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Special Issue "Oil Spill Remote Sensing"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2019).

Special Issue Editors

Guest Editor
Dr. Ira Leifer

Bubbleology Research International, 1642 Elm Ave, Solvang, CA 93463, USA
Website | E-Mail
Interests: oil slicks in the ocean; remote sensing; trace gas measurement; trace gas remote sensing; arctic processes; bubble processes; marine seepage
Guest Editor
Dr. Xiaofeng Li

NCWCP - E/RA3, 5830 University Research Court, College Park, MD 20740, U.S.A.
Website | E-Mail
Phone: (301)683-3314
Fax: (301)683-3301
Interests: AI oceanography, big data, ocean remote sensing, physical oceanography, boundary layer meteorology, synthetic aperture radar imaging mechanism, multiple-polarization radar applications, satellite image classification and segmentation

Special Issue Information

Dear Colleagues,

Large oil spills produce massive ecological, economic, and social damage, while more common small oil spills can lead to chronic environmental degradation and health concerns. Traditionally, remote sensing has played a small role in oil spill response, primarily that of detection; however, advances in sensors, algorithms, and computational power are enabling new technologies to play more important roles in oil spill response, monitoring, and mitigation.

Last year, a Special Issue of Remote Sensing presented a broad view of the state-of-the-art in oil spill remote sensing. It is still a hot topic and we would like to cover it again. This Special Issue seeks remote sensing papers in three focus areas:

  1. Coastal marine and inland waterway oil spill remote sensing
  2. Intertidal and terrestrial oil spill remote sensing
  3. Transitioning from academic oil spill remote sensing to operational applications.

This Special Issue will highlight the potential of oil spill remote sensing, revealed by technological development for novel applications (moving beyond detection), including oil thickness quantification, assessment of mitigation strategy efficacy, and ecological impacts (and recovery) monitoring and assessment. The goal is to present a strategy with a clear potential to exploit existent and quick-response data collections, which can be analyzed and transformed into timely information products, directly useable in the initial response, progressive clean-up, and long-term monitoring.

The extent and persistence of the Deepwater Horizon (DWH) oil spill provided an unprecedented opportunity for data collection and algorithm development, which generally is not feasible during typical, far-shorter, oil spills. This Special Issue seeks papers that highlight new developments, including those arising from DWH; however, all papers must address (at a minimum in the discussion) application to other, more-typical, oil spills.

All manuscripts are expected to address key remote sensing issues of validation and uncertainty assessment, as well as data analysis approaches. Given that many key oil spill remote sensing datasets are not collected with validation data, manuscripts may satisfy this requirement by discussing such needs. In addition, any important ancillary data must be identified. More prosaically, manuscripts should include a discussion of a general road map to inform the reader how the remote sensing technology studied can be brought into the operational world to improve oil spill response.

1. Marine (coastal) and inland waterway oil spill remote sensing

Remote sensing of on-water oil spills is highly challenging due to the fluidity of the water surface, its dependence on meteorology, hydrology/oceanography, chemistry, and the difficulty of working in the marine environment. Traditionally, remote sensing has primarily aided detection, by identifying a contrast in sea surface characteristics, which is inferred due to oil; however, this is largely binary, triggering on thin sheens. This leads to a mismatch with the primary oil spill response need of addressing thick oil slicks. Recently, a range of new technologies has demonstrated capabilities to remote sense oil thickness (qualitatively or quantitatively). Manuscripts responsive to this focus should highlight applications that enable thickness discrimination, effective tracking, are diagnostic, and/or enhance confidence in interpretation by reducing false positives and negatives.

In response to several significant riverine oil spills in recent years, attention has focused on specific response needs for inland waterway spills. Rivers bring unique challenges and opportunities to remote sensing, manuscripts investigating this new concern are requested.

Given the massive challenges associated with Arctic oil spill response (from production or from shipping), manuscripts are strongly encouraged that focus on the application of remote sensing to meet critical oil spill response needs in the harsh Arctic environment, both in this focus area, and in the other focus areas.

