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Geosciences
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Progress in Seafloor Mapping
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Progress in Seafloor Mapping

A special issue of Geosciences (ISSN 2076-3263).

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 4811

Special Issue Editors

Dr. Mark Borrelli

E-Mail Website
Guest Editor
1. School for the Environment, University of Massachusetts, 100 Morrissey Blvd, Boston, MA 02125, USA
2. Coastal Processes and Ecosystems (CaPE) Laboratory, Center for Coastal Studies, 5 Holway Ave, Provincetown, MA 02657, USA
Interests: coastal sediment transport; tidal inlets; seafloor mapping
Dr. Arthur Trembanis

E-Mail Website
Guest Editor
College of Earth, Ocean and Environment, Newark, NJ, USA
Interests: autonomous underwater vehicles for seafloor mapping
Dr. Bryan A. Oakley

E-Mail Website
Guest Editor
Department of Environmental Earth Science, Eastern Connecticut State University, Willimantic, CT 06226, USA
Interests: coastal geology; marine geology/habitat mapping

Special Issue Information

Dear Colleagues,

Ocean and coastal seafloor mapping are critical to our understanding of the processes that shape these areas and help us better understand possible future landform evolution. The impacts of climate change include sea level rise, increasing storm intensities, and anthropogenic alteration. This Special Issue invites papers using sensors, techniques, and platforms for seafloor mapping, as well as those that use the latest seafloor mapping data.

Sensors, Techniques, and Platforms:

  • Acoustic: Side-scan imagery, bathymetry, backscatter, and seismic reflection profiling;
  • Optical: bathymetric lidar, UAS imagery, structure from motion, and satellite;
  • Modeling: wave, near-shore bathymetry, etc.;
  • Multi-modal studies;
  • Machine and deep learning and AI;
  • Crewed and uncrewed vessels, autonomous surface vessels and autonomous underwater vessels and gliders.

Studies with state-of-the-art seafloor mapping techniques concern:

  • Sediment transport;
  • Tidal inlets;
  • Barrier islands and spits;
  • Salt marsh and submerged aquatic vegetation;
  • Mangroves and rocky coasts;
  • Continental shelf and slope processes;
  • Benthic habitat studies;
  • Application of CMECS and other classification systems for seafloor mapping;
  • Anthropogenic alterations;
  • Siting of wind, wave, and tidal energy projects;
  • Marine debris;
  • Unexploded ordnance.

Dr. Mark Borrelli
Dr. Arthur Trembanis
Dr. Bryan A. Oakley
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. Geosciences 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 1800 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.

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

Published Papers (4 papers)

