Exploratory Data Analysis of Synthetic Aperture Radar (SAR) Measurements to Distinguish the Sea Surface Expressions of Naturally-Occurring Oil Seeps from Human-Related Oil Spills in Campeche Bay (Gulf of Mexico)

ISPRS International Journal of Geo-Information

Peter J. Minnett¹  Eduardo Tavares Paes²  Gustavo de Araújo Carvalho³  Fernando Pellon de Miranda³  Luiz Landau³ 

[1] Department of Ocean Sciences (OCE), Rosenstiel School of Marine and Atmospheric Science (RSMAS), University of Miami (UM), Miami, FL 33149, USA;Laboratório de Ecologia Marinha e Oceanografia Pesqueira da Amazônia (LEMOPA), Instituto Socioambiental e dos Recursos Hídricos (ISARH), Universidade Federal Rural da Amazônia (UFRA), Belém, PA 66077-830, Brazil;Programa de Engenharia Civil (PEC), Laboratório de Métodos Computacionais em Engenharia (LAMCE), Laboratório de Sensoriamento Remoto por Radar Aplicado à Indústria do Petróleo (LabSAR), Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ 21941-909, Brazil;

关键词: Exploratory Data Analysis (EDA);    sea surface monitoring;    oil slick type differentiation;    oil seep;    oil spill;    remote sensing;    Synthetic Aperture Radar (SAR);    RADARSAT-2;   

DOI  :  10.3390/ijgi6120379

来源: DOAJ

【 摘 要 】

An Exploratory Data Analysis (EDA) aims to use Synthetic Aperture Radar (SAR) measurements for discriminating between two oil slick types observed on the sea surface: naturally-occurring oil seeps versus human-related oil spills—the use of satellite sensors for this task is poorly documented in scientific literature. A long-term RADARSAT dataset (2008–2012) is exploited to investigate oil slicks in Campeche Bay (Gulf of Mexico). Simple Classification Algorithms to distinguish the oil slick type are designed based on standard multivariate data analysis techniques. Various attributes of geometry, shape, and dimension that describe the oil slick Size Information are combined with SAR-derived backscatter coefficients—sigma-(σo), beta-(βo), and gamma-(γo) naught. The combination of several of these characteristics is capable of distinguishing the oil slick type with ~70% of overall accuracy, however, the sole and simple use of two specific oil slick’s Size Information (i.e., area and perimeter) is equally capable of distinguishing seeps from spills. The data mining exercise of our EDA promotes a novel idea bridging petroleum pollution and remote sensing research, thus paving the way to further investigate the satellite synoptic view to express geophysical differences between seeped and spilled oil observed on the sea surface for systematic use.

【 授权许可】

Unknown