Abstract
Our research focuses on refining the ability to discriminate two petrogenic oil-slick categories: the sea surface expression of naturally-occurring oil seeps andman-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 ofMexico (Campeche Bay,Mexico). As the scientific literature on the use of satellite-derivedmeasurements 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.
| Original language | English (US) |
|---|---|
| Article number | 153 |
| Journal | Journal of Marine Science and Engineering |
| Volume | 6 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 11 2018 |
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Keywords
- Campeche Bay
- Exploratory data analysis
- Gulf of Mexico
- Man-made oil spills
- Naturally-occurring oil seeps
- Oil-slick discrimination algorithm
- Petrogenic oil-slick category
- RADARSAT
- Remote sensing
- Synthetic aperture radar
ASJC Scopus subject areas
- Civil and Structural Engineering
- Water Science and Technology
- Ocean Engineering
Cite this
Refined analysis of RADARSAT-2 measurements to discriminate two petrogenic oil-slick categories : Seeps versus spills. / Carvalho, Gustavo de Araújo; Minnett, Peter J.; Paes, Eduardo Tavares; de Miranda, Fernando Pellon; Landau, Luiz.
In: Journal of Marine Science and Engineering, Vol. 6, No. 4, 153, 11.12.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Refined analysis of RADARSAT-2 measurements to discriminate two petrogenic oil-slick categories
T2 - Seeps versus spills
AU - Carvalho, Gustavo de Araújo
AU - Minnett, Peter J.
AU - Paes, Eduardo Tavares
AU - de Miranda, Fernando Pellon
AU - Landau, Luiz
PY - 2018/12/11
Y1 - 2018/12/11
N2 - Our research focuses on refining the ability to discriminate two petrogenic oil-slick categories: the sea surface expression of naturally-occurring oil seeps andman-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 ofMexico (Campeche Bay,Mexico). As the scientific literature on the use of satellite-derivedmeasurements 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.
AB - Our research focuses on refining the ability to discriminate two petrogenic oil-slick categories: the sea surface expression of naturally-occurring oil seeps andman-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 ofMexico (Campeche Bay,Mexico). As the scientific literature on the use of satellite-derivedmeasurements 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.
KW - Campeche Bay
KW - Exploratory data analysis
KW - Gulf of Mexico
KW - Man-made oil spills
KW - Naturally-occurring oil seeps
KW - Oil-slick discrimination algorithm
KW - Petrogenic oil-slick category
KW - RADARSAT
KW - Remote sensing
KW - Synthetic aperture radar
UR - http://www.scopus.com/inward/record.url?scp=85058824219&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058824219&partnerID=8YFLogxK
U2 - 10.3390/jmse6040153
DO - 10.3390/jmse6040153
M3 - Article
AN - SCOPUS:85058824219
VL - 6
JO - Journal of Marine Science and Engineering
JF - Journal of Marine Science and Engineering
SN - 2077-1312
IS - 4
M1 - 153
ER -