Satellite Environmental Monitoring of Oil Spills in the South Gulf Of Mexico

Miranda, Fernando; Bannerman, K.; Pedroso, Enrico Campos; Lopez, O.; Herrera, Miguel

doi:10.4043/16410-ms

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…In this sense, we resorted to the satellite-field data put together by Pemex (Petróleos Mexicanos; Mexico City, Mexico) that carried out a decadal (2000-2012) environmental monitoring program in Campeche Bay (Figure 1). This database came from the dire necessity to survey oil slicks in the surroundings of its numerous fossil fuel facilities in this region [18,[46][47][48][49]. The entire satellite database has 766 images from the Canadian C-band SAR satellites: RADARSAT-1 (482; 63%) and RADARSAT-2 (284; 37%); all scenes of the former are 8-bit HH polarized, whereas most of the latter are 16-bit VV polarized [50,51].…”

Section: Datasetmentioning

confidence: 99%

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

et al. 2019

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A novel empirical approach to categorize oil slicks’ sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear discriminant analysis (LDA) to try accuracy improvements from our previously published methods of discriminating seeps from spills that achieved ~70% of overall accuracy. Analyzing 244 RADARSAT-2 scenes containing 4562 slicks observed in Campeche Bay (Gulf of Mexico), our exploratory data analysis evaluates the impact of 61 combinations of SAR backscatter coefficients (σ°, β°, γ°), SAR calibrated products (received radar beam given in amplitude or decibel, with or without a despeckle filter), and data transformations (none, cube root, log10). The LDA ability to discriminate the oil-slick category is rather independent of backscatter coefficients and calibrated products, but influenced by data transformations. The combination of attributes plays a role in the discrimination; combining oil-slicks’ size and SAR information is more effective. We have simplified our analyses using fewer attributes to reach accuracies comparable to those of our earlier studies, and we suggest using other multivariate data analyses—cubist or random forest—to attempt to further improve oil-slick category discrimination.

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Section: Datasetmentioning

confidence: 99%

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

et al. 2019

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“…Nevertheless, at the beginning of 2009, said prices fell substantially, which indicates that the price does not depend exclusively on the availability of petroleum, but also on multiple financial and geopolitical interests [5]. Over the potential reserves (possible and probable), the exploration in various parts of the world [5,6] and the application of new technologies [7][8][9] shows that it will be possible to extract petroleum at competitive costs for at least all of the 21st century.…”

Section: Introductionmentioning

confidence: 99%

Does Biodiesel from Jatropha Curcas Represent a Sustainable Alternative Energy Source?

et al. 2009

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Abstract:Various government agencies around the world have proposed vegetable oils and their conversion to biodiesel as a renewable alternative to fossil fuels. Due to its adaptability to marginal soils and environments, the cultivation of Jatropha curcas is frequently mentioned as the best option for producing biodiesel. In the present work the current situation of proven and potential reserves of fossil fuel, and the production and consumption model for the same are analyzed, in order to later review the sustainability of the production process which begins with the cultivation of J. curcas, and culminates with the consumption of biodiesel. A review of the following topics is proposed in order to improve the sustainability of the process: areas destined for cultivation, use of external (chemical) inputs in cultivation, processes for converting the vegetable oil to biodiesel, and, above all, the location for ultimate consumption of the biofuel.

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“…Because the Digital Mega-Collection Database is an important part of our research, this section is dedicated to describing this repository. Besides including ~15 thousand petroleum signatures observed on the surface of the ocean with ~3 thousand SAR images, the mega collection also includes related technical reports, conference presentations (e.g., [4,5]), M.Sc. dissertations (e.g., [6,7]), Ph.D. theses (e.g., [8,9]), and scientific papers (e.g., [10,11]).…”

Section: Digital Mega-collection Databasementioning

confidence: 99%

Computational Oil-Slick Hub for Offshore Petroleum Studies

Ebecken

Miranda

Landau

et al. 2023

JMSE

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The paper introduces the Oil-Slick Hub (OSH), a computational platform to facilitate the data visualization of a large database of petroleum signatures observed on the surface of the ocean with synthetic aperture radar (SAR) measurements. This Internet platform offers an information search and retrieval system of a database resulting from >20 years of scientific projects that interpreted ~15 thousand offshore mineral oil “slicks”: natural oil “seeps” versus operational oil “spills”. Such a Digital Mega-Collection Database consists of satellite images and oil-slick polygons identified in the Gulf of Mexico (GMex) and the Brazilian Continental Margin (BCM). A series of attributes describing the interpreted slicks are also included, along with technical reports and scientific papers. Two experiments illustrate the use of the OSH to facilitate the selection of data subsets from the mega collection (GMex variables and BCM samples), in which artificial intelligence techniques—machine learning (ML)—classify slicks into seeps or spills. The GMex variable dataset was analyzed with simple linear discriminant analyses (LDAs), and a three-fold accuracy performance pattern was observed: (i) the least accurate subset (~65%) solely used acquisition aspects (e.g., acquisition beam mode, date, and time, satellite name, etc.); (ii) the best results (>90%) were achieved with the inclusion of location attributes (i.e., latitude, longitude, and bathymetry); and (iii) moderate performances (~70%) were reached using only morphological information (e.g., area, perimeter, perimeter to area ratio, etc.). The BCM sample dataset was analyzed with six traditional ML methods, namely naive Bayes (NB), K-nearest neighbors (KNN), decision trees (DT), random forests (RF), support vector machines (SVM), and artificial neural networks (ANN), and the most effective algorithms per sample subsets were: (i) RF (86.8%) for Campos, Santos, and Ceará Basins; (ii) NB (87.2%) for Campos with Santos Basins; (iii) SVM (86.9%) for Campos with Ceará Basins; and (iv) SVM (87.8%) for only Campos Basin. The OSH can assist in different concerns (general public, social, economic, political, ecological, and scientific) related to petroleum exploration and production activities, serving as an important aid in discovering new offshore exploratory frontiers, avoiding legal penalties on oil-seep events, supporting oceanic monitoring systems, and providing valuable information to environmental studies.

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Satellite Environmental Monitoring of Oil Spills in the South Gulf Of Mexico

Cited by 4 publications

References 3 publications

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

Does Biodiesel from Jatropha Curcas Represent a Sustainable Alternative Energy Source?

Computational Oil-Slick Hub for Offshore Petroleum Studies

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