2. Terrestrial, intertidal, oil spill remote sensing

Oil is introduced into the terrestrial environment during transport and pipeline failures, and inadvertent releases from offshore activities that wash ashore, as well as onshore extraction activities. Often the terrestrial landscapes affected by the spill are wetlands of numerous types that occupy a variety of morphologies and landforms maintained by a specific set of physical processes. Importantly, the ecological importance and sensitivity of these environments can benefit from remote sensing capabilities that can detect detrimental changes in the oil-exposed wetland flora before irreversible damage occurs.

Manuscripts are sought that address the complexity and sensitivity of these transition land–water zones, which requires special remote sensing capabilities that can track oil movement in convoluted tidal channels and embayment’s and into wetland forests, marshes, beaches, mudflats. Of particular interest are algorithms that provide timely detection. Additionally of interest are oil remote sensing studies of these complex terrestrial ecosystems, which can discriminate between vegetation and oil.

Oil spills resulting from land-based petroleum extraction activities are largely immobile (unlike ocean/river oil spills); however, remote sensing also can play a key role. Although oil near to the source likely is identified quickly, oil, far beyond the sources, can become hidden by the vegetation canopy, making the extent of the spill hard to determine. Investigations reporting on new remote sensing technologies that can respond rapidly to the relatively small-scale terrestrial spills are strongly solicited.

3. Transitioning from academic oil spill remote sensing application to operational

Information during most marine oil spills “ages” rapidly, such that interpretations generally have lost most of their value after half a day. Manuscripts responsive to this focus area are expected to highlight the specific adaptations needed for rapid remote sensing (existing or needed). The key need is for speed over accuracy; however, responders need to have confidence in interpretation including any critical ancillary data needed for interpretation. In addition, manuscripts that focus on the enabling the policies and programmatic structures that can facilitate this transition are strongly encouraged.

Dr. Ira Leifer
Dr. Xiaofeng Li
Guest Editors

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. Remote Sensing is an international peer-reviewed open access semimonthly 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 1800 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

  • Remote sensing
  • Oil spill
  • Thickness
  • Validation
  • Ecosystem

Published Papers (7 papers)