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Research

19 pages, 19539 KiB  
Article
Seabed Acoustic Mapping Revealing an Uncharted Habitat of Circular Depressions Along the Southeast Brazilian Outer Shelf
by Ana Carolina Lavagnino, Marcos Daniel Leite, Tarcila Franco, Pedro Smith Menandro, Fernanda Vedoato Vieira, Geandré Carlos Boni and Alex Cardoso Bastos
Geosciences 2025, 15(1), 7; https://doi.org/10.3390/geosciences15010007 - 1 Jan 2025
Viewed by 599
Abstract
Initiatives such as the United Nations Decade of Ocean Science for Sustainable Development and Seabed 2030 promote seabed mapping worldwide. In Brazil, especially on the Espírito Santo Continental Shelf, high-resolution seabed mapping has revealed an unknown complex seascape. Circular depressions (CDs) were mapped [...] Read more.
Initiatives such as the United Nations Decade of Ocean Science for Sustainable Development and Seabed 2030 promote seabed mapping worldwide. In Brazil, especially on the Espírito Santo Continental Shelf, high-resolution seabed mapping has revealed an unknown complex seascape. Circular depressions (CDs) were mapped for the first time in the Costa das Algas Marine Protection Area. Herein, we aim to present the CD metrics characteristics and discuss their relationship with morphology and relevance as a habitat based on multibeam bathymetry and ground truthing. A total of 3660 depressions were mapped between 46 and 85 m in depth, reaching an area of 460 m2 and 5 m relief. The continental shelf morphology was subdivided into three sectors based on morphology: inter-valleys, valley edges, and valley flanks, and eleven sites were selected for direct sampling/imaging at the CDs along the sectors. The direct sampling was carried out by scuba-diving with video images and sediment samples collected inside and outside the depressions. The deeper central parts of the circular depressions appear to function as a sink, presenting aggregations of rhodoliths or other carbonate fragments. In most inter-valley depressions, mounds were observed along the edges of the depression. We did not have any indication of gas seeps and no clear sedimentological or morphological control on their occurrence. We first hypothesize that their origin results from combined diachronous processes. The circular depressions mapped at high resolution could be related to sea level processes acting during the last glacial period and shelf exposure, i.e., relict features. The CDs are responsible for biomass aggregation and fish bioturbation, forming holes and rubble mounds, representing a modern process occurring on a centimetric scale. The data collected so far indicate that this fine-scale feature is an important habitat for different fish species. The modern maintenance of these structures could be due to low sedimentation regime areas shaped by biotic excavation. Full article
(This article belongs to the Special Issue Progress in Seafloor Mapping)
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18 pages, 4832 KiB  
Article
An Inter-Method Comparison of Drones, Side-Scan Sonar, Airplanes, and Satellites Used for Eelgrass (Zostera marina) Mapping and Management
by Jillian Carr and Todd Callaghan
Geosciences 2024, 14(12), 345; https://doi.org/10.3390/geosciences14120345 - 17 Dec 2024
Viewed by 703
Abstract
Remote sensing is heavily relied upon where eelgrass maps are needed for tracking trends, project siting and permitting, water quality assessments, and restoration planning. However, there is only a moderate degree of confidence in the accuracy of maps derived from remote sensing, thus [...] Read more.
Remote sensing is heavily relied upon where eelgrass maps are needed for tracking trends, project siting and permitting, water quality assessments, and restoration planning. However, there is only a moderate degree of confidence in the accuracy of maps derived from remote sensing, thus risking inadequate resource protection. In this study, semi-synchronous drone, side-scan sonar, airplane, and satellite missions were conducted at five Massachusetts eelgrass meadows to assess each method’s edge-detection capability and mapping accuracy. To ground-truth the remote sensing surveys, SCUBA divers surveyed the meadow along transects perpendicular to shore to locate the last shoot (i.e., meadow’s edge) and sampled quadrat locations along the transect for percent cover, canopy height, and meadow patchiness. In addition, drop frame underwater camera surveys were conducted to assess the accuracy of each remote sensing survey. Eelgrass meadow delineations derived from each remote sensing method were compared to ground-truthing data to address the following study objectives: (1) determine if and how much eelgrass was missed during manual photointerpretation of the imagery from each remote sensing method, (2) assess map accuracy, as well as the effects of eelgrass percent cover, canopy height, and meadow patchiness on method performance, and (3) make management recommendations regarding the use of remote sensing data for eelgrass mapping. Results showed that all remote sensing methods were associated with the underestimation of eelgrass. At the shallow edge, mean edge detection error was lowest for drone imagery (11.2 m) and increased with decreasing image resolution, up to 38.5 m for satellite imagery. At the deep edge, mean edge detection error varied by survey method but ranged from 72 to 106 m. Maximum edge detection errors across all sites and depths for each survey method were 112.4 m, 121.4 m, 121.7 m, and 106.7 m for drone, sonar, airplane, and satellite data, respectively. The overall accuracy of