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Advances in Oil Spill Remote Sensing
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Advances in 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 July 2022) | Viewed by 19751

Special Issue Editor

Dr. Merv Fingas

E-Mail Website
Guest Editor
Spill Science, Edmonton, AB T6W 1J6, Canada
Interests: oil spills; remote sensing; oil spill behaviour 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 in the open ocean on a 24-hour basis. With knowledge of slick locations, response personnel can more effectively commence countermeasures.

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

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
  • Different uses of satellite sensors
  • New sensors and testing of sensors
  • Use of remote sensing on spills
  • 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 and aerostat-mounted sensors

Prof. 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. 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 2700 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 detection
  • Oil spill mapping

Published Papers (6 papers)

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24 pages, 12137 KiB  
Article
An Analysis of the Optimal Features for Sentinel-1 Oil Spill Datasets Based on an Improved J–M/K-Means Algorithm
by Lingxiao Cheng, Ying Li, Xiaohui Zhang and Ming Xie
Remote Sens. 2022, 14(17), 4290; https://doi.org/10.3390/rs14174290 - 31 Aug 2022
Cited by 8 | Viewed by 1804
Abstract
With the rapid development of world shipping, oil spill accidents such as tanker collisions, illegal sewage discharges, and oil pipeline ruptures occur frequently. As the SAR system expands from single polarization to multipolarization, the Polarmetric Synthetic Aperture Radar (Pol-SAR) system has been widely [...] Read more.
With the rapid development of world shipping, oil spill accidents such as tanker collisions, illegal sewage discharges, and oil pipeline ruptures occur frequently. As the SAR system expands from single polarization to multipolarization, the Polarmetric Synthetic Aperture Radar (Pol-SAR) system has been widely used in marine oil spill detection. However, in the studies of the oil spill extraction in SAR images, there are some problems that limit large-scale oil spill detection work. As a transition from single-polarized to full-polarized, the dual-polarized system carries some polarization information and can be obtained in large quantities for free, which has become a major breakthrough in solving the problem of large-scale oil spill detection. In order to optimize the multisource features that can be extracted from dual-polarized SAR images, greatly improve the utilization rate of dual-polarized SAR oil spill images under the premise of reducing workload, and ensure the accuracy of marine oil spill extraction, this paper adopts the metric of inter-class separability, the Jeffries–Matusita distance, which improves on the traditional K-means algorithm by focusing on the noise sensitivity defect of the K-means algorithm; the artificial influence of J–M distance in measuring the separability between classes improves the algorithm in three aspects: sample selection, distance calculation, and data evaluation. Finally, using the inter-sample J–M distance of multisource features, the overall accuracy of image segmentation, the F1-score, and the results of correlation analysis between features, three advantageous features and three subdominant features are selected that can be used for marine oil spill detection. Full article
(This article belongs to the Special Issue Advances in Oil Spill Remote Sensing)
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26 pages, 8172 KiB  
Article
Measuring Floating Thick Seep Oil from the Coal Oil Point Marine Hydrocarbon Seep Field by Quantitative Thermal Oil Slick Remote Sensing
by Ira Leifer, Christopher Melton, William J. Daniel, David M. Tratt, Patrick D. Johnson, Kerry N. Buckland, Jae Deok Kim and Charlotte Marston
Remote Sens. 2022, 14(12), 2813; https://doi.org/10.3390/rs14122813 - 11 Jun 2022
Cited by 4 | Viewed by 1863
Abstract
Remote sensing techniques offer significant potential for generating accurate thick oil slick maps critical for marine oil spill response. However, field validation and methodology assessment challenges remain. Here, we report on an approach to leveraging oil emissions from the Coal Oil Point (COP) [...] Read more.