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Research

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Open AccessArticle
Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)
Remote Sens. 2019, 11(14), 1652; https://doi.org/10.3390/rs11141652
Received: 10 May 2019 / Revised: 13 June 2019 / Accepted: 24 June 2019 / Published: 11 July 2019
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Abstract
A novel empirical approach to categorize oil slicks’ sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear [...] Read more.
A novel empirical approach to categorize oil slicks’ sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear discriminant analysis (LDA) to try accuracy improvements from our previously published methods of discriminating seeps from spills that achieved ~70% of overall accuracy. Analyzing 244 RADARSAT-2 scenes containing 4562 slicks observed in Campeche Bay (Gulf of Mexico), our exploratory data analysis evaluates the impact of 61 combinations of SAR backscatter coefficients (σ°, β°, γ°), SAR calibrated products (received radar beam given in amplitude or decibel, with or without a despeckle filter), and data transformations (none, cube root, log10). The LDA ability to discriminate the oil-slick category is rather independent of backscatter coefficients and calibrated products, but influenced by data transformations. The combination of attributes plays a role in the discrimination; combining oil-slicks’ size and SAR information is more effective. We have simplified our analyses using fewer attributes to reach accuracies comparable to those of our earlier studies, and we suggest using other multivariate data analyses—cubist or random forest—to attempt to further improve oil-slick category discrimination. Full article
(This article belongs to the Special Issue Oil Spill Remote Sensing)
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Open AccessArticle
Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders
Remote Sens. 2019, 11(12), 1402; https://doi.org/10.3390/rs11121402
Received: 19 April 2019 / Revised: 11 June 2019 / Accepted: 12 June 2019 / Published: 12 June 2019
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Abstract
We present a method to detect maritime oil spills from Side-Looking Airborne Radar (SLAR) sensors mounted on aircraft in order to enable a quick response of emergency services when an oil spill occurs. The proposed approach introduces a new type of neural architecture [...] Read more.
We present a method to detect maritime oil spills from Side-Looking Airborne Radar (SLAR) sensors mounted on aircraft in order to enable a quick response of emergency services when an oil spill occurs. The proposed approach introduces a new type of neural architecture named Convolutional Long Short Term Memory Selectional AutoEncoders (CMSAE) which allows the simultaneous segmentation of multiple classes such as coast, oil spill and ships. Unlike previous works using full SLAR images, in this work only a few scanlines from the beam-scanning of radar are needed to perform the detection. The main objective is to develop a method that performs accurate segmentation using only the current and previous sensor information, in order to return a real-time response during the flight. The proposed architecture uses a series of CMSAE networks to process in parallel each of the objectives defined as different classes. The output of these networks are given to a machine learning classifier to perform the final detection. Results show that the proposed approach can reliably detect oil spills and other maritime objects in SLAR sequences, outperforming the accuracy of previous state-of-the-art methods and with a response time of only 0.76 s. Full article
(This article belongs to the Special Issue Oil Spill Remote Sensing)
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Open AccessArticle
On Capabilities of Tracking Marine Surface Currents Using Artificial Film Slicks
Remote Sens. 2019, 11(7), 840; https://doi.org/10.3390/rs11070840
Received: 13 March 2019 / Revised: 29 March 2019 / Accepted: 4 April 2019 / Published: 8 April 2019
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Abstract
It is known that films on the sea surface can appear due to ship pollution, river and collector drains, as well as natural biological processes. Marine film slicks can indicate various geophysical processes in the upper layer of the ocean and in the [...] Read more.
It is known that films on the sea surface can appear due to ship pollution, river and collector drains, as well as natural biological processes. Marine film slicks can indicate various geophysical processes in the upper layer of the ocean and in the atmosphere. In particular, slick signatures in SAR-imagery of the sea surface at low and moderate wind speeds are often associated with marine currents. Apart from the current itself, other factors such as wind and the physical characteristics of films can significantly influence the dynamics of slick structures. In this paper, a prospective approach aimed at measuring surface currents is developed. The approach is based on the investigation of the geometry of artificial banded slicks formed under the action of marine currents and on the retrieval of the current characteristics from this geometry. The developed approach is applied to quasi stationary slick bands under conditions when the influence of the film spreading effects can be neglected. For the stationary part of the slick band where transition processes of the band formation, e.g., methods of application of surfactants on water, film spreading processes, possible wind transformation etc., become negligible, some empirical relations between the band geometrical characteristics and the characteristics of the surface currents are obtained. The advantage of the approach is a possibility of getting information concerning the spatial structure of marine currents along the entire slick band. The suggested approach can be efficient for remote sensing data verification. Full article
(This article belongs to the Special Issue Oil Spill Remote Sensing)
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Open AccessArticle
Dual-Polarized L-Band SAR Imagery for Temporal Monitoring of Marine Oil Slick Concentration
Remote Sens. 2018, 10(7), 1012; https://doi.org/10.3390/rs10071012
Received: 7 May 2018 / Revised: 15 June 2018 / Accepted: 20 June 2018 / Published: 25 June 2018
Cited by 1 | PDF Full-text (6191 KB) | HTML Full-text | XML Full-text
Abstract
SAR sensors are usually used in the offshore domain to detect marine oil slicks which allows the authorities to guide cleanup operations or prosecute polluters. As radar imagery can be used any time of day or year and in almost any weather conditions, [...] Read more.
SAR sensors are usually used in the offshore domain to detect marine oil slicks which allows the authorities to guide cleanup operations or prosecute polluters. As radar imagery can be used any time of day or year and in almost any weather conditions, the use and programming of such remote sensing data is usually favored over optical imagery. Nevertheless, images collected in the optical domain provide access to key information not accessible today by SAR instruments, such as the thickness or the amount of pollutant. To address this knowledge gap, a methodology based on the joint use of a scattering model (U-WCA) and remote sensing data collected by a low frequency (e.g., L-band) imaging radar over controlled release of mineral oil spill is reported in this paper. The proposed method allows estimation of the concentration of pollutant within an oil-in-water mixture as well as the temporal variation of this quantity due to weathering processes. Full article
(This article belongs to the Special Issue Oil Spill Remote Sensing)
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Open AccessArticle
Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images
Remote Sens. 2018, 10(3), 421; https://doi.org/10.3390/rs10030421
Received: 5 December 2017 / Revised: 27 January 2018 / Accepted: 7 March 2018 / Published: 9 March 2018
Cited by 1 | PDF Full-text (11775 KB) | HTML Full-text | XML Full-text
Abstract
It is important to detect floating oil slicks after spill accidents, and hyperspectral remote sensing technology is capable of achieving this task. Traditional methods mainly utilize the spectral indices of hydrocarbons to detect floating oil slicks, but are poor at distinguishing the thickness [...] Read more.
It is important to detect floating oil slicks after spill accidents, and hyperspectral remote sensing technology is capable of achieving this task. Traditional methods mainly utilize the spectral indices of hydrocarbons to detect floating oil slicks, but are poor at distinguishing the thickness of oil slicks and cannot detect sheens. Since the spectra of oil slicks should be affected by seawater as well as oil, this paper investigated the use of spectral indices of hydrocarbons and seawater to identify different thicknesses of oil slicks. In this research, a measurement, called index separability (IS), was proposed for quantitatively evaluating the identification ability of these spectral indices. Based on the evaluation results, experiments were conducted to validate the applicability of these spectral indices. The results show that the spectral indices of hydrocarbons are more suitable for detecting continuous true color oil slicks and emulsions and that spectral indices of seawater are more suitable for sheens and seawater. In addition, the spectral indices of hydrocarbons and seawater are complementary for detecting oil slicks. Finally, combining the spectral indices of hydrocarbons and seawater is conducive to achieving more accurate oil slick recognition results. Full article
(This article belongs to the Special Issue Oil Spill Remote Sensing)
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Review