eelgrass delineations across the survey methods ranged from 76–89% and corresponded with image resolution, where drones performed best, followed by sonar, airplanes, and satellites; however, there was a high degree of site variability. Accuracy at the shallow edge was greater than at the deep edge across all survey types except for satellite, where accuracy was the same at both depths. Accuracy was influenced by eelgrass percent cover, canopy height, and meadow patchiness. Low eelgrass density (i.e., 1–10% cover), patchy eelgrass (i.e., shoots or patches spaced > 5 m) and shorter canopy height (i.e., <22 cm) were associated with reduced accuracy across all methods; however, drones performed best across all scenarios. Management recommendations include applying regulatory buffers to eelgrass maps derived from remote sensing in order to protect meadow edge areas from human disturbances, the prioritization of using SCUBA and high-resolution platforms like drones and sonar for eelgrass mapping, and for existing mapping programs to allocate more resources to ground-truthing along meadow edges. Full article
(This article belongs to the Special Issue Progress in Seafloor Mapping)
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34 pages, 40857 KiB  
Article
Application of the Coastal and Marine Ecological Classification Standard (CMECS) to Create Benthic Geologic Habitat Maps for Portions of Acadia National Park, Maine, USA
by Bryan Oakley, Brian Caccioppoli, Monique LaFrance Bartley, Catherine Johnson, Alexandra Moen, Cameron Soulagnet, Genevieve Rondeau, Connor Rego and John King
Geosciences 2024, 14(10), 256; https://doi.org/10.3390/geosciences14100256 - 28 Sep 2024
Viewed by 1204
Abstract
The Coastal and Marine Ecological Classification Standard (CMECS) was applied to four portions of Acadia National Park, USA, focusing on intertidal rocky and tidal flat habitats. Side-scan sonar coupled with multi-phase echo sounder bathymetry are the primary data sources used to map the [...] Read more.
The Coastal and Marine Ecological Classification Standard (CMECS) was applied to four portions of Acadia National Park, USA, focusing on intertidal rocky and tidal flat habitats. Side-scan sonar coupled with multi-phase echo sounder bathymetry are the primary data sources used to map the seafloor, coupled with underwater video imagery and surface grab samples for grain size and macrofaunal analysis. The CMECS Substrate, Geoform, and Biotic components were effective in describing the study areas. However, integrating the CMECS components to define Biotopes was more challenging due to the limited number of grab samples available and because the dominant species within a given map unit is largely inconsistent. While Biotopes ultimately could not be defined in this study, working within the CMECS framework to create statistically significant biotopes revealed the complexity of these study areas that may otherwise have been overlooked. This study demonstrates the effectiveness of the CMECS classification, including the framework’s ability to be flexible in communicating information. Full article
(This article belongs to the Special Issue Progress in Seafloor Mapping)
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21 pages, 10962 KiB  
Article
Mapping Seafloor Sediment Distributions Using Public Geospatial Data and Machine Learning to Support Regional Offshore Renewable Energy Development
by Connor W. Capizzano, Alexandria C. Rhoads, Jennifer A. Croteau, Benjamin G. Taylor, Marisa L. Guarinello and Emily J. Shumchenia
Geosciences 2024, 14(7), 186; https://doi.org/10.3390/geosciences14070186 - 11 Jul 2024
Viewed by 1206
Abstract
Given the rapid expansion of offshore wind development in the United States (US), the accurate mapping of benthic habitats, specifically surficial sediments, is essential for mitigating potential impacts on these valuable ecosystems. However, offshore wind development has outpaced results from environmental monitoring efforts, [...] Read more.
Given the rapid expansion of offshore wind development in the United States (US), the accurate mapping of benthic habitats, specifically surficial sediments, is essential for mitigating potential impacts on these valuable ecosystems. However, offshore wind development has outpaced results from environmental monitoring efforts, compelling stakeholders to rely on a limited set of public geospatial data for conducting impact assessments. The present study therefore sought to develop and evaluate a systematic workflow for generating regional-scale sediment maps using public geospatial data that may pose integration and modeling challenges. To demonstrate this approach, sediment distributions were characterized on the northeastern US continental shelf where offshore wind development has occurred since 2016. Publicly available sediment and bathymetric data in the region were processed using national classification standards and spatial tools, respectively, and integrated using a machine learning algorithm to predict sediment occurrence. Overall, this approach and the generated sediment composite effectively predicted sediment distributions in coastal areas but underperformed in offshore areas where data were either scarce or of poor quality. Despite these shortcomings, this study builds on benthic habitat mapping efforts and highlights the need for regional collaboration to standardize seafloor data collection and sharing activities for supporting offshore wind energy decisions. Full article
(This article belongs to the Special Issue Progress in Seafloor Mapping)
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