance even against complex water backgrounds and under non-optimal observing conditions with sun glint and cloud cover. The TCG-LAT method was further applied to multiple HY1C/D-CZI images for automatic Ulva bloom monitoring in the Yellow Sea in 2023. Moreover, promising results were obtained by applying the TCG-LAT method to multiple optical satellite sensors, including GF-Wide Field View Camera (GF-WFV), HJ-Charge Coupled Device (HJ-CCD), Sentinel2B-Multispectral Imager (S2B-MSI), and the Geostationary Ocean Color Imager (GOCI-II). The TCG-LAT method is poised for integration into operational systems for disaster monitoring to enable the rapid monitoring of Ulva blooms in nearshore waters, facilitated by the availability of near-real-time satellite images. Full article

(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)

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14 pages, 3865 KiB

Open AccessArticle

An Improved Method for Retrieving Subsurface Temperature Using the ConvLSTM Model in the Western Pacific Ocean

by Yuyuan Zhang, Yahao Liu, Yuan Kong and Po Hu

J. Mar. Sci. Eng. 2024, 12(4), 620; https://doi.org/10.3390/jmse12040620 - 4 Apr 2024

Viewed by 1237

Abstract

In the era of marine big data, making full use of multi-source satellite observations to accurately retrieve and predict the temperature structure of the ocean subsurface layer is very significant in advancing the understanding of oceanic processes and their dynamics. Considering the time dependence and spatial correlation of marine characteristics, this study employed the convolutional long short-term memory (ConvLSTM) method to retrieve the subsurface temperature in the Western Pacific Ocean from several types of satellite observations. Furthermore, considering the temperature’s vertical distribution, the retrieved results for the upper layer were iteratively used in the calculation for the deeper layer as input data to improve the algorithm. The results show that the retrieved results for the 100 to 500 m depth temperature using the 50 m layer in the calculation resulted in higher accuracy than those retrieved from the standard ConvLSTM method. The largest improvement was in the calculation for the 100 m layer, where the thermocline was located. The results indicate that our improved ConvLSTM method can increase the accuracy of subsurface temperature retrieval without additional input data. Full article

(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)

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13 pages, 1938 KiB

Open AccessArticle

Global Investigation of Wind–Wave Interaction Using Spaceborne SAR Measurements

by Huimin Li and Yijun He

J. Mar. Sci. Eng. 2024, 12(3), 433; https://doi.org/10.3390/jmse12030433 - 28 Feb 2024

Viewed by 1197

Abstract

Spaceborne synthetic aperture radar (SAR) has been widely acknowledged for its advantages in collecting ocean surface measurements under all weather conditions during day and night. Despite the strongly nonlinear imaging process, SAR measurements of ocean waves provide an invaluable resource for studies into wave dynamics at the global scale. In this study, we take advantage of a newly defined parameter, the mean cross-spectrum (MACS) at a discrete wavenumber along the sensor line-of-sight axis, to further investigate the ocean wave properties. With the range peak wavenumber extracted from the MACS profile, together with the collocated model winds, the inverse wave age (iwa) is estimated. As an indicator of local wind–wave coupling, the global map of the iwa depicts a distinct pattern, with larger iwa values observed in the storm tracks. In addition to the mean, stronger variability in the iwa is also found in the storm tracks, while the iwa remains relatively steady in the trade winds with lower variability. This makes the SAR-derived iwa a significant parameter in reflecting the varying degrees of wind–wave coupling in variable geographical locations across the ocean basins. It will help to promote the practical application of SAR measurements, as well as advancing our understanding of ocean wave dynamics. Full article

(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)

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21 pages, 11292 KiB

Open AccessArticle

Estimating Total Suspended Matter and Analyzing Influencing Factors in the Pearl River Estuary (China)

by Zhaoyue Ma, Yong Zhao, Wenjing Zhao, Jiajun Feng, Yingying Liu, Jin Yeu Tsou and Yuanzhi Zhang

J. Mar. Sci. Eng. 2024, 12(1), 167; https://doi.org/10.3390/jmse12010167 - 15 Jan 2024

Cited by 3 | Viewed by 1365

Abstract

This study on total suspended matter (TSM) in the Pearl River Estuary established a regression analysis model using Landsat 8 reflectance and measured TSM data, crucial for environmental management and engineering projects. High coefficients of determination (>0.6) were reported for the selected models. TSM concentration was notably high in 2013, peaking at 180 mg/L during the flood season and 80 mg/L in the dry season. In contrast, 2020 saw lower concentrations. Similar spatial distribution patterns were observed during dry and flood seasons, with high nearshore and low offshore TSM concentrations. Statistical analyses revealed natural factors (precipitation and runoff) as major influencers of the TSM distribution, with human activities presenting localized, limited impacts, except under long-term and large-scale conditions. Over time, the influence of large-scale water-based construction, such as the Hong Kong–Zhuhai–Macao Bridge, on TSM distribution became significant. Full article

(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)

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17 pages, 3502 KiB

Open AccessArticle

Remote Sensing Monitoring of Green Tide Disaster Using MODIS and GF-1 Data: A Case Study in the Yellow Sea

by Yanzhuo Men, Yingying Liu, Yufei Ma, Ka Po Wong, Jin Yeu Tsou and Yuanzhi Zhang

J. Mar. Sci. Eng. 2023, 11(12), 2212; https://doi.org/10.3390/jmse11122212 - 22 Nov 2023

Cited by 3 | Viewed by 1395

Abstract

Satellites with low-to-medium spatial resolution face challenges in monitoring the early and receding stages of green tides, while those with high spatial resolution tend to reduce the monitoring frequency of such phenomena. This study aimed to observe the emergence, evolution, and migratory patterns of green tides. We integrated GF-1 and MODIS imagery to collaboratively monitor the green tide disaster in the Yellow Sea during 2021. Initially, a linear regression model was employed to adjust the green tide coverage area as captured using MODIS imagery. We jointly observed the distribution range, drift path, and coverage area of the green tide and analyzed the drift path in coordination with offshore wind field and flow field data. Furthermore, we investigated the influence of SST, SSS, and rainfall on the 2021 green tide outbreak. The correlations calculated between SST, SSS, and precipitation with the changes in the area of the green tide were 0.43, 0.76, and 0.48, respectively. Our findings indicate that the large-scale green tide outbreak in 2021 may be associated with several factors. An increase in SST and SSS during the initial phase of the green tide established the essential conditions, while substantial rainfall during its developmental stage provided favorable conditions. Notably, the SSS exhibited a close association with the outbreak of the green tide. Full article

(This article belongs to the Special Issue New Advances in Marine Remote Sensing Applications)

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