Remote Sensing Applications in Marine Environmental Monitoring

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: 30 September 2024 | Viewed by 1241

Special Issue Editors


E-Mail Website
Guest Editor
Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy (CNR), Neaples, Italy
Interests: SAR; SAR processing; sea-surface parameters; sea-surface radial velocity; doppler centroid anomaly
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute for the Electromagnetic Sensing of the Environment (IREA), National Research Council (CNR), Naples, Italy
Interests: remote sensing; synthetic aperture radar; SAR tomography; SAR interferometry
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute for the Electromagnetic Sensing of the Environment (IREA), National Research Council (CNR), Naples, Italy
Interests: remote sensing; machine learning; SAR Interferometry; change detection

Special Issue Information

Dear Colleagues,

We are pleased to invite you to the Special Issue on “Remote Sensing Applications in Marine Environmental Monitoring”.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Detection and biogeophysical property determination of marine litters and floating materials (such as sea ice, algal blooms, and spilled oil), through the use of remote sensing methods;
  • PolSAR and InSAR methods for maritime surveillance, ocean waves, and sea state measurement;
  • Remote sensing of the ocean water color;
  • Maritime surveillance case studies such as oil-spill monitoring, navigation in sea-ice-infested waters, ship detection, and ship traffic;
  • Mapping of the marine environment, including high-resolution wind fields, coastal wave fields, shoreline changes, upwelling phenomena, roll vortices, currents, fronts, gravity waves, internal waves, rain cells, salinity, and shallow-water bathymetry;
  • Innovative SAR concepts for optimal sensing of the marine environment;
  • Remote sensing concepts and advanced sensors for the marine/ocean environment.

We look forward to receiving your contributions.  

Dr. Virginia Zamparelli
Dr. Simona Verde
Dr. Pietro Mastro
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

  • ocean winds, wave, currents, bathimetry
  • ecological applications: water quality, oil spill, algal blooms, etc.
  • radar
  • remote sensing
  • ocean and coastal monitoring
  • coastal areas safety and protection
  • sea ice
  • syntetic aperture radar
  • optical data

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

29 pages, 27799 KiB  
Article
Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data
by Polina Lemenkova
J. Mar. Sci. Eng. 2024, 12(5), 709; https://doi.org/10.3390/jmse12050709 - 25 Apr 2024
Viewed by 775
Abstract
This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in [...] Read more.
This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Marine Environmental Monitoring)
Show Figures

Figure 1

Back to TopTop