Special Issue "Geological Seafloor Mapping"

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Geophysics".

Deadline for manuscript submissions: 31 March 2019

Special Issue Editors

Guest Editor
Dr. Markus Diesing

Geological Survey of Norway
Website | E-Mail
Interests: marine geology; habitat mapping; sediment dynamics; organic carbon; spatial prediction; object-based image analysis; machine learning
Guest Editor
Dr. Peter Feldens

Leibniz Institute for Baltic Sea Research Warnemünde
Website | E-Mail
Interests: marine geology; hydroacoustic methods; geomorphology; geology of the Baltic Sea; habitat mapping

Special Issue Information

Dear Colleagues,

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
Guest Editors

Manuscript Submission Information

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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.

Keywords

  • Marine geology
  • Seafloor mapping
  • Sediment
  • Benthic habitats
  • Multibeam echosounder
  • Acoustic backscatter
  • Spatial prediction
  • Image analysis
  • Machine learning
  • Accuracy
  • Confidence
  • Spatial scale

Published Papers (7 papers)

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Research

Open AccessArticle Insights into the Short-Term Tidal Variability of Multibeam Backscatter from Field Experiments on Different Seafloor Types
Geosciences 2019, 9(1), 34; https://doi.org/10.3390/geosciences9010034
Received: 4 December 2018 / Revised: 24 December 2018 / Accepted: 2 January 2019 / Published: 10 January 2019
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Abstract
Three experiments were conducted in the Belgian part of the North Sea to investigate short-term variation in seafloor backscatter strength (BS) obtained with multibeam echosounders (MBES). Measurements were acquired on predominantly gravelly (offshore) and sandy and muddy (nearshore) areas. Kongsberg EM3002 and EM2040
[...] Read more.
Three experiments were conducted in the Belgian part of the North Sea to investigate short-term variation in seafloor backscatter strength (BS) obtained with multibeam echosounders (MBES). Measurements were acquired on predominantly gravelly (offshore) and sandy and muddy (nearshore) areas. Kongsberg EM3002 and EM2040 dual MBES were used to carry out repeated 300-kHz backscatter measurements over tidal cycles (~13 h). Measurements were analysed in complement to an array of ground-truth variables on sediment and current nature and dynamics. Seafloor and water-column sampling was used, as well as benthic landers equipped with different oceanographic sensors. Both angular response (AR) and mosaicked BS were derived. Results point at the high stability of the seafloor BS in the gravelly area (<0.5 dB variability at 45° incidence) and significant variability in the sandy and muddy areas with envelopes of variability >2 dB and 4 dB at 45° respectively. The high-frequency backscatter sensitivity and short-term variability are interpreted and discussed in the light of the available ground-truth data for the three experiments. The envelopes of variability differed considerably between areas and were driven either by external sources (not related to the seafloor sediment), or by intrinsic seafloor properties (typically for dynamic nearshore areas) or by a combination of both. More specifically, within the gravelly areas with a clear water mass, seafloor BS measurements where unambiguous and related directly to the water-sediment interface. Within the sandy nearshore area, the BS was shown to be strongly affected by roughness polarization processes, particularly due to along- and cross-shore current dynamics, which were responsible for the geometric reorganization of the morpho-sedimentary features. In the muddy nearshore area, the BS fluctuation was jointly driven by high-concentrated mud suspension dynamics, together with surficial substrate changes, as well as by water turbidity, increasing the transmission losses. Altogether, this shows that end-users and surveyors need to consider the complexity of the environment since its dynamics may have severe repercussions on the interpretation of BS maps and change-detection applications. Furthermore, the experimental observations revealed the sensitivity of high-frequency BS values to an array of specific configurations of the natural water-sediment interface which are of interest for monitoring applications elsewhere. This encourages the routine acquisition of different and concurrent environmental data together with MBES survey data. In view of promising advances in MBES absolute calibration allowing more straightforward data comparison, further investigations of the drivers of BS variability and sensitivity are required. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle A Multispectral Bayesian Classification Method for Increased Acoustic Discrimination of Seabed Sediments Using Multi-Frequency Multibeam Backscatter Data
Geosciences 2018, 8(12), 455; https://doi.org/10.3390/geosciences8120455
Received: 7 November 2018 / Revised: 27 November 2018 / Accepted: 29 November 2018 / Published: 4 December 2018
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Abstract
Multi-frequency backscatter data collected from multibeam echosounders (MBESs) is increasingly becoming available. The ability to collect data at multiple frequencies at the same time is expected to allow for better discrimination between seabed sediments. We propose an extension of the Bayesian method for
[...] Read more.
