Special Issue "Geological Seafloor Mapping"
Deadline for manuscript submissions: 31 March 2019
The ocean floor is vast, yet largely uncharted. Although an ambitious pledge was made to map the entire ocean floor by the year 2030, this only pertains to the bathymetry of the oceans. Mapping the geological makeup of the seafloor remains one of the great challenges in marine geoscience. Recent advances in data acquisition, processing, analysis and dissemination should, however, put us in a better position to deliver accurate and detailed maps of seafloor sediment and substratum types.
A significant part of the analysis rests on the acoustic backscatter intensity of the seafloor gathered with sidescan sonars and, more recently, multibeam echosounders (MBES). We have witnessed significant advances in this field of technology in recent years, including global efforts to standardise the collection and processing of calibrated backscatter data and the introduction of multispectral MBES for seafloor mapping. Such advances will ultimately lead to better maps of the geology of the seafloor and the distribution of benthic habitats.
Progress has also been made by introducing methods of image analysis, spatial prediction and machine learning, widely utilised in terrestrial mapping applications, to geological seafloor mapping. These methods have several advantages over traditional mapping ‘by eye’, including repeatability, time-savings, cost-effectiveness and the provision of estimates of accuracy. More recently, attempts have been made in spatially predicting quantitative sediment properties (e.g., grain-size composition) rather than sediment classes. Such studies can also shed light on the relationships between sediment properties and the marine environmental drivers that determine the distribution of sediments on the seafloor.
It is generally acknowledged that due to the high costs involved in collecting marine datasets we should ‘collect once, use many times’. Efficient systems for data search and retrieval make it now much easier to search for relevant datasets and download them from databases.
The aim of this Special Issue of Geosciences is to showcase the latest developments in the field of geological seafloor mapping. We specifically invite contributions addressing the following aspects:
- Studies assessing the potential of multispectral MBES for geological seafloor mapping
- Systematic and quantitative comparisons of mapping approaches
- The impact of spatial scale on mapping performance
- The assessment and communication of mapping uncertainty and confidence
- Quantification of the relationships between sediments and environmental drivers
- Quantification of the relationships between sediments, benthic organisms, and backscatter
- Case studies from local to global scales making innovative use of legacy data from data repositories
Dr. Markus Diesing
Dr. Peter Feldens
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 papers will be 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 850 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.
- Marine geology
- Seafloor mapping
- Benthic habitats
- Multibeam echosounder
- Acoustic backscatter
- Spatial prediction
- Image analysis
- Machine learning
- Spatial scale
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Evaluation of Multispectral Multibeam Backscatter for Seafloor Surficial Geology and Benthic Habitat Mapping
Craig J. Brown
NSERC Industrial Research Chair: Integrated Ocean Mapping Technologies
Seafloor Classification Using Machine Learning: A Review and Perspective
Daniel David Buscombe
Northern Arizona University, Flagstaff, USA
Automating the Physical Characterisation of Reef Habitat Using Terrain Variables
Eimear O’Keeffe and Oliver Tully
Marine Institute, Galway, Ireland
Acoustically Noisy Substrates in Space and Time: Insights on the Half-Diel Variability of MBES Seafloor Backscatter from Field Measurements in the Belgian Continental Shelf
Giacomo Montereale-Gavazzi 1, 2, Marc Roche 3, Nathan Terseleer 1, Frederic Francken 1, Matthias Baeye 1, Vera Van Lancker 1, 2
1 Royal Belgian Institute of Natural Sciences, Operational Directorate of Nature, Gulledelle 100, B, 1200 Brussels, Belgium
2 Renard Centre of Marine Geology Department of Geology and Soil Science, Geological Institute, Ghent University Krijgslaan 281 s.8, B-9000 Gent, Belgium
3 Federal Public Service Economies, Continental Shelf Service, Boulevard du Roi Albert II, 16, 1000 Brussels, Belgium
Effects of Grain Size Data Aggregation on Multiscale Seabed Sediment Distribution Models
Benjamin Misiuk1, Markus Diesing2, Evan Edinger1, Alec Aitken3, Trevor Bell1
1 Department of Geography, Memorial University of Newfoundland, St.John's, Newfoundland, Canada
2 Marine Geology, Geological Survey of Norway, Trondheim, Norway
3 Department of Geography and Planning, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Legacy Data: How Decades of Seabed Sampling can Produce Robust Predictions and Versatile Products
Peter J Mitchell1, John Aldridge1 and Markus Diesing2
1 Centre for Environment, Fisheries and Aquaculture Science (Cefas), Pakefield Road, Lowestoft, NR33 0HT, UK
2Geological Survey of Norway (NGU), Postal Box 6315 Torgarden, 7491 Trondheim, Norway
New Seafloor Sediment Mappings for the Gulf of Mexico, with Spatial Heterogeneity Statistics
Chris J Jenkins
INSTAAR, University of Colorado at Boulder, USA
A Multispectral Bayesian Method for Improved Discrimination Performance of Seabed Sediment Classification Using Multi-Frequency Multibeam Backscatter Data
T. C. Gaida 1, T. A. Tengku Ali 1,3, M. Snellen 1,2, D. G. Simons 1
1 Acoustics Group, Faculty of Aerospace Engineering, Delft University of Technology
2 Department of Applied Geology and Geophysics, Deltares
3 Department of Survey Science and Geomatics, Universiti Teknologi MARA, Perlis, Malaysia
Seabed Feature Classification Along a Subtropical-Temperate Continental Shelf, Southeast Australia
Michelle Linklater1, Tim Ingleton1, Michael Kinsela1, Brad Morris1, Katie Allen1, Michael Sutherland1, and David Hanslow1
1 Coastal and Marine Unit, New South Wales Office of Environment and Heritage, 59-61 Goulburn Street, Sydney, Australia 2001
The Hyper-Angular Cube Concept for Improving the Spatial and Acoustic Resolution of MBES Backscatter Angular Response Analysis
Evangelos Alevizos, Jens Greinert
GEOMAR Helmholtz Center for Ocean Research, 24148 Kiel, Germany
Evaluation of Automated Seafloor Classification Algorithms for Cold-Water Coral Habitats
Veerle Huvenne et al
National Oceanography Centre, Southampton, UK
Developing an optimal spatial predictive model for seabed sand content using machine learning, geostatistics and their hybrid methods based on acoustic multibeam data and their derived predictive variables
Jin Li1*, Justy Siwabessy1, Zhi Huang1 and Scott Nichol1
1Geoscience Australia, GPO Box 378, Canberra, ACT 2601, Australia