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Geomatics
Special Issues
Advances in Ocean Mapping and Hydrospatial Applications
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Advances in Ocean Mapping and Hydrospatial Applications

A special issue of Geomatics (ISSN 2673-7418).

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 1832

Special Issue Editors

Dr. Anand Hiroji

E-Mail Website
Guest Editor
The School of Ocean Science and Engineering, The University of Southern Mississippi, Stennis Space Center, MS 39529, USA
Interests: ocean mapping; multibeam sonar
Special Issues, Collections and Topics in MDPI journals
Dr. Ian Church

E-Mail Website
Guest Editor
Department of Geodesy and Geomatics, University of New Brunswick, Fredericton, NB E3B5A3, Canada
Interests: ocean mapping; data processing and analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Masetti

E-Mail Website
Guest Editor
Center for Coastal and Ocean Mapping, University of New Hampshire, Durham, NH 03824, USA
Interests: automated data processing; ocean mapping; nautical cartography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of ocean mapping and hydrospatial applications has seen rapid advancements driven by cutting-edge technologies, the adoption of the IHO S-100 framework, and the growing need for comprehensive marine management. As human activities expand in coastal and deep-sea regions, accurate and comprehensive ocean mapping has become essential across diverse applications, including environmental protection, resource management, infrastructure development, and scientific exploration. These advancements are vital to sustainable ocean management, where data accuracy and accessibility are paramount.

Integrating hydrospatial data with geospatial applications provides a transformative approach to addressing complex challenges in marine science, policy, and engineering. By organizing vast, multidimensional marine datasets into spatially intelligent systems, hydrospatial methods enhance decision-making in areas such as marine spatial planning, ecosystem monitoring, and climate resilience. The S-100 framework, with its standardized model for marine data exchange, facilitates interoperability and precision, enabling the seamless integration of diverse datasets. This shift towards a hydrospatial perspective broadens traditional hydrography, aligning ocean mapping with the demand for real-time, data-informed insights into marine and coastal environments.

This Special Issue on ‘Advances in Ocean Mapping and Hydrospatial Applications’ aims to capture these innovations and provide a platform for sharing the latest research, tools, and applications driving the future of ocean mapping. We invite original research, case studies, technical notes, and reviews on topics including, but not limited to:

  • Innovative techniques for seafloor mapping and underwater feature detection.
  • Integration of remote sensing, autonomous vehicles, and multi-sensor data in hydrography.
  • Advances in multibeam, single beam, and side-scan sonar applications.
  • Data processing algorithms for improving spatial resolution and accuracy.
  • Hydrospatial analysis in marine spatial planning and resource management.
  • Modeling and visualization of bathymetric, backscatter, and water column data.
  • Standards and best practices in hydrographic surveying.
  • Applications of machine learning and AI in hydrography.
  • Real-time data acquisition for hydrographic and oceanographic purposes.
  • Applications of the S-100 framework, including its role in e-navigation, data harmonization, and interoperability across marine systems and stakeholders.
  • Case studies on hydrospatial solutions in policy and marine management.

Through this Special Issue, we aim to showcase contributions that advance the field of ocean mapping and hydrospatial applications, supporting sustainable marine management and enhanced scientific and environmental insights.

We look forward to receiving your original research articles and reviews.

Dr. Anand Hiroji
Dr. Ian Church
Dr. Giuseppe Masetti
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. Geomatics is an international peer-reviewed open access quarterly 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 1000 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

  • S-100 Standards
  • ocean mapping
  • hydrospatial applications
  • marine data integration
  • autonomous vehicles
  • multi-sensor data fusion
  • multibeam sonar
  • data processing algorithms
  • marine spatial planning
  • artificial intelligence and machine learning in hydrography
  • acoustic backscatter data
  • sustainable ocean management

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

Related Special Issue

Published Papers (2 papers)

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Research

24 pages, 10881 KB  
Article
Dynamics of Water Quality in the Mirim–Patos–Mangueira Coastal Lagoon System with Sentinel-3 OLCI Data
by Paula Andrea Contreras Rojas, Felipe de Lucia Lobo, Wesley J. Moses, Gilberto Loguercio Collares and Lino Sander de Carvalho
Geomatics 2025, 5(3), 36; https://doi.org/10.3390/geomatics5030036 - 25 Jul 2025
Viewed by 559
Abstract
The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the [...] Read more.
The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the spatial and temporal patterns of water quality in the lagoon system using Sentinel-3/OLCI satellite imagery. Atmospheric correction was performed using ACOLITE, followed by spectral grouping and classification into optical water types (OWTs) using the Sentinel Applications Platform (SNAP). To explore the behavior of water quality parameters across OWTs, Chlorophyll-a and turbidity were estimated using semi-empirical algorithms specifically designed for complex inland and coastal waters. Results showed a gradual increase in mean turbidity from OWT 2 to OWT 6 and a rise in chlorophyll-a from OWT 2 to OWT 4, with a decline at OWT 6. These OWTs correspond, in general terms, to distinct water masses: OWT 2 to clearer waters, OWT 3 and 4 to intermediate/mixed conditions, and OWT 6 to turbid environments. In the second part, we analyzed the response of the Patos Lagoon to flooding in Rio Grande do Sul during an extreme weather event in May 2024. Satellite-derived turbidity estimates were compared with in situ measurements, revealing a systematic underestimation, with a negative bias of 2.6%, a mean relative error of 78%, and a correlation coefficient of 0.85. The findings highlight the utility of OWT classification for tracking changes in water quality and support the use of remote sensing tools to improve environmental monitoring in data-scarce regions, particularly under extreme hydrometeorological conditions. Full article
(This article belongs to the Special Issue Advances in Ocean Mapping and Hydrospatial Applications)
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23 pages, 4594 KB  
Article
Ensemble Machine Learning Approaches for Bathymetry Estimation in Multi-Spectral Images
by Kazi Aminul Islam, Omar Abul-Hassan, Hongfang Zhang, Victoria Hill, Blake Schaeffer, Richard Zimmerman and Jiang Li
Geomatics 2025, 5(3), 34; https://doi.org/10.3390/geomatics5030034 - 22 Jul 2025
Cited by 1 | Viewed by 418
Abstract
Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named [...] Read more.
Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs for bathymetry estimation, including raw reflectance, log-linear, and log-ratio transforms of the raw reflectance value in WV-2 images. In addition, we investigated the contribution of each spectral band and found that utilizing all eight spectral bands in WV-2 images offers the best solution for handling complex water quality conditions. We compared CatBoostOpt with linear regression (LR), support vector machine (SVM), random forest (RF), AdaBoost, gradient boosting, and deep convolutional neural network (DCNN). CatBoostOpt with log-ratio transformed reflectance achieved the best performance with an average root mean square error (RMSE) of 0.34 and coefficient of determination (R2) of 0.87. Full article
(This article belongs to the Special Issue Advances in Ocean Mapping and Hydrospatial Applications)
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