Multi-frequency backscatter data collected from multibeam echosounders (MBESs) is increasingly becoming available. The ability to collect data at multiple frequencies at the same time is expected to allow for better discrimination between seabed sediments. We propose an extension of the Bayesian method for seabed classification to multi-frequency backscatter. By combining the information retrieved at single frequencies we produce a multispectral acoustic classification map, which allows us to distinguish more seabed environments. In this study we use three triple-frequency (100, 200, and 400 kHz) backscatter datasets acquired with an R2Sonic 2026 in the Bedford Basin, Canada in 2016 and 2017 and in the Patricia Bay, Canada in 2016. The results are threefold: (1) combining 100 and 400 kHz, in general, reveals the most additional information about the seabed; (2) the use of multiple frequencies allows for a better acoustic discrimination of seabed sediments than single-frequency data; and (3) the optimal frequency selection for acoustic sediment classification depends on the local seabed. However, a quantification of the benefit using multiple frequencies cannot clearly be determined based on the existing ground-truth data. Still, a qualitative comparison and a geological interpretation indicate an improved discrimination between different seabed environments using multi-frequency backscatter. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle The Hyper-Angular Cube Concept for Improving the Spatial and Acoustic Resolution of MBES Backscatter Angular Response Analysis
Geosciences 2018, 8(12), 446; https://doi.org/10.3390/geosciences8120446
Received: 16 October 2018 / Revised: 24 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
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Abstract
This study presents a novel approach, based on high-dimensionality hydro-acoustic data, for improving the performance of angular response analysis (ARA) on multibeam backscatter data in terms of acoustic class separation and spatial resolution. This approach is based on the hyper-angular cube (HAC) data
[...] Read more.
This study presents a novel approach, based on high-dimensionality hydro-acoustic data, for improving the performance of angular response analysis (ARA) on multibeam backscatter data in terms of acoustic class separation and spatial resolution. This approach is based on the hyper-angular cube (HAC) data structure which offers the possibility to extract one angular response from each cell of the cube. The HAC consists of a finite number of backscatter layers, each representing backscatter values corresponding to single-incidence angle ensonifications. The construction of the HAC layers can be achieved either by interpolating dense soundings from highly overlapping multibeam echo-sounder (MBES) surveys (interpolated HAC, iHAC) or by producing several backscatter mosaics, each being normalized at a different incidence angle (synthetic HAC, sHAC). The latter approach can be applied to multibeam data with standard overlap, thus minimizing the cost for data acquisition. The sHAC is as efficient as the iHAC produced by actual soundings, providing distinct angular responses for each seafloor type. The HAC data structure increases acoustic class separability between different acoustic features. Moreover, the results of angular response analysis are applied on a fine spatial scale (cell dimensions) offering more detailed acoustic maps of the seafloor. Considering that angular information is expressed through high-dimensional backscatter layers, we further applied three machine learning algorithms (random forest, support vector machine, and artificial neural network) and one pattern recognition method (sum of absolute differences) for supervised classification of the HAC, using a limited amount of ground truth data (one sample per seafloor type). Results from supervised classification were compared with results from an unsupervised method for inter-comparison of the supervised algorithms. It was found that all algorithms (regarding both the iHAC and the sHAC) produced very similar results with good agreement (>0.5 kappa) with the unsupervised classification. Only the artificial neural network required the total amount of ground truth data for producing comparable results with the remaining algorithms. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle Probabilistic Substrate Classification with Multispectral Acoustic Backscatter: A Comparison of Discriminative and Generative Models
Geosciences 2018, 8(11), 395; https://doi.org/10.3390/geosciences8110395
Received: 5 October 2018 / Revised: 22 October 2018 / Accepted: 25 October 2018 / Published: 30 October 2018
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Abstract
We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully-connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay,
[...] Read more.
We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully-connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of allowable proximity and backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model (GMM) that doesn’t include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF therefore may prove to be a powerful ‘spatially aware’ alternative to other discriminative classifiers. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle Where, What, When, and Why Is Bottom Mapping Needed? An On-Line Application to Set Priorities Using Expert Opinion
Geosciences 2018, 8(10), 379; https://doi.org/10.3390/geosciences8100379
Received: 18 September 2018 / Revised: 9 October 2018 / Accepted: 11 October 2018 / Published: 16 October 2018
