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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 November 2017) | Viewed by 74395

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Special Issue Editor

Dr. Merv Fingas

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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 on 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, as well as 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
New sensors and testing of sensors
Use of remote sensing on spills, e.g., DeepWater Horizon and others
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.

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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
Use of remote sensing on spills
Use of remote sensing for illegal discharge detection
Fluorosensors or thickness sensors
Ship or coastal-mounted oil spill sensors
Drone and aerostat-mounted oil spill sensors

Published Papers (12 papers)

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Research

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13 pages, 29258 KiB

Open AccessArticle

Experimental L-Band Airborne SAR for Oil Spill Response at Sea and in Coastal Waters

by Cathleen E. Jones and Benjamin Holt

Sensors 2018, 18(2), 641; https://doi.org/10.3390/s18020641 - 22 Feb 2018

Cited by 35 | Viewed by 5213

Abstract

Satellite synthetic aperture radar (SAR) is frequently used during oil spill response efforts to identify oil slick extent, but suffers from the major disadvantages of potential long latency between when a spill occurs and when a satellite can image the site and an inability to continuously track the spill as it develops. We show using data acquired with the Uninhabited Aerial Vehicle SAR (UAVSAR) instrument how a low noise, high resolution, L-band SAR could be used for oil spill response, with specific examples of tracking slick extent, position and weathering; determining zones of relatively thicker or more emulsified oil within a slick; and identifying oil slicks in coastal areas where look-alikes such as calm waters or biogenic slicks can confound the identification of mineral oil spills. From these key points, the essential features of an airborne SAR system for operational oil spill response are described, and further research needed to determine SAR’s capabilities and limitations in quantifying slick thickness is discussed. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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20 pages, 3181 KiB

Open AccessArticle

Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact

by Shruti Khanna, Maria J. Santos, Susan L. Ustin, Kristen Shapiro, Paul J. Haverkamp and Mui Lay

Sensors 2018, 18(2), 558; https://doi.org/10.3390/s18020558 - 12 Feb 2018

Cited by 29 | Viewed by 5343

Abstract

Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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17 pages, 3245 KiB

Open AccessArticle

Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction

by Bingxin Liu, Ying Li, Chengyu Liu, Feng Xie and Jan-Peter Muller

Sensors 2018, 18(1), 234; https://doi.org/10.3390/s18010234 - 15 Jan 2018

Cited by 17 | Viewed by 4170

Abstract

Researchers have studied oil spills in open waters using remote sensors, but few have focused on extracting reflectance features of oil pollution on sea ice. An experiment was conducted on natural sea ice in Bohai Bay, China, to obtain the spectral reflectance of oil-contaminated sea ice. The spectral absorption index (SAI), spectral peak height (SPH), and wavelet detail coefficient (DWT d5) were calculated using stepwise multiple linear regression. The reflectances of some false targets were measured and analysed. The simulated false targets were sediment, iron ore fines, coal dust, and the melt pool. The measured reflectances were resampled using five common sensors (GF-2, Landsat8-OLI, Sentinel3-OLCI, MODIS, and AVIRIS). Some significant spectral features could discriminate between oil-polluted and clean sea ice. The indices correlated well with the oil area fractions. All of the adjusted R² values exceeded 0.9. The SPH model1, based on spectral features at 507–670 and 1627–1746 nm, displayed the best fitting. The resampled data indicated that these multi-spectral and hyper-spectral sensors could be used to detect crude oil on the sea ice if the effect of noise and spatial resolution are neglected. The spectral features and their identified changes may provide reference on sensor design and band selection. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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15 pages, 6084 KiB

Open AccessArticle

Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation

by Pablo Gil and Beatriz Alacid

Sensors 2018, 18(1), 151; https://doi.org/10.3390/s18010151 - 08 Jan 2018

Cited by 10 | Viewed by 4866

Abstract

This work presents a method for oil-spill detection on Spanish coasts using aerial Side-Looking Airborne Radar (SLAR) images, which are captured using a Terma sensor. The proposed method uses grayscale image processing techniques to identify the dark spots that represent oil slicks on the sea. The approach is based on two steps. First, the noise regions caused by aircraft movements are detected and labeled in order to avoid the detection of false-positives. Second, a segmentation process guided by a map saliency technique is used to detect image regions that represent oil slicks. The results show that the proposed method is an improvement on the previous approaches for this task when employing SLAR images. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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7526 KiB

