Characterization and discrimination of evolving mineral and plant oil slicks based on L-band synthetic aperture radar (SAR)

Jones, Cathleen E.; Espeseth, Martine M.; Holt, Benjamin; Brekke, Camilla; Skrunes, Stine

doi:10.1117/12.2241266

Cited by 8 publications

(12 citation statements)

References 18 publications

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“…Based on the experiment presented in [20], the damping ratio studied here will, most likely, increase with oil thickness. The damping ratio has been used in several oil spill studies to identify internal zones [3], [11], [22], to extract the volumetric mixing ratio of oil in water [6], and to identify areas containing thicker oil within a slick [12]. The VV channel is used when calculating the damping ratio in preference to the HH and HV channels because VV provides higher contrast between oil and clean sea and is less affected by the system noise [1], [23], [24].…”

Section: A Vv-damping Ratiomentioning

confidence: 99%

Oil-Spill-Response-Oriented Information Products Derived From a Rapid-Repeat Time Series of SAR Images

Espeseth

Jones

Holt

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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New quantitative and semi-automatable methods for analyzing oil slick evolution using a time series of L-band Synthetic Aperture Radar (SAR) images with short repeat time are developed and explored. In this study, two methods that are complementary in terms of identifying temporal changes within an oil slick are presented. The two methods reflect two ways of evaluating the oil slicks. The first method identifies regions within the slick that show persistently high damping ratio (the contrast between clean sea and oil intensity), using higher damping values as a proxy for increasing oil thickness. This method also weights the age of the scenes as the algorithm incorporates new images. The second method outputs the short-term drift pattern and the changes in the damping ratios and copolarization ratios between two scenes, proxies for thickness and emulsification. Both methods can aid in identifying regions of high priority for oil recovery. Due to the simplicity of the methods, they can be adapted to time series data from different types of sensors, e.g., optical and SAR imagery. The methods are demonstrated on three L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) time series acquired in November 2016 over a persistent seep in the Mississippi Canyon block 20 (MC-20) of the Gulf of Mexico. The results of the two methods clearly show the movement and the weathering of the oil as a function of both time and location.

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Section: A Vv-damping Ratiomentioning

confidence: 99%

Oil-Spill-Response-Oriented Information Products Derived From a Rapid-Repeat Time Series of SAR Images

Espeseth

Jones

Holt

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In general, the viscosity of crude oils will tend to be larger than that of natural oils. This means that crude oils will tend to spread out less rapidly than biogenic slicks and will have less homogenous regions in oil thickness and volumetric water content [20].…”

Section: B Slick Characteristics and Weatheringmentioning

confidence: 99%

Retrieval of Marine Surface Slick Dielectric Properties From Radarsat-2 Data via a Polarimetric Two-Scale Model

Quigley

Brekke

Eltoft

2020

IEEE Trans. Geosci. Remote Sensing

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We propose the use of a polarimetric two-scale surface scattering model to retrieve the dielectric parameters of oil slick from polarimetric synthetic aperture radar. The ocean surface is modeled as an ensemble of randomly orientated, slightly roughened, tilted facets for which the small perturbation model is assumed to be valid under the condition of no tilt. The orientation of the random facets causes a variation in the in-plane and out-of-plane tilt angles. As the original model utilizes both the co-polarization and cross-polarization channels to determine both the dielectric and roughness characteristics simultaneously from a series of look-up tables, the model is adapted from its original form in order to estimate the roughness characteristics of the scattering surface first, before the dielectric properties are inferred. The performance of the altered scattering model is then evaluated by applying it to multiple sets of quad-polarimetric data containing verified oil slicks, acquired from oil-on-water clean-up exercises in the North Sea. Histograms of retrieved values for the modulus of the dielectric constant indicate that the model is able to invert for values similar to the actual value of 2.3, the dielectric constant of pure crude oil at the lower limit, with successively higher values being found up to values of approximately 20 at the edges of the slicks. An error evaluation is also presented and demonstrates that sources of error are related to the alteration of the model to suit co-polarimetric data and the variance of the speckle which is related to the size of the averaging window. While the results are interesting, the approach is limited to the use of only the ratio of the co-polarimetric channels, from which two unknowns are estimated.

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“…The potential of the polarimetric SAR (PolSAR) data in full, dual, and compact polarization modes was indicated to distinguish petrogenic from biogenic oil slicks and other look-alikes in some circumstances [8][9][10]. However, methodologies able to characterize oil slicks by extracting additional information regarding the thickness variations within the slicks have been less explored until recently [2,5,[11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…PolSAR data has the potential to detect a wide range of scattering mechanisms that may be related to oil slick thickness, weathering, as well as different concentrations of water in oil-mixtures and emulsions [2,5,8,12,13]. However, there is evidence that low noise airborne SAR instruments are also able to characterize oil slicks with single polarization SAR data.…”

Section: Introductionmentioning

confidence: 99%

Oil Slick Characterization Using a Statistical Region-Based Classifier Applied to UAVSAR Data

Genovez

Jones

Sant’Anna

et al. 2019

JMSE

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During emergency responses to oil spills on the sea surface, quick detection and characterization of an oil slick is essential. The use of Synthetic Aperture Radar (SAR) in general and polarimetric SAR (PolSAR) in particular to detect and discriminate mineral oils from look-alikes is known. However, research exploring its potential to detect oil slick characteristics, e.g., thickness variations, is relatively new. Here a Multi-Source Image Processing System capable of processing optical, SAR and PolSAR data with proper statistical models was tested for the first time for oil slick characterization. An oil seep detected by NASA`s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in the Gulf of Mexico was used as a study case. This classifier uses a supervised approach to compare stochastic distances between different statistical distributions (fx) and hypothesis tests to associate confidence levels to the classification results. The classifier was able to detect zoning regions within the slick with high global accuracies and low uncertainties. Two different classes, likely associated with the thicker and thinner oil layers, were recognized. The best results, statistically equivalent, were obtained using different data formats: polarimetric, intensity pair and intensity single-channel. The presence of oceanic features in the form of oceanic fronts and internal waves created convergence zones that defined the shape, spreading and concentration of the thickest layers of oil. The statistical classifier was able to detect the thicker oil layers accumulated along these features. Identification of the relative thickness of spilled oils can increase the oil recovery efficiency, allowing better positioning of barriers and skimmers over the thickest layers. Decision makers can use this information to guide aerial surveillance, in situ oil samples collection and clean-up operations in order to minimize environmental impacts.

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Characterization and discrimination of evolving mineral and plant oil slicks based on L-band synthetic aperture radar (SAR)

Cited by 8 publications

References 18 publications

Oil-Spill-Response-Oriented Information Products Derived From a Rapid-Repeat Time Series of SAR Images

Oil-Spill-Response-Oriented Information Products Derived From a Rapid-Repeat Time Series of SAR Images

Retrieval of Marine Surface Slick Dielectric Properties From Radarsat-2 Data via a Polarimetric Two-Scale Model

Oil Slick Characterization Using a Statistical Region-Based Classifier Applied to UAVSAR Data

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