PDF Full-text (4028 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Globally, there is a lack of resources to survey the vast seafloor areas in need of basic mapping data. Consequently, smaller areas must be prioritized to address the most urgent needs. We developed a systematic, quantitative approach and on-line application to gather mapping
[...] Read more.
Globally, there is a lack of resources to survey the vast seafloor areas in need of basic mapping data. Consequently, smaller areas must be prioritized to address the most urgent needs. We developed a systematic, quantitative approach and on-line application to gather mapping suggestions from diverse stakeholders. Participants are each provided with 100 virtual coins to place throughout a region of interest to convey their mapping priorities. Inputs are standardized into a spatial framework using a grid and pull-down menus. These enabled participants to convey the types of mapping products that they need, the rationale used to justify their needs, and the locations that they prioritize for mapping. This system was implemented in a proposed National Marine Sanctuary encompassing 2784 km2 of Lake Michigan, Wisconsin. We demonstrate key analyses of the outputs, including coin counts, cell ranking, and multivariate cluster analysis for isolating high priority topics and locations. These techniques partition the priorities among the disciplines of the respondents, their selected justifications, and types of desired map products. The results enable respondents to identify potential collaborations to achieve common goals and more effectively invest limited mapping funds. The approach can be scaled to accommodate larger geographic areas and numbers of participants and is not limited to seafloor mapping. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle Detection of Stones in Marine Habitats Combining Simultaneous Hydroacoustic Surveys
Geosciences 2018, 8(8), 279; https://doi.org/10.3390/geosciences8080279
Received: 18 June 2018 / Revised: 24 July 2018 / Accepted: 26 July 2018 / Published: 28 July 2018
Cited by 1 | PDF Full-text (4361 KB) | HTML Full-text | XML Full-text
Abstract
Exposed stones in sandy sublittoral environments are hotspots for marine biodiversity, especially for benthic communities. The detection of single stones is principally possible using sidescan-sonar (SSS) backscatter data. The data resolution has to be high to visualize the acoustic shadows of the stones.
[...] Read more.
Exposed stones in sandy sublittoral environments are hotspots for marine biodiversity, especially for benthic communities. The detection of single stones is principally possible using sidescan-sonar (SSS) backscatter data. The data resolution has to be high to visualize the acoustic shadows of the stones. Otherwise, stony substrates will not be differentiable from other high backscatter substrates (e.g., gravel). Acquiring adequate sonar data and identifying stones in backscatter images is time consuming because it usually requires visual-manual procedures. To develop a more efficient identification and demarcation procedure of stone fields, sidescan sonar and parametric echo sound data were recorded within the marine protected area of “Sylt Outer Reef” (German Bight, North Sea). The investigated area (~5.900 km2) is characterized by dispersed heterogeneous moraine and marine deposits. Data from parametric sediment echo sounder indicate hyperbolas at the sediment surface in stony areas, which can easily be exported. By combining simultaneous recorded low backscatter data and parametric single beam data, stony grounds were demarcated faster, less complex and reproducible from gravelly substrates indicating similar high backscatter in the SSS data. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle Improved Interpretation of Marine Sedimentary Environments Using Multi-Frequency Multibeam Backscatter Data
Geosciences 2018, 8(6), 214; https://doi.org/10.3390/geosciences8060214
Received: 8 May 2018 / Revised: 8 June 2018 / Accepted: 9 June 2018 / Published: 12 June 2018
Cited by 1 | PDF Full-text (4283 KB) | HTML Full-text | XML Full-text
Abstract
Backscatter mosaics based on a multi-frequency multibeam echosounder survey in the continental shelf setting of the North Sea were compared. The uncalibrated backscatter data were recorded with frequencies of 200, 400 and 600 kHz. The results showed that the seafloor appears mostly featureless
[...] Read more.
Backscatter mosaics based on a multi-frequency multibeam echosounder survey in the continental shelf setting of the North Sea were compared. The uncalibrated backscatter data were recorded with frequencies of 200, 400 and 600 kHz. The results showed that the seafloor appears mostly featureless in acoustic backscatter mosaics derived from 600 kHz data. The same area surveyed with 200 kHz reveals numerous backscatter anomalies with diameters of 10–70 m deviating between −2 dB and +4 dB from the background sediment. Backscatter anomalies were further subdivided based on their frequency-specific texture and were attributed to bioturbation within the sediment and the presence of polychaetes on the seafloor. While low frequencies show the highest overall contrast between different seafloor types, a consideration of all frequencies permits an improved interpretation of subtle seafloor features. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Planned Papers

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
1
Geoscience Australia, GPO Box 378, Canberra, ACT 2601, Australia

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