Abstract
We classify low-backscatter regions observed in Synthetic Aperture Radar (SAR) measurements of the surface of the ocean as either oil slicks or look-alike slicks (radar false targets). Our proposed classification algorithm is based on Linear Discriminant Analyses (LDAs) of RADARSAT-1 measurements (402 scenes off the southeast coast of Brazil from July 2001 to June 2003) and Meteorological-Oceanographic (MetOc) data from other earth observation sensors: Advanced Very High Resolution Radiometer (AVHRR), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Quick Scatterometer (QuikSCAT). Oil slicks are sea-surface expressions of exploration and production oil, ship- and orphan-spills. False targets are associated with environmental phenomena, such as biogenic films, algal blooms, upwelling, low wind, or rain cells. Both categories have been interpreted by domain-experts: mineral oil (n = 350; 45.5%) and petroleum free (n = 419; 54.5%). We explore nine size variables (area, perimeter, etc.) and three types of MetOc information (sea surface temperature, chlorophyll-a, and wind speed) that describe the 769 samples analyzed. Seven attribute–domain combinations are tested with three non-linear transformations (none, cube root, log10), with and without MetOc, adding to 39 attribute subdivisions. Classification accuracies are independent of data transformation and improve when selected size attributes are combined with MetOc, leading to overall accuracies of ~80% and sound levels of sensitivity (~90%), specificity (~80%), positive (~80%) and negative (~90%) predictive values. The effectiveness of this data-driven attempt supports further commercial or academic implementation of our LDA algorithm.
Highlights
The presence and development of oil and gas exploration and production in open oceanic waters of Brazil has led to many environmental oil-related incidents over time, and two major episodes have occurred since the eve of the current millennium
To further address the issues revealed in the automated Linear Discriminant Analyses (LDAs) seep-spill discrimination, in this current paper we focus on investigating the application of such classical, linear, multivariate data analysis technique to tell apart oil slicks and look-alikes
The discrimination of two categories of low-backscatter regions derived from Synthetic Aperture Radar (SAR) measurements has been demonstrated
Summary
The presence and development of oil and gas exploration and production in open oceanic waters of Brazil has led to many environmental oil-related incidents over time, and two major episodes have occurred since the eve of the current millennium. In 2001, the world’s largest floating offshore oilrig at the time (P-36) sank in Brazilian waters, and the many tonnes of crude oil it had on board were spilled into the sea [1]. The importance of timely and strategic environmental response efforts highlights the need for improved remote sensing surveillance methods capable of correctly identifying petroleum pollution on the surface of the ocean. Improved remote sensing methods of differentiating mineral oil slicks (sea-surface footprint of natural oil seeps or anthropogenic oil spills) from other possible petroleum-free false targets (often referred to as “slick look-alikes” or “slick-alikes”) are a constant and pressing need for effectively guiding countermeasures to combat oil pollution in our oceans
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