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JMSE
Special Issues
Modeling and Monitoring of Coastal Ocean, Nearshore and Estuarine Environments
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Modeling and Monitoring of Coastal Ocean, Nearshore and Estuarine Environments

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: 25 January 2025 | Viewed by 5479

Special Issue Editors

Dr. Mustafa Kemal Cambazoglu

E-Mail Website
Guest Editor
The University of Southern Mississippi, Division of Marine Science, Stennis Space Center, Hattiesburg, MS, USA
Interests: coastal ocean modeling; coastal engineering; ocean engineering; coastal sediment transport
Dr. Zafer Defne

E-Mail Website
Guest Editor
US Geological Survey, Woods Hole, MA, USA
Interests: physical oceanography; numerical modeling; marine renewable energy; coastal wetlands; geospatial analysis

Special Issue Information

Dear Colleagues,

The coastal environment is becoming more vulnerable due to climate change and its associated impacts such as sea level rise, significant coastal erosion, coastal hazards and compound flooding, among others. Additionally, there is a vast number of natural resources in coastal ecosystems such as coastal wetlands, fisheries and shellfisheries that require sustainable management and restoration as well as major blue economy initiatives and new technology developments in coastal oceans. Advancements in computer modeling, new technologies in remote sensing, development of autonomous underwater and surface vehicles as well as the expanding in situ measurement networks enhance coastal predictions, forecasting and monitoring in coastal ocean, nearshore and estuarine environments.

We encourage submissions with research and applications of modeling systems and coastal monitoring systems to study hydrodynamics, morphodynamics, and hydrology of the coastal environments.

We are particularly encouraging submissions from the following areas:

(1) Coastal Ocean Modeling applications

These include coupled atmosphere, wave, ocean, sediment transport, ecosystem, riverine and hydrological modeling studies and applications that investigate or forecast the coastal ocean state at the near-coastal, shelf, estuarine and nearshore scales for:

  • Meteorological events and natural disasters such as storms, hurricanes, tsunamis, or meteorological tsunamis;
  • Any natural or artificial features for shore protection;
  • Remedies for improved boat navigation;
  • Ocean energy converters.

(2) Coastal Remote Sensing applications and Geospatial Techniques

Application of previously established geospatial techniques to new domains or development of new geospatial techniques to interpret remote sensed data from satellites, land stations (i.e., H/F radar) airplanes or unmanned aircraft systems applied to:

  • Coastal waters;
  • Shorelines (e.g., barrier islands, beaches, cliffs);
  • Beach features (e.g., dunes, sandbars);
  • Coastal wetlands (e.g., marshes, river deltas).

(3) Coastal land and/or ocean monitoring with autonomous systems, including

  • Uncrewed aerial systems (UAS); 
  • Remotely operated vehicles (ROV); 
  • Autonomous underwater vehicles (AUV);
  • Autonomous surface vehicles (ASV).

(4) Coastal Change Prediction/Forecast Systems

Application and development of operational or pre-operational prediction and forecast systems at near-coastal scales including but not limited to:

  • High-resolution applications of variable resolution grids, unstructured grids or nested systems;
  • Coupled model applications to study wind–wave, wave–current and ocean–atmosphere interactions;
  • Modeling systems with applications for water quality, habitat suitability, ecosystem and ecological effects;
  • Multi-resolution applications to understand the impact of global large-scale dynamics on the near-coastal environment.

(5) Synthesis of coastal data and models for improved decision making

Any studies that combine large scale in situ monitoring or geospatial monitoring with ocean modeling in the coastal environments to inform management actions and strategical coastal planning.

Dr. Mustafa Kemal Cambazoglu
Dr. Zafer Defne
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

  • coastal ocean modeling
  • coastal ocean remote sensing
  • coastal ocean monitoring
  • coastal ocean forecasting
  • autonomous systems
  • coastal ocean energy
  • coastal data synthesis
  • coastal sediment transport
  • nearshore hydrodynamics
  • estuarine circulation
  • coastal wetlands

