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Marine Oil Spills 2023
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Marine Oil Spills 2023

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

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 7455

Special Issue Editor

Prof. Dr. Merv Fingas

E-Mail Website
Guest Editor
Spill Science, Edmonton, AB T6W 1J6, Canada
Interests: oil spills; remote sensing; oil spill behavior and dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Oil spill remote sensing has progressed significantly in the past few years. Remote sensing plays an increasingly important role in oil spill response efforts. Through the use of modern remote sensing instrumentation, oil can be monitored on the open ocean on a 24-hour basis. With knowledge of slick locations, response personnel can more effectively conduct countermeasures.

There is growing progress in the performance of both strategic sensors, such as satellite-borne radars, as well as low cost sensors such as visible and infrared cameras. Most progress has been made in the development of use and application software for all sensors. We are now able to eliminate noise, correct images and then focus on oil spills.

This Special Issue aims to highlight advances in the development, testing, and use of oil spill remote sensing systems. Topics include, but are not limited to:

  • New developments in remote sensing;
  • Software to remove noise and enhance oil spill signals;
  • New sensors and the testing of sensors;
  • The use of remote sensing on spills, e.g., Deepwater Horizon and others;
  • The use of remote sensing for illegal discharge detection;
  • Specialized sensors, such as fluorosensors and thickness sensors;
  • Ship or coastal-mounted sensors;
  • Airborne sensors and campaigns;
  • Drone-mounted sensors.

Dr. Merv Fingas
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

  • oil spill remote sensing
  • oil spill remote sensing software
  • new oil spill sensors
  • the use of remote sensing on spills
  • the use of remote sensing for illegal discharge detection
  • fluorosensors or thickness sensors
  • ship or coastal-mounted oil spill sensors
  • drone-mounted oil spill sensors

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Published Papers (3 papers)

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Research

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20 pages, 6196 KiB  
Article
Research on the Directional Characteristics of the Reflectance of Oil-Contaminated Sea Ice
by Yulong Du, Bingxin Liu, Jiankang Xu, Ying Li, Peng Liu and Peng Chen
J. Mar. Sci. Eng. 2023, 11(8), 1503; https://doi.org/10.3390/jmse11081503 - 28 Jul 2023
Viewed by 1146
Abstract
Remote sensing has been widely used for oil spill monitoring in open waters. However, research on remote sensing monitoring of oil spills in ice-infested sea waters (IISWs) is still scarce. The spectral characteristics of oil-contaminated sea ice (OCSI) and clean sea ice (CSI) [...] Read more.
Remote sensing has been widely used for oil spill monitoring in open waters. However, research on remote sensing monitoring of oil spills in ice-infested sea waters (IISWs) is still scarce. The spectral characteristics of oil-contaminated sea ice (OCSI) and clean sea ice (CSI) and their differences are an important basis for oil spill detection using visible/near-infrared (VNIR) remote sensing. Such features and differences can change with the observation geometry, affecting the identification accuracy. In this study, we carried out multi-angle reflection observation experiments of oil-contaminated sea ice (OCSI) and proposed a kernel-driven bidirectional reflectance distribution function (BRDF) model, Walthall–Ross thick-Litransit-Lisparse-r-RPV (WaRoLstRPV), which takes into account the strong forward-scattering characteristics of sea ice. We also analyzed the preferred observation geometry for oil spill monitoring in IISWs. In the validation using actual measured data, the proposed WaRoLstRPV performed well, with RMSEs of 0.0031 and 0.0026 for CSI and OCSI, respectively, outperforming the commonly used kernel-driven BRDF models, Ross thick-Li sparse (R-LiSpr), QU-Roujean (Qu-R), QU-Lisparse R-r-RPV (Qu-LiSpr-RrRPV), and Walthall (Wa). The observation geometry with a zenith angle around 50° and relative azimuth ranging from 250° to 290° is preferred for oil spill detection in IISWs. Full article
(This article belongs to the Special Issue Marine Oil Spills 2023)
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19 pages, 3829 KiB  
Article
Computational Oil-Slick Hub for Offshore Petroleum Studies
by Nelson F. F. Ebecken, Fernando Pellon de Miranda, Luiz Landau, Carlos Beisl, Patrícia M. Silva, Gerson Cunha, Maria Célia Santos Lopes, Lucas Moreira Dias and Gustavo de Araújo Carvalho
J. Mar. Sci. Eng. 2023, 11(8), 1497; https://doi.org/10.3390/jmse11081497 - 27 Jul 2023
Cited by 1 | Viewed by 1283
Abstract
The paper introduces the Oil-Slick Hub (OSH), a computational platform to facilitate the data visualization of a large database of petroleum signatures observed on the surface of the ocean with synthetic aperture radar (SAR) measurements. This Internet platform offers an information search and [...] Read more.
The paper introduces the Oil-Slick Hub (OSH), a computational platform to facilitate the data visualization of a large database of petroleum signatures observed on the surface of the ocean with synthetic aperture radar (SAR) measurements. This Internet platform offers an information search and retrieval system of a database resulting from >20 years of scientific projects that interpreted ~15 thousand offshore mineral oil “slicks”: natural oil “seeps” versus operational oil “spills”. Such a Digital Mega-Collection Database consists of satellite images and oil-slick polygons identified in the Gulf of Mexico (GMex) and the Brazilian Continental Margin (BCM). A series of attributes describing the interpreted slicks are also included, along with technical reports and scientific papers. Two experiments illustrate the use of the OSH to facilitate the selection of data subsets from the mega collection (GMex variables and BCM samples), in which artificial intelligence techniques—machine learning (ML)—classify slicks into seeps or spills. The GMex variable dataset was analyzed with simple linear discriminant analyses (LDAs), and a three-fold accuracy performance pattern was observed: (i) the least accurate subset (~65%) solely used acquisition aspects (e.g., acquisition beam mode, date, and time, satellite name, etc.); (ii) the best results (>90%) were achieved with the inclusion of location attributes (i.e., latitude, longitude, and bathymetry); and (iii) moderate performances (~70%) were reached using only morphological information (e.g., area, perimeter, perimeter to area ratio, etc.). The BCM sample dataset was analyzed with six traditional ML methods, namely naive Bayes (NB), K-nearest neighbors (KNN), decision trees (DT), random forests (RF), support vector machines (SVM), and artificial neural networks (ANN), and the most effective algorithms per sample subsets were: (i) RF (86.8%) for Campos, Santos, and Ceará Basins; (ii) NB (87.2%) for Campos with Santos Basins; (iii) SVM (86.9%) for Campos with Ceará Basins; and (iv) SVM (87.8%) for only Campos Basin. The OSH can assist in different concerns (general public, social, economic, political, ecological, and scientific) related to petroleum exploration and production activities, serving as an important aid in discovering new offshore exploratory frontiers, avoiding legal penalties on oil-seep events, supporting oceanic monitoring systems, and providing valuable information to environmental studies. Full article
(This article belongs to the Special Issue Marine Oil Spills 2023)
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Review