Remote sensing techniques offer significant potential for generating accurate thick oil slick maps critical for marine oil spill response. However, field validation and methodology assessment challenges remain. Here, we report on an approach to leveraging oil emissions from the Coal Oil Point (COP) natural marine hydrocarbon seepage offshore of southern California, where prolific oil seepage produces thick oil slicks stretching many kilometers. Specifically, we demonstrate and validate a remote sensing approach as part of the Seep Assessment Study (SAS). Thick oil is sufficient for effective mitigation strategies and is set at 0.15 mm. The brightness temperature of thick oil, TBO, is warmer than oil-free seawater, TBW, allowing segregation of oil from seawater. High spatial-resolution airborne thermal and visible slick imagery were acquired as part of the SAS; including along-slick “streamer” surveys and cross-slick calibration surveys. Several cross-slick survey-imaged short oil slick segments that were collected by a customized harbor oil skimmer; termed “collects”. The brightness temperature contrast, ΔTB (TBO − TBW), for oil pixels (based on a semi-supervised classification of oil pixels) and oil thickness, h, from collected oil for each collect provided the empirical calibration of ΔTB(h). The TB probability distributions provided TBO and TBW, whereas a spatial model of TBW provided ΔTB for the streamer analysis. Complicating TBW was the fact that streamers were located at current shears where two water masses intersect, leading to a TB discontinuity at the slick. This current shear arose from a persistent eddy down current of the COP that provides critical steering of oil slicks from the Coal Oil Point. The total floating thick oil in a streamer observed on 23 May and a streamer observed on 25 May 2016 was estimated at 311 (2.3 bbl) and 2671 kg (20 bbl) with mean linear floating oil 0.14 and 2.4 kg m−1 with uncertainties by Monte Carlo simulations of 25% and 7%, respectively. Based on typical currents, the average of these two streamers corresponds to 265 g s−1 (~200 bbl day−1) in a range of 60–340 bbl day−1, with significant short-term temporal variability that suggests slug flow for the seep oil emissions. Given that there are typically four or five streamers, these data are consistent with field emissions that are higher than the literature estimates. Full article
(This article belongs to the Special Issue Advances in Oil Spill Remote Sensing)
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27 pages, 60559 KiB  
Article
Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer
by Zongchen Jiang, Jie Zhang, Yi Ma and Xingpeng Mao
Remote Sens. 2022, 14(1), 157; https://doi.org/10.3390/rs14010157 - 30 Dec 2021
Cited by 19 | Viewed by 3993
Abstract
Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site [...] Read more.
Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight. Full article
(This article belongs to the Special Issue Advances in Oil Spill Remote Sensing)
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18 pages, 5109 KiB  
Article
Feature Merged Network for Oil Spill Detection Using SAR Images
by Yonglei Fan, Xiaoping Rui, Guangyuan Zhang, Tian Yu, Xijie Xu and Stefan Poslad
Remote Sens. 2021, 13(16), 3174; https://doi.org/10.3390/rs13163174 - 11 Aug 2021
Cited by 17 | Viewed by 3208
Abstract
The frequency of marine oil spills has increased in recent years. The growing exploitation of marine oil and continuous increase in marine crude oil transportation has caused tremendous damage to the marine ecological environment. Using synthetic aperture radar (SAR) images to monitor marine [...] Read more.
The frequency of marine oil spills has increased in recent years. The growing exploitation of marine oil and continuous increase in marine crude oil transportation has caused tremendous damage to the marine ecological environment. Using synthetic aperture radar (SAR) images to monitor marine oil spills can help control the spread of oil spill pollution over time and reduce the economic losses and environmental pollution caused by such spills. However, it is a significant challenge to distinguish between oil-spilled areas and oil-spill-like in SAR images. Semantic segmentation models based on deep learning have been used in this field to address this issue. In addition, this study is dedicated to improving the accuracy of the U-Shape Network (UNet) model in identifying oil spill areas and oil-spill-like areas and alleviating the overfitting problem of the model; a feature merge network (FMNet) is proposed for image segmentation. The global features of SAR image, which are high-frequency component in the frequency domain and represents the boundary between categories, are obtained by a threshold segmentation method. This can weaken the impact of spot noise in SAR image. Then high-dimensional features are extracted from the threshold segmentation results using convolution operation. These features are superimposed with to the down sampling and combined