Open AccessArticle

Design and Implementation of a Coastal-Mounted Sensor for Oil Film Detection on Seawater

by Yongchao Hou, Ying Li, Bingxin Liu, Yu Liu and Tong Wang

Sensors 2018, 18(1), 70; https://doi.org/10.3390/s18010070 - 28 Dec 2017

Cited by 26 | Viewed by 4291

Abstract

The routine surveillance of oil spills in major ports is important. However, existing techniques and sensors are unable to trace oil and micron-thin oil films on the surface of seawater. Therefore, we designed and studied a coastal-mounted sensor, using ultraviolet-induced fluorescence and fluorescence-filter systems (FFSs), to monitor oil spills and overcome the disadvantages of traditional surveillance systems. Using seawater from the port of Lingshui (Yellow Sea, China) and six oil samples of different types, we found that diesel oil’s relative fluorescence intensity (RFI) was significantly higher than those of heavy fuel and crude oils in the 180–300 nm range—in the 300–400 nm range, the RFI value of diesel is far lower. The heavy fuel and crude oils exhibited an opposite trend in their fluorescence spectra. A photomultiplier tube, employed as the fluorescence detection unit, efficiently monitored different oils on seawater in field experiments. On-site tests indicated that this sensor system could be used as a coastal-mounted early-warning detection system for oil spills. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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10923 KiB

Open AccessArticle

Adaptive Enhancement of X-Band Marine Radar Imagery to Detect Oil Spill Segments

by Peng Liu, Ying Li, Jin Xu and Xueyuan Zhu

Sensors 2017, 17(10), 2349; https://doi.org/10.3390/s17102349 - 14 Oct 2017

Cited by 19 | Viewed by 4206

Abstract

Oil spills generate a large cost in environmental and economic terms. Their identification plays an important role in oil-spill response. We propose an oil spill detection method with improved adaptive enhancement on X-band marine radar systems. The radar images used in this paper were acquired on 21 July 2010, from the teaching-training ship “YUKUN” of the Dalian Maritime University. According to the shape characteristic of co-channel interference, two convolutional filters are used to detect the location of the interference, followed by a mean filter to erase the interference. Small objects, such as bright speckles, are taken as a mask in the radar image and improved by the Fields-of-Experts model. The region marked by strong reflected signals from the sea’s surface is selected to identify oil spills. The selected region is subject to improved adaptive enhancement designed based on features of radar images. With the proposed adaptive enhancement technique, calculated oil spill detection is comparable to visual interpretation in accuracy. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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1495 KiB

Open AccessArticle

Analytical Modeling Tool for Design of Hydrocarbon Sensitive Optical Fibers

by Khalil Al Handawi, Nader Vahdati, Oleg Shiryayev and Lydia Lawand

Sensors 2017, 17(10), 2227; https://doi.org/10.3390/s17102227 - 28 Sep 2017

Cited by 4 | Viewed by 5739

Abstract

Pipelines are the main transportation means for oil and gas products across large distances. Due to the severe conditions they operate in, they are regularly inspected using conventional Pipeline Inspection Gages (PIGs) for corrosion damage. The motivation for researching a real-time distributed monitoring solution arose to mitigate costs and provide a proactive indication of potential failures. Fiber optic sensors with polymer claddings provide a means of detecting contact with hydrocarbons. By coating the fibers with a layer of metal similar in composition to that of the parent pipeline, corrosion of this coating may be detected when the polymer cladding underneath is exposed to the surrounding hydrocarbons contained within the pipeline. A Refractive Index (RI) change occurs in the polymer cladding causing a loss in intensity of a traveling light pulse due to a reduction in the fiber’s modal capacity. Intensity losses may be detected using Optical Time Domain Reflectometry (OTDR) while pinpointing the spatial location of the contact via time delay calculations of the back-scattered pulses. This work presents a theoretical model for the above sensing solution to provide a design tool for the fiber optic cable in the context of hydrocarbon sensing following corrosion of an external metal coating. Results are verified against the experimental data published in the literature. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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9238 KiB

Open AccessArticle

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN

by Hao Guo, Danni Wu and Jubai An

Sensors 2017, 17(8), 1837; https://doi.org/10.3390/s17081837 - 09 Aug 2017

Cited by 53 | Viewed by 4943

Abstract

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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17220 KiB

Open AccessArticle

Hyperspectral and Radar Airborne Imagery over Controlled Release of Oil at Sea

by Sébastien Angelliaume, Xavier Ceamanos, Françoise Viallefont-Robinet, Rémi Baqué, Philippe Déliot and Véronique Miegebielle