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21 pages, 3936 KiB  
Review
Deep Learning-Based Approaches for Oil Spill Detection: A Bibliometric Review of Research Trends and Challenges
by Rodrigo N. Vasconcelos, André T. Cunha Lima, Carlos A. D. Lentini, José Garcia V. Miranda, Luís F. F. de Mendonça, José M. Lopes, Mariana M. M. Santana, Elaine C. B. Cambuí, Deorgia T. M. Souza, Diego P. Costa, Soltan G. Duverger and Washington S. Franca-Rocha
J. Mar. Sci. Eng. 2023, 11(7), 1406; https://doi.org/10.3390/jmse11071406 - 12 Jul 2023
Cited by 12 | Viewed by 4295
Abstract
Oil spill detection and mapping using deep learning (OSDMDL) is crucial for assessing its impact on coastal and marine ecosystems. A novel approach was employed in this study to evaluate the scientific literature in this field through bibliometric analysis and literature review. The [...] Read more.
Oil spill detection and mapping using deep learning (OSDMDL) is crucial for assessing its impact on coastal and marine ecosystems. A novel approach was employed in this study to evaluate the scientific literature in this field through bibliometric analysis and literature review. The Scopus database was used to evaluate the relevant scientific literature in this field, followed by a bibliometric analysis to extract additional information, such as architecture type, country collaboration, and most cited papers. The findings highlight significant advancements in oil detection at sea, with a strong correlation between technological evolution in detection methods and improved remote sensing data acquisition. Multilayer perceptrons (MLP) emerged as the most prominent neural network architecture in 11 studies, followed by a convolutional neural network (CNN) in 5 studies. U-Net, DeepLabv3+, and fully convolutional network (FCN) were each used in three studies, demonstrating their relative significance too. The analysis provides insights into collaboration, interdisciplinarity, and research methodology and contributes to the development of more effective policies, strategies, and technologies for mitigating the environmental impact of oil spills in OSDMDL. Full article
(This article belongs to the Special Issue Marine Oil Spills 2023)
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