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Spatial and Spatiotemporal Methods in Marine Science
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Spatial and Spatiotemporal Methods in Marine Science

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Geological Oceanography".

Deadline for manuscript submissions: closed (20 March 2022) | Viewed by 4383

Special Issue Editor

Dr. Emmanouil Varouchakis

E-Mail Website
Guest Editor
School of Mineral Resources Engineering, Technical University of Crete, 73100 Crete, Greece
Interests: space–time geostatistics; geosciences; stochastic methods; water resources; groundwater
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main goal of spatial statistics has been the development of statistical dependence models that allow optimal prediction and simulation of spatial processes. Spatiotemporal analysis allows identifying and explaining patterns and anomalies which are useful to quantify the dynamic distribution of physical variables and for understanding environmental processes. In recent years there has been an explosion of spatial and spatiotemporal data fueled by technological advances which include remote-sensing capabilities and onshore–offshore sensor networks. This development has motivated new research efforts directed at building novel space–time models for analyzing the emerging datasets.

Dr. Emmanouil Varouchakis
Guest Editor

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. Journal of Marine Science and Engineering 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 2600 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

  • Spatiotemporal methods
  • Geostatistics
  • Machine learning
  • Stochastic models
  • Wave data analysis
  • Coastal management
  • Coastal reservoirs (aquifers, hydrocarbons)
  • Risk assessment
  • Remote sensing and satellite data
  • Oil spill monitoring

Published Papers (2 papers)

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Research

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28 pages, 18776 KiB  
Article
Prediction of Changes in Seafloor Depths Based on Time Series of Bathymetry Observations: Dutch North Sea Case
by Reenu Toodesh, Sandra Verhagen and Anastasia Dagla
J. Mar. Sci. Eng. 2021, 9(9), 931; https://doi.org/10.3390/jmse9090931 - 27 Aug 2021
Cited by 3 | Viewed by 1882
Abstract
Guaranteeing safety of navigation within the Netherlands Continental Shelf (NCS), while efficiently using its ocean mapping resources, is a key task of Netherlands Hydrographic Service (NLHS) and Rijkswaterstaat (RWS). Resurvey frequencies depend on seafloor dynamics and the aim of this research is to [...] Read more.
Guaranteeing safety of navigation within the Netherlands Continental Shelf (NCS), while efficiently using its ocean mapping resources, is a key task of Netherlands Hydrographic Service (NLHS) and Rijkswaterstaat (RWS). Resurvey frequencies depend on seafloor dynamics and the aim of this research is to model the seafloor dynamics to predict changes in seafloor depth that would require resurveying. Characterisation of the seafloor dynamics is based on available time series of bathymetry data obtained from the acoustic remote sensing method of both single-beam echosounding (SBES) and multibeam echosounding (MBES). This time series is used to define a library of mathematical models describing the seafloor dynamics in relation to spatial and temporal changes in depth. An adaptive, functional model selection procedure is developed using a nodal analysis (0D) approach, based on statistical hypothesis testing using a combination of the Overall Model Test (OMT) statistic and Generalised Likelihood Ratio Test (GLRT). This approach ensures that each model has an equal chance of being selected, when more than one hypothesis is plausible for areas that exhibit varying seafloor dynamics. This ensures a more flexible and rigorous decision on the choice of the nominal model assumption. The addition of piecewise linear models to the library offers another characterisation of the trends in the nodal time series. This has led to an optimised model selection procedure and parameterisation of each nodal time series, which is used for the spatial and temporal predictions of the changes in the depths and associated uncertainties. The model selection results show that the models can detect the changes in the seafloor depths with spatial consistency and similarity, particularly in the shoaling areas where tidal sandwaves are present. The predicted changes in depths and uncertainties are translated into a probability risk-alert map by evaluating the probabilities of an indicator variable exceeding a certain decision threshold. This research can further support the decision-making process when optimising resurvey frequencies. Full article
(This article belongs to the Special Issue Spatial and Spatiotemporal Methods in Marine Science)
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11 pages, 23315 KiB  
Technical Note
Median Polish Kriging and Sequential Gaussian Simulation for the Spatial Analysis of Source Rock Data
by Emmanouil A. Varouchakis
J. Mar. Sci. Eng. 2021, 9(7), 717; https://doi.org/10.3390/jmse9070717 - 29 Jun 2021
Cited by 2 | Viewed by 1866
Abstract
In this technical note, a geostatistical model was applied to explore the spatial distribution of source rock data in terms of total organic carbon weight concentration. The median polish kriging method was used to approximate the “row and column effect” in the generated [...] Read more.
In this technical note, a geostatistical model was applied to explore the spatial distribution of source rock data in terms of total organic carbon weight concentration. The median polish kriging method was used to approximate the “row and column effect” in the generated array data, in order for the ordinary kriging methodology to be applied by means of the residuals. Moreover, the sequential Gaussian simulation was employed to quantify the uncertainty of the estimates. The modified Box–Cox technique was applied to normalize the residuals and a cross-validation analysis was performed to evaluate the efficiency of the method. A map of the spatial distribution of total organic carbon weight concentration was constructed along with the 5% and 95% confidence intervals. This work encourages the use of the median polish kriging method for similar applications. Full article
(This article belongs to the Special Issue Spatial and Spatiotemporal Methods in Marine Science)
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