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Remote Sensing and GIS Applications for Coastal Morphodynamic Systems
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Remote Sensing and GIS Applications for Coastal Morphodynamic Systems

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 (15 April 2025) | Viewed by 3861

Special Issue Editor

Dr. Yann Balouin

E-Mail Website
Guest Editor
Bureau de Recherches Géologiques et Minières, 1039 Rue de Pinville, 34000 Montpellier, France
Interests: coastal engineering; nearshore hydrodynamic and morphodynamic processes; coastal erosion; coastal observations; storm impacts, early warning systems; video-derived coastal indicators

Special Issue Information

Dear Colleagues,

Waves, currents, tides, climate change and human activities are important factors affecting coastal morphology. Due to the complex and dynamic nature of coastal environments, studying and monitoring these processes is challenging. Remote sensing and geographic information systems (GIS), as important monitoring means, can be used to deeply understand the coastal conditions and coastal evolution under different geomorphic environments and time scales through remote sensing observation.

The purpose of this Special Issue is to publish innovative research and application-oriented works related to coastal morphological monitoring. Potential topics include, but are not limited to, the following:

  1. Coastal geomorphology (Coastal evolution, erosion, Sediment dynamic processes) monitoring;
  2. GIS and Remote sensing technologies such as optical, radar, and LiDAR for Coastal/beach Systems;
  3. New technology, numerical modeling, applications, etc. such as machine learning, AI.

Dr. Yann Balouin
Guest Editor

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 and beach geomorphology
  • coastal sediments process
  • coastal erosion
  • coastline evolution
  • GIS/remote sensing technologies
  • numerical modeling

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

Published Papers (2 papers)

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Research

28 pages, 75722 KiB  
Article
An Integrated Approach to Riverbed Morphodynamic Modeling Using Remote Sensing Data
by Matteo Bozzano, Francesco Varni, Monica De Martino, Alfonso Quarati, Nicoletta Tambroni and Bianca Federici
J. Mar. Sci. Eng. 2024, 12(11), 2055; https://doi.org/10.3390/jmse12112055 - 13 Nov 2024
Cited by 1 | Viewed by 1086
Abstract
River inlets, deltas, and estuaries represent delicate ecosystems highly susceptible to climate change impacts. While significant progress has been made in understanding the morphodynamics of these environments in recent decades, the development of models still requires thorough testing and data integration. In this [...] Read more.
River inlets, deltas, and estuaries represent delicate ecosystems highly susceptible to climate change impacts. While significant progress has been made in understanding the morphodynamics of these environments in recent decades, the development of models still requires thorough testing and data integration. In this context, remote sensing emerges as a potent tool, providing crucial data and the ability to monitor temporal changes. In this paper, an integrated approach combining remote sensing and morphodynamic modeling is proposed to assess river systems comprehensively. By utilizing multispectral or RGB optical imagery from satellites or UAVs for river classification and remotely derived bathymetry, echo sounder data for ground truth, and photogrammetric modeling of emerged areas, we outline a procedure to create an integrated and continuous digital terrain model (DTM) of a riverbed, paying particular attention to the wet–dry interface. This method enables us to identify the river centerline, its width, and its slope variations. Additionally, by applying a linear morphodynamic model that considers the spatial variability of river morphology commonly found in estuarine environments, it is possible to predict the wavelength and migration rate of sediment bars. This approach has been successfully applied to recreate the DTM and monitor the morphodynamics of the seaward reach of the Roya River (Italy). Full article
(This article belongs to the Special Issue Remote Sensing and GIS Applications for Coastal Morphodynamic Systems)
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17 pages, 5927 KiB  
Article
Accuracy of Bathymetric Depth Change Maps Using Multi-Temporal Images and Machine Learning
by Kim Lowell and Joan Hermann
J. Mar. Sci. Eng. 2024, 12(8), 1401; https://doi.org/10.3390/jmse12081401 - 15 Aug 2024
Cited by 4 | Viewed by 1124
Abstract
Most work to date on satellite-derived bathymetry (SDB) depth change estimates water depth at individual times t1 and t2 using two separate models and then differences the model estimates. An alternative approach is explored in this study: a multi-temporal Sentinel-2 image is created [...] Read more.
Most work to date on satellite-derived bathymetry (SDB) depth change estimates water depth at individual times t1 and t2 using two separate models and then differences the model estimates. An alternative approach is explored in this study: a multi-temporal Sentinel-2 image is created by “stacking” the bands of the times t1 and t2 images, geographically coincident reference data for times t1 and t2 allow for “true” depth change to be calculated for the pixels of the multi-temporal image, and this information is used to fit a single model that estimates depth change directly rather than indirectly as in the model-differencing approach. The multi-temporal image approach reduced the depth change RMSE by about 30%. The machine learning modelling method (categorical boosting) outperformed linear regression. Overfitting of models was limited even for the CatBoost models having the maximum number of variables examined. The visible Sentinel-2 spectral bands contributed most to the model predictions. Though the multi-temporal stacked image approach produced clearly superior depth change estimates compared to the conventional approach, it is limited only to those areas for which geographically coincident multi-temporal reference/“true” depth data exist. Full article
(This article belongs to the Special Issue Remote Sensing and GIS Applications for Coastal Morphodynamic Systems)
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