Sensors 2017, 17(8), 1772; https://doi.org/10.3390/s17081772 - 02 Aug 2017

Cited by 43 | Viewed by 5334

Abstract

Remote sensing techniques are commonly used by Oil and Gas companies to monitor hydrocarbon on the ocean surface. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as thickness and composition of the detected oil, which is critical for both exploration purposes and efficient cleanup operations. Today, state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI (Système Expérimental de Télédection Hyperfréquence Imageur), the airborne system developed by ONERA (the French Aerospace Lab), during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this dataset lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the EM spectrum. Specific processing techniques have been developed to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows estimating slick surface properties such as the age of the emulsion released at sea, the spatial abundance of oil and the relative concentration of hydrocarbons remaining on the sea surface. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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6043 KiB

Open AccessArticle

Acoustic Emission and Echo Signal Compensation Techniques Applied to an Ultrasonic Logging-While-Drilling Caliper

by Yongchao Yao, Xiaodong Ju, Junqiang Lu and Baiyong Men

Sensors 2017, 17(6), 1351; https://doi.org/10.3390/s17061351 - 10 Jun 2017

Cited by 12 | Viewed by 7222

Abstract

A logging-while-drilling (LWD) caliper is a tool used for the real-time measurement of a borehole diameter in oil drilling engineering. This study introduces the mechanical structure and working principle of a new LWD caliper based on ultrasonic distance measurement (UDM). The detection range is a major performance index of a UDM system. This index is determined by the blind zone length and remote reflecting interface detection capability of the system. To reduce the blind zone length and detect near the reflecting interface, a full bridge acoustic emission technique based on bootstrap gate driver (BGD) and metal-oxide-semiconductor field effect transistor (MOSFET) is designed by analyzing the working principle and impedance characteristics of a given piezoelectric transducer. To detect the remote reflecting interface and reduce the dynamic range of the received echo signals, the relationships between the echo amplitude and propagation distance of ultrasonic waves are determined. A signal compensation technique based on time-varying amplification theory, which can automatically change the gain according to the echo arrival time is designed. Lastly, the aforementioned techniques and corresponding circuits are experimentally verified. Results show that the blind zone length in the UDM system of the LWD caliper is significantly reduced and the capability to detect the remote reflecting interface is considerably improved. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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2427 KiB

Open AccessArticle

Fluorometric Index for Sensing Oil in the Sea Environment

by Emilia Baszanowska and Zbigniew Otremba

Sensors 2017, 17(6), 1276; https://doi.org/10.3390/s17061276 - 02 Jun 2017

Cited by 16 | Viewed by 3673

Abstract

Excitation-emission matrix spectroscopy (EEMS) was applied to determine the fluorometric index (FI) as a parameter indicating the presence of a source of oil pollution in a specific area of the sea. Seawater from the Polish coast (the Baltic Sea) and the same water combined with various amounts of crude oil extracted from the Baltic Sea shelf (Petrobaltic-type oil) were used in this study. The FI values were calculated for excitation and emission wavelengths found at the maximal peak, taking into account the natural seawater and the seawater artificially contaminated (for an oil-to-water ratio range of 0.5 × 10⁻⁶ − 500 × 10⁻⁶). The wavelength configurations (Ex/Em) (225/355 and 225/340) for the FI index were applied. It was found that, independent of the amount of oil, the FI achieves a higher value for natural seawater than for seawater that has had contact with oil. These results provide the basis to design a sensor signaling the appearance of oil in a defined sea area. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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Review

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7728 KiB

Open AccessReview

A Review of Oil Spill Remote Sensing

by Merv Fingas and Carl E. Brown

Sensors 2018, 18(1), 91; https://doi.org/10.3390/s18010091 - 30 Dec 2017

Cited by 273 | Viewed by 18506

Abstract

The technical aspects of oil spill remote sensing are examined and the practical uses and drawbacks of each technology are given with a focus on unfolding technology. The use of visible techniques is ubiquitous, but limited to certain observational conditions and simple applications. Infrared cameras offer some potential as oil spill sensors but have several limitations. Both techniques, although limited in capability, are widely used because of their increasing economy. The laser fluorosensor uniquely detects oil on substrates that include shoreline, water, soil, plants, ice, and snow. New commercial units have come out in the last few years. Radar detects calm areas on water and thus oil on water, because oil will reduce capillary waves on a water surface given moderate winds. Radar provides a unique option for wide area surveillance, all day or night and rainy/cloudy weather. Satellite-carried radars with their frequent overpass and high spatial resolution make these day–night and all-weather sensors essential for delineating both large spills and monitoring ship and platform oil discharges. Most strategic oil spill mapping is now being carried out using radar. Slick thickness measurements have been sought for many years. The operative technique at this time is the passive microwave. New techniques for calibration and verification have made these instruments more reliable. Full article

(This article belongs to the Special Issue Sensors for Oil Applications)

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