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Open AccessReview
The Challenges of Remotely Measuring Oil Slick Thickness
Remote Sens. 2018, 10(2), 319; https://doi.org/10.3390/rs10020319
Received: 18 January 2018 / Revised: 30 January 2018 / Accepted: 13 February 2018 / Published: 20 February 2018
Cited by 6 | PDF Full-text (3551 KB) | HTML Full-text | XML Full-text
Abstract
The thickness of oil spills on the sea is an important but poorly studied topic. Means to measure slick thickness are reviewed. More than 30 concepts are summarized. Many of these are judged not to be viable for a variety of scientific reasons. [...] Read more.
The thickness of oil spills on the sea is an important but poorly studied topic. Means to measure slick thickness are reviewed. More than 30 concepts are summarized. Many of these are judged not to be viable for a variety of scientific reasons. Two means are currently available to remotely measure oil thickness, namely, passive microwave radiometry and time of acoustic travel. Microwave radiometry is commercially developed at this time. Visual means to ascertain oil thickness are restricted by physics to thicknesses smaller than those of rainbow sheens, which rarely occur on large spills, and thin sheen. One can observe that some slicks are not sheen and are probably thicker. These three thickness regimes are not useful to oil spill countermeasures, as most of the oil is contained in the thick portion of a slick, the thickness of which is unknown and ranges over several orders of magnitude. There is a continuing need to measure the thickness of oil spills. This need continues to increase with time, and further research effort is needed. Several viable concepts have been developed but require further work and verification. One of the difficulties is that ground truthing and verification methods are generally not available for most thickness measurement methods. Full article
(This article belongs to the Special Issue Oil Spill Remote Sensing)
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Other

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Open AccessCase Report
Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding
Remote Sens. 2019, 11(7), 756; https://doi.org/10.3390/rs11070756
Received: 17 March 2019 / Revised: 25 March 2019 / Accepted: 26 March 2019 / Published: 28 March 2019
PDF Full-text (13639 KB) | HTML Full-text | XML Full-text
Abstract
Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding [...] Read more.
Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation. Full article
(This article belongs to the Special Issue Oil Spill Remote Sensing)
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