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J. Mar. Sci. Eng. 2018, 6(4), 153; https://doi.org/10.3390/jmse6040153

Refined Analysis of RADARSAT-2 Measurements to Discriminate Two Petrogenic Oil-Slick Categories: Seeps versus Spills

1
LabSAR—Laboratório de Sensoriamento Remoto por Radar Aplicado à Indústria do Petróleo, LAMCE—Laboratório de Métodos Computacionais em Engenharia, PEC—Programa de Engenharia Civil, COPPE—Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia, UFRJ—Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil
2
OCE—Department of Ocean Sciences, RSMAS—Rosenstiel School of Marine and Atmospheric Science, UM—University of Miami, Miami, FL 33145, USA
3
LEMOPA—Laboratório de Ecologia Marinha e Oceanografia Pesqueira da Amazônia, ISARH—Instituto Socioambiental e dos Recursos Hídricos, UFRA—Universidade Federal Rural da Amazônia, Belém 66077-830, Brazil
*
Author to whom correspondence should be addressed.
Received: 1 November 2018 / Revised: 29 November 2018 / Accepted: 30 November 2018 / Published: 11 December 2018
(This article belongs to the Special Issue Marine Oil Spills 2018)
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Abstract

Our research focuses on refining the ability to discriminate two petrogenic oil-slick categories: the sea surface expression of naturally-occurring oil seeps and man-made oil spills. For that, a long-term RADARSAT-2 dataset (244 scenes imaged between 2008 and 2012) is analyzed to investigate oil slicks (4562) observed in the Gulf of Mexico (Campeche Bay, Mexico). As the scientific literature on the use of satellite-derived measurements to discriminate the oil-slick category is sparse, our research addresses this gap by extending our previous investigations aimed at discriminating seeps from spills. To reveal hidden traits of the available satellite information and to evaluate an existing Oil-Slick Discrimination Algorithm, distinct processing segments methodically inspect the data at several levels: input data repository, data transformation, attribute selection, and multivariate data analysis. Different attribute selection strategies similarly excel at the seep-spill differentiation. The combination of different Oil-Slick Information Descriptors presents comparable discrimination accuracies. Among 8 non-linear transformations, the Logarithm and Cube Root normalizations disclose the most effective discrimination power of almost 70%. Our refined analysis corroborates and consolidates our earlier findings, providing a firmer basis and useful accuracies of the seep-spill discrimination practice using information acquired with space-borne surveillance systems based on Synthetic Aperture Radars. View Full-Text
Keywords: oil-slick discrimination algorithm; petrogenic oil-slick category; naturally-occurring oil seeps; man-made oil spills; exploratory data analysis; remote sensing; synthetic aperture radar; RADARSAT; Gulf of Mexico; Campeche Bay oil-slick discrimination algorithm; petrogenic oil-slick category; naturally-occurring oil seeps; man-made oil spills; exploratory data analysis; remote sensing; synthetic aperture radar; RADARSAT; Gulf of Mexico; Campeche Bay
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Carvalho, G.A.; Minnett, P.J.; Paes, E.T.; De Miranda, F.P.; Landau, L. Refined Analysis of RADARSAT-2 Measurements to Discriminate Two Petrogenic Oil-Slick Categories: Seeps versus Spills. J. Mar. Sci. Eng. 2018, 6, 153.

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