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

18 pages, 6110 KiB  
Article
An Algorithm for Ship Detection in Complex Observation Scenarios Based on Mooring Buoys
by Wenbo Li, Chunlin Ning, Yue Fang, Guozheng Yuan, Peng Zhou and Chao Li
J. Mar. Sci. Eng. 2024, 12(7), 1226; https://doi.org/10.3390/jmse12071226 - 20 Jul 2024
Viewed by 1027
Abstract
Marine anchor buoys, as fixed-point profile observation platforms, are highly susceptible to the threat of ship collisions. Installing cameras on buoys can effectively monitor and collect evidence from ships. However, when using a camera to capture images, it is often affected by the [...] Read more.
Marine anchor buoys, as fixed-point profile observation platforms, are highly susceptible to the threat of ship collisions. Installing cameras on buoys can effectively monitor and collect evidence from ships. However, when using a camera to capture images, it is often affected by the continuous shaking of buoys and rainy and foggy weather, resulting in problems such as blurred images and rain and fog occlusion. To address these problems, this paper proposes an improved YOLOv8 algorithm. Firstly, the polarized self-attention (PSA) mechanism is introduced to preserve the high-resolution features of the original deep convolutional neural network and solve the problem of image spatial resolution degradation caused by shaking. Secondly, by introducing the multi-head self-attention (MHSA) mechanism in the neck network, the interference of rain and fog background is weakened, and the feature fusion ability of the network is improved. Finally, in the head network, this model combines additional small object detection heads to improve the accuracy of small object detection. Additionally, to enhance the algorithm’s adaptability to camera detection scenarios, this paper simulates scenarios, including shaking blur, rain, and foggy conditions. In the end, numerous comparative experiments on a self-made dataset show that the algorithm proposed in this study achieved 94.2% mAP50 and 73.2% mAP50:95 in various complex environments, which is superior to other advanced object detection algorithms. Full article
(This article belongs to the Special Issue Modeling and Monitoring of Coastal Ocean, Nearshore and Estuarine Environments)
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18 pages, 6624 KiB  
Article
Hydraulic Bottom Friction and Aerodynamic Roughness Coefficients for Mangroves in Southwest Florida, USA
by Stephen C. Medeiros
J. Mar. Sci. Eng. 2023, 11(11), 2053; https://doi.org/10.3390/jmse11112053 - 27 Oct 2023
Cited by 3 | Viewed by 1635
Abstract
Mangroves are a natural feature that enhance the resilience of natural and built coastal environments worldwide. They mitigate the impacts of hurricanes by dissipating energy from storm surges and waves, as well as reducing wind speeds. To incorporate mangroves into storm surge simulations, [...] Read more.
Mangroves are a natural feature that enhance the resilience of natural and built coastal environments worldwide. They mitigate the impacts of hurricanes by dissipating energy from storm surges and waves, as well as reducing wind speeds. To incorporate mangroves into storm surge simulations, surface roughness parameters that accurately capture mangrove effects are required. These effects are typically parameterized using Manning’s n bottom friction coefficient for overland flow and aerodynamic roughness length (z0) for wind speed reduction. This paper presents the suggested values for these surface roughness parameters based on field observation and a novel voxel-based processing method for laser scanning point clouds. The recommended Manning’s n and z0 values for mangroves in southwest Florida are 0.138 and 2.34 m, respectively. The data were also used to retrain a previously developed random forest model to predict these surface roughness parameters based on point cloud statistics. The addition of the mangrove sites to the training data produced mixed results, improving the predictions of z0 while weakening the predictions of Manning’s n. The paper concludes that machine learning models developed to predict environmental attributes using small datasets with predictor features containing subjective estimates are sensitive to the uncertainty in the field observations. Full article
(This article belongs to the Special Issue Modeling and Monitoring of Coastal Ocean, Nearshore and Estuarine Environments)
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25 pages, 9732 KiB  
Article
Forecasting Vertical Profiles of Ocean Currents from Surface Characteristics: A Multivariate Multi-Head Convolutional Neural Network–Long Short-Term Memory Approach
by Soumyashree Kar, Jason R. McKenna, Glenn Anglada, Vishwamithra Sunkara, Robert Coniglione, Steve Stanic and Landry Bernard
J. Mar. Sci. Eng. 2023, 11(10), 1964; https://doi.org/10.3390/jmse11101964 - 11 Oct 2023
Cited by 2 | Viewed by 1435
Abstract
While study of ocean dynamics usually involves modeling deep ocean variables, monitoring and accurate forecasting of nearshore environments is also critical. However, sensor observations often contain artifacts like long stretches of missing data and noise, typically after an extreme event occurrence or some [...] Read more.
While study of ocean dynamics usually involves modeling deep ocean variables, monitoring and accurate forecasting of nearshore environments is also critical. However, sensor observations often contain artifacts like long stretches of missing data and noise, typically after an extreme event occurrence or some accidental damage to the sensors. Such data artifacts, if not handled diligently prior to modeling, can significantly impact the reliability of any further predictive analysis. Therefore, we present a framework that integrates data reconstruction of key sea state variables and multi-step-ahead forecasting of current speed from the reconstructed time series for 19 depth levels simultaneously. Using multivariate chained regressions, the reconstruction algorithm rigorously tests from an ensemble of tree-based models (fed only with surface characteristics) to impute gaps in the vertical profiles of the sea state variables down to 20 m deep. Subsequently, a deep encoder–decoder model, comprising multi-head convolutional networks, extracts high-level features from each depth level’s multivariate (reconstructed) input and feeds them to a deep long short-term memory network for 24 h ahead forecasts of current speed profiles. In this work, we utilized Viking buoy data, and demonstrated that with limited training data, we could explain an overall 80% variation in the current speed profiles across the forecast period and the depth levels. Full article
(This article belongs to the Special Issue Modeling and Monitoring of Coastal Ocean, Nearshore and Estuarine Environments)
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