Progress in Seafloor Mapping

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

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 2836

Special Issue Editors


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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

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Guest Editor
College of Earth, Ocean and Environment, Newark, NJ, USA
Interests: autonomous underwater vehicles for seafloor mapping

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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

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Published Papers (2 papers)

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Research

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 885
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 983
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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