Distinguishing Mineral Oil Slicks from Low Wind Areas using Rapid Repeat Synthetic Aperture Radar Imagery
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https://hdl.handle.net/10037/33313Date
2024-03-18Type
Journal articleTidsskriftartikkel
Peer reviewed
Author
Quigley, Cornelius Patrick; Johansson, Anna Malin Kristin; Jones, Cathleen Elaine; Holt, BenjaminAbstract
A method for differentiating marine oil slicks from radar-dark, low wind areas in open water using rapid repeat SAR imagery is reported. The study uses data acquired by the airborne UAVSAR L-band SAR instrument, imaging the Coal Oil Point seep field near Santa Barbara, California. Time-series of images from three different days are analyzed, all containing both verified oil slicks and low wind zones. We propose a method to derive high confidence oil/open water maps by exploiting the differences in spatial and temporal evolution between the low wind zones and oil slicks over time scales of ∼1-3.5 hours. Our method uses the standard deviation of the backscatter intensity for ensembles of co-located SAR pixels and is sufficiently simple and generic to be applied in near-real-time and without special processing code. The derived maps are compared to images of the ocean surface obtained by cameras mounted on a boat surveying the seep field simultaneously with the SAR. The imagery is manually classified into 1) confirmed oil, 2) likely oil, and 3) open water classes. Our results show ∼1 – 7 dB difference between the SAR-derived mean standard deviation values of the confirmed/likely oil classes compared to the open water class. The minimum number of scenes needed to distinguish between areas of high likelihood of open water and oil slick was determined to be 3 – 5 scenes, spanning 50 – 80 minutes, depending on the spatial extent and persistence of the low wind zones in the imagery.
Publisher
IEEECitation
Quigley, Johansson, Jones, Holt. Distinguishing Mineral Oil Slicks from Low Wind Areas using Rapid Repeat Synthetic Aperture Radar Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2024Metadata
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