Modulation of the Spectra of Short Gravity Waves by Sea Surface Films: Slick Detection and Characterization with a Microwave Probe

Lombardini, P. P.; Fiscella, B.; Trivero, P.; Cappa, Christopher D.; Garrett, William D.

doi:10.1175/1520-0426(1989)006<0882:motsos>2.0.co;2

Cited by 107 publications

(61 citation statements)

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“…There are many methods developed to detect oil spills from SAR intensity images: (1) machine learning and neural-network recognition (Kubat et al, 1998, Ozkan and Sunar, 2007, Ozkan et al, 2013, (2) frequency spectrum attenuation (Lombardini et al, 1989, Gade et al, 1998, Kim et al, 2010, (3) segmentation techniques (Barni et al, 1995, Solberg et al, 2007, and (4) slick feature extraction (Fiscella et al, 2000, Del Frate et al, 2000. Some algorithms also combine ocean drift models to assist data analysis (Espedal, 1999, Cheng et al, 2011.…”

Section: Methodsmentioning

confidence: 99%

Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

Osmanoǧlu

Özkan

Sunar

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12,000 to 15,000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C-and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

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Section: Methodsmentioning

confidence: 99%

Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

Osmanoǧlu

Özkan

Sunar

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…We will focus on the oil from a layer up the sea surface (contaminated sea surface). Then we present its influence on the ocean spectrum using a damping model [18], and we compared the obtained results with the representation of clean sea surface spectrum.…”

Section: ( )mentioning

confidence: 99%

“…This paper is organized as follows: in the first part, we present the impact of the oil spills on the surface height spectrum of a rough sea surface. To characterize the clean sea surface, the model of Elfouhaily et al [6] is used, and for the contaminated sea surface we use the Lombardini et al model [18]. In the second part, we discuss the calculation method of electromagnetic scattering coefficients (FBM).…”

Section: Introductionmentioning

confidence: 99%

Numerical Modeling of Electromagnetic Scattering from Sea Surface Covered by Oil

Ghanmi¹,

Khenchaf²,

Comblet³

2014

JEMAA

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The aim of this work is to study the impacts of the oil spills on the electromagnetic scattering of the ocean surfaces in bistatic and monostatic configurations. Therefore, in this paper, we will study the influence of the pollutants (oil spills) on the physical and geometrical properties of sea surface. In recent literature, the study of the electromagnetic scattering from contaminated sea surface (sea surface covered by oil spill) was limited in monostatic case. In this paper, we will study this effect in bistatic configuration, which is interested in presence of pollution in sea surface. Indeed, we will start the numerical analysis of the bistatic scattering coefficients of a clean sea surface. Then, we will study the electromagnetic signature from sea surface covered by oil spills in bistatic case using the numerical Forward-Backward Method (FBM). The obtained numerical simulation of bistatic scattering coefficients of clean and contaminated sea surface is studied as a function of various parameters (frequency, incident angle, sea state, type of pollutant…). And the obtained results are also compared with those published in the literature, including those using asymptotic methods.

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“…The theoretical analysis, based upon the Navier-Stokes equation and developed for the case of small ripples on an interface covered by a surface-active substance, has been extended by with a formalism which includes both soluble and insoluble monomolecular films for the two coexisting modal solutions: the Laplace or transversal mode and the Marangoni or longitudinal mode , Fiscella et al 1985a). According to Lombardini et al (1989), the analytical form which describes the ratio between real parts of the complex radian frequencies on pure water to that for water covered by slick (damping ratio) can be given by the semi -empirical formula:…”

Section: Water Surface Slicksmentioning

confidence: 99%

Observing Marine Pollution with Synthetic Aperture Radar

Trivero¹,

Biamino²

2010

Geoscience and Remote Sensing New Achievements

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Modulation of the Spectra of Short Gravity Waves by Sea Surface Films: Slick Detection and Characterization with a Microwave Probe

Cited by 107 publications

References 0 publications

Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

Numerical Modeling of Electromagnetic Scattering from Sea Surface Covered by Oil

Observing Marine Pollution with Synthetic Aperture Radar

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