Remotely Measuring Oil Slick Thickness: An Epic Challenge

Mapping Intimacies ◽

10.5194/egusphere-egu21-978 ◽

2021 ◽

Author(s):

Merv Fingas

Keyword(s):

Oil Spills ◽

Research Effort ◽

Thickness Measurement ◽

Measurement Methods ◽

Microwave Radiometry ◽

Oil Slick ◽

Verification Methods ◽

Ground Truthing ◽

Passive Microwave Radiometry ◽

Oil Thickness

<p>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. 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 (~3 &#181;m), 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.</p>

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Real-Time Thickness Measurement of Marine Oil Spill by Fiber-Optic Surface Plasmon Resonance Sensors

Frontiers in Marine Science ◽

10.3389/fmars.2021.764970 ◽

2022 ◽

Vol 8 ◽

Author(s):

Huiting Yin ◽

Shaohuang Chen ◽

Renliang Huang ◽

Heng Chang ◽

Jiayue Liu ◽

...

Keyword(s):

Surface Plasmon Resonance ◽

Surface Plasmon ◽

Plasmon Resonance ◽

Oil Spills ◽

Thickness Measurement ◽

Detection Sensitivity ◽

Marine Oil ◽

Oil Slick ◽

Marine Oil Spills ◽

Oil Thickness

Rapid detection of marine oil spills is becoming increasingly critical in the face of frequent marine oil spills. Oil slick thickness measurement is critical in the hazard assessment of such oil leaks. As surface plasmon resonance (SPR) sensors are sensitive to slight changes in refractive index, they can monitor offshore oil spills arising from significant differences in the refractive index between oil and water. This study presents a gold-film fiber-optic surface plasmon resonance (FOSPR) sensor prepared by polydopamine accelerated wet chemical plating for rapid and real-time measurement of oil slick thickness. We examined oil thickness detection at two interfaces, namely, water-oil and air-oil. Detection sensitivity of −1.373%/mm is obtained at the water-oil interface in the thickness range of 0–5 mm; detection sensitivity of −2.742%/mm is obtained at the air-oil interface in the thickness range of 0–10 mm. Temperature and salinity present negligible effects on the oil slick thickness measurement. The fabricated FOSPR sensor has the ability to detect the presence of oil as well as quantify the oil thickness. It has favorable repeatability and reusability, demonstrating the significant potential for use in the estimation of marine oil slick thickness.

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Detection of Actionable Oil using RADARSAT-2 and Simulated RADARSAT Constellation Mission Data

International Oil Spill Conference Proceedings ◽

10.7901/2169-3358-2021.1.1141664 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Gordon Staples ◽

Oscar Garcia ◽

Ji Chen ◽

Benjamin Desschamps ◽

Dean Flett

Keyword(s):

Oil Spills ◽

Free Water ◽

Relative Difference ◽

Oil Slick ◽

The North ◽

The North Sea ◽

Entropy Parameter ◽

The Impact ◽

Oil Thickness

Abstract 688970 For oil spill response, one of the key parameters is detection of actionable oil. Actionable oil (AO), which tends to be thick, emulsified oil, refers to oil that can be cleaned-up versus non-actionable oil (sheen) which cannot be readily cleaned-up. Previous studies by MDA of a controlled oil spills in the North Sea have shown the capability of RADARSAT-2 quad polarized data to detect AO using the entropy parameter (H) derived from the Cloude-Pottier decomposition. H → 0 for oil-free water and H → 1 in the presence of an oil slick. To further test the detection of AO, RADARSAT-2, ASTER, WorldView imagery and in situ measurement were acquired April 25, 2017 at the MC20 site off Louisiana. Five oil thickness classes ranging from 1 micron to 100 microns were derived from a maximum likelihood classifier based on the ASTER and WorldView images and in situ samples. For oil-free water, the average H was 0.13 and 0.62 for the slick. There was good correlation between the variability of H and the oil thickness classes. Specifically, larger H was correlated with oil thickness in the 50 – 200 micron range and smaller H was correlated with oil in the 1 micro range. Not surprising there was overlap in H for area were the oil thickness was ~ 1 micron and the area deemed to be slick-free. The results indicate that actionable oil can be discriminated from non-actionable oil based on the relative difference of H. Although these results are encouraging, one of the operational limitations is based on the use of the relatively small swath-width (25 km – 50 km) of the RADARSAT-2 quad polarized mode. The impact of the swath-width can be mitigated with data from the RADARSAT Constellation Mission (RCM). The RCM has a compact polarimetry (CP) mode that provides more polarimetric information than dual polarized modes, less than quad polarized modes, but is available for swath widths up to 500 km. Analysis of simulated SC50 RCM data (50 m resolution, 350 km swath width) derived from the aforementioned RADARSAT-2 image shows similar oil-slick variability that was observed in the RADARSAT-2 image and hence the capability to detect AO.

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Development of a winter snow water equivalent algorithm using in situ passive microwave radiometry over snow-covered first-year sea ice

Remote Sensing of Environment ◽

10.1016/j.rse.2006.07.018 ◽

2007 ◽

Vol 106 (1) ◽

pp. 75-88 ◽

Cited By ~ 16

Author(s):

A. Langlois ◽

D.G. Barber ◽

B.J. Hwang

Keyword(s):

Sea Ice ◽

Snow Water Equivalent ◽

Passive Microwave ◽

Microwave Radiometry ◽

First Year ◽

Winter Snow ◽

Snow Water ◽

Passive Microwave Radiometry

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Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data

Scientific Reports ◽

10.1038/s41598-021-88301-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Vaishali Chaudhary ◽

Shashi Kumar

Keyword(s):

Oil Spills ◽

Marine Ecosystem ◽

Dark Spot ◽

Accurate Estimation ◽

Support Vector ◽

Potential Hazard ◽

Significant Drop ◽

Oil Slick ◽

Spot Detection ◽

Marine Surface

AbstractOil spills are a potential hazard, causing the deaths of millions of aquatic animals and this leaves a calamitous effect on the marine ecosystem. This research focuses on evaluating the potential of polarimetric parameters in discriminating the oil slick from water and also possible thicker/thinner zones within the slick. For this purpose, L-band UAVSAR quad-pol data of the Gulf of Mexico region is exploited. A total number of 19 polarimetric parameters are examined to study their behavior and ability in distinguishing oil slick from water and its own less or more oil accumulated zones. The simulation of compact-pol data from UAVSAR quad-pol data is carried out which has shown good performance in detection and discrimination of oil slick from water. To know the extent of separation between oil and water classes, a statistical separability analysis is carried out. The outcomes of each polarimetric parameter from separability analysis are then quantified with the radial basis function (RBF) supervised Support Vector Machine classifier followed with an accurate estimation of the results. Moreover, a comparison of the achieved and estimated accuracy has shown a significant drop in accuracy values. It has been observed that the highest accuracy is given by LHV compact-pol decomposition and coherency matrix with a classification accuracy of ~ 94.09% and ~ 94.60%, respectively. The proposed methodology has performed well in discriminating the oil slick by utilizing UAVSAR dataset for both quad-pol and compact-pol simulation.

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