with the high-dimensional features of original image. The proposed model obtains more features, which allows the model to make more accurate decisions. The overall accuracy of the proposed method increased by 1.82% and reached 61.90% compared with the UNet. The recognition accuracy of oil spill areas and oil-spill-like areas increased by approximately 3% and reached 56.33%. The method proposed in this paper not only improves the recognition accuracy of the original model, but also alleviates the overfitting problem of the original model and provides a more effective monitoring method for marine oil spill monitoring. More importantly, the proposed method provides a design principle that opens up new development ideas for the optimization of other deep learning network models. Full article
(This article belongs to the Special Issue Advances in Oil Spill Remote Sensing)
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22 pages, 7268 KiB  
Article
SAR Oil Spill Detection System through Random Forest Classifiers
by Marcos Reinan Assis Conceição, Luis Felipe Ferreira de Mendonça, Carlos Alessandre Domingos Lentini, André Telles da Cunha Lima, José Marques Lopes, Rodrigo Nogueira de Vasconcelos, Mainara Biazati Gouveia and Milton José Porsani
Remote Sens. 2021, 13(11), 2044; https://doi.org/10.3390/rs13112044 - 22 May 2021
Cited by 23 | Viewed by 4908
Abstract
A set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset. The first random forest model is an ocean SAR image classifier where the labeling inputs were [...] Read more.
A set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset. The first random forest model is an ocean SAR image classifier where the labeling inputs were oil spills, biological films, rain cells, low wind regions, clean sea surface, ships, and terrain. The second one was a SAR image oil detector named “Radar Image Oil Spill Seeker (RIOSS)”, which classified oil-like targets. An optimized feature space to serve as input to such classification models, both in terms of variance and computational efficiency, was developed. It involved an extensive search from 42 image attribute definitions based on their correlations and classifier-based importance estimative. This number included statistics, shape, fractal geometry, texture, and gradient-based attributes. Mixed adaptive thresholding was performed to calculate some of the features studied, returning consistent dark spot segmentation results. The selected attributes were also related to the imaged phenomena’s physical aspects. This process helped us apply the attributes to a random forest, increasing our algorithm’s accuracy up to 90% and its ability to generate even more reliable results. Full article
(This article belongs to the Special Issue Advances in Oil Spill Remote Sensing)
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16 pages, 6318 KiB  
Technical Note
Satellite Survey of Offshore Oil Seep Sites in the Caspian Sea
by Marina Mityagina and Olga Lavrova
Remote Sens. 2022, 14(3), 525; https://doi.org/10.3390/rs14030525 - 22 Jan 2022
Cited by 7 | Viewed by 2722
Abstract
This paper presents the results of a long-term survey of the Caspian Sea using satellite SAR and multispectral sensors. The primary environmental problem of the Caspian Sea is oil pollution which is determined by its natural properties, mainly by the presence of big [...] Read more.
This paper presents the results of a long-term survey of the Caspian Sea using satellite SAR and multispectral sensors. The primary environmental problem of the Caspian Sea is oil pollution which is determined by its natural properties, mainly by the presence of big oil and gas deposits beneath the seabed. Our research focuses on natural oil slicks (NOS), i.e., oil showings on the sea surface due to natural hydrocarbon emission from seabed seeps. The spatial and temporal variability of NOS in the Caspian Sea and the possibilities of their reliable detection using satellite data are examined. NOS frequency and detectability in satellite images depending on sensor type, season and geographical region are assessed. It is shown that both parameters vary significantly, and largely depend on sensor type and season, with season being most pronounced in visible (VIS) data. The locations of two offshore seep sites at the Iranian and Turkmenian shelves are accurately estimated. Statistics on individual sizes of NOS are drawn. The release rates of crude oil from the seabed to the sea surface are compared. Detailed maps of NOS are put together, and areas exposed to high risk of sea surface oil pollution are determined. Full article
(This article belongs to the Special Issue Advances in Oil Spill Remote Sensing)
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