Detection of Floating Oil Anomalies From the Deepwater Horizon Oil Spill With Synthetic Aperture Radar

Garcia-Pineda, Oscar; MacDonald, Ian R.; Hu, Chuanmin; Svejkovsky, Jan; Hess, Mark; Dukhovskoy, Dmitry S.; Morey, Steven L.

doi:10.5670/oceanog.2013.38

Cited by 107 publications

(71 citation statements)

References 25 publications

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“…To distinguish better between deposition from the intrusion layers and deposition from the sea surface, we used hopane's distribution to anchor analysis of two other datasets: the footprint for surface oil slicks as observed by remote sensing during the time of discharge (29) and the spatial distribution of several volatile hydrocarbons present at contaminated locations. These datasets have the potential to discriminate between two possible modes of deposition: mode 1, sedimentation of oil that was released from the Macondo Well and trapped in the ocean's interior without ever being exposed to the atmosphere; and mode 2, sedimentation of Macondo oil from the sea surface to the sea floor [the sinking of oil-laden particles from the sea surface popularly referred to as "the dirty blizzard" (16), by analogy with natural marine snow].…”

Section: Significancementioning

confidence: 99%

Fallout plume of submerged oil from Deepwater Horizon

Valentine

Fisher

Bagby

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

235

204

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Significance Following the sinking of the Deepwater Horizon in the Gulf of Mexico an unprecedented quantity of oil irrupted into the ocean at a depth of 1.5 km. The novelty of this event makes the oil’s subsequent fate in the deep ocean difficult to predict. This work identifies a fallout plume of hydrocarbons from the Macondo Well contaminating the ocean floor over an area of 3,200 km 2 . Our analysis suggests the oil initially was suspended in deep waters and then settled to the underlying sea floor. The spatial distribution of contamination implicates accelerated settling as an important fate for suspended oil, supports a patchwork mosaic model of oil deposition, and frames ongoing attempts to determine the event’s impact on deep-ocean ecology.

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Section: Significancementioning

confidence: 99%

Fallout plume of submerged oil from Deepwater Horizon

Valentine

Fisher

Bagby

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

235

204

View full text Add to dashboard Cite

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“…For example, Minchew et al [23,24] detected oil emulsion mixtures using UAVSAR. Garcia-Pineda et al [13] applied a similar analysis to SAR satellite data to detect emulsified oil on the ocean surface.…”

Section: Sar Overviewmentioning

confidence: 99%

“…2017, 9, 567 5 of 19 [23,24] detected oil emulsion mixtures using UAVSAR. Garcia-Pineda et al [13] applied a similar analysis to SAR satellite data to detect emulsified oil on the ocean surface. The example shown in Figure 2 is a sequence of UAVSAR collected on 23 June 2010 ( Figure 2A) followed by RADARSAT-1 ( Figure 2C) collected over the same area on two days after on June 25.…”

Section: Sar Overviewmentioning

confidence: 99%

“…Garcia-Pineda et al [7] developed a semi-automated SAR oil delineation algorithm called the Textural Classifier Neural Network Algorithm (TCNNA). Garcia-Pineda et al [12,13] further refined the algorithm to process SAR imagery collected during the DWH event to identify not only presence/absence of oil, but to identify oil emulsions that are detectable in SAR imagery. TCNNA is semi-automated because it can still be subject to false positive oil detections; thus, the final delineation of oil in SAR imagery requires a detailed inspection from a trained SAR image interpreter.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Oil near Shorelines during the Deepwater Horizon Oil Spill Using Synthetic Aperture Radar (SAR)

et al. 2017

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During any marine oil spill, floating oil slicks that reach shorelines threaten a wide array of coastal habitats. To assess the presence of oil near shorelines during the Deepwater Horizon (DWH) oil spill, we scanned the library of Synthetic Aperture Radar (SAR) imagery collected during the event to determine which images intersected shorelines and appeared to contain oil. In total, 715 SAR images taken during the DWH spill were analyzed and processed, with 188 of the images clearly showing oil. Of these, 156 SAR images showed oil within 10 km of the shoreline with appropriate weather conditions for the detection of oil on SAR data. We found detectable oil in SAR images within 10 km of the shoreline from west Louisiana to west Florida, including near beaches, marshes, and islands. The high number of SAR images collected in Barataria Bay, Louisiana in 2010 allowed for the creation of a nearshore oiling persistence map. This analysis shows that, in some areas inside Barataria Bay, floating oil was detected on as many as 29 different days in 2010. The nearshore areas with persistent floating oil corresponded well with areas where ground survey crews discovered heavy shoreline oiling. We conclude that satellite-based SAR imagery can detect oil slicks near shorelines, even in sheltered areas. These data can help assess potential shoreline oil exposure without requiring boats or aircraft. This method can be particularly helpful when shoreline assessment crews are hampered by difficult access or, in the case of DWH, a particularly large spatial and temporal spill extent.

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“…The Deepwater Horizon oil spill is the largest offshore discharge disaster in history (Garcia-Pineda, 2013). It was caused on April 20 th 2010, by an explosion in the Gulf of Mexico, located at 28.74˚N/88.39˚W (figure 1).…”

Section: Research Area and Materialsmentioning

confidence: 99%

Large Oil Spill Classification Using Sar Images Based on Spatial Histogram

Schvartzman

Havivi

Maman

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VIII, WG VIII/9KEY WORDS: Non-linear filter, Unsupervised Automatic Classification, Natural Hazards ABSTRACT:Among the different types of marine pollution, oil spill is a major threat to the sea ecosystems. Remote sensing is used in oil spill response. Synthetic Aperture Radar (SAR) is an active microwave sensor that operates under all weather conditions and provides information about the surface roughness and covers large areas at a high spatial resolution. SAR is widely used to identify and track pollutants in the sea, which may be due to a secondary effect of a large natural disaster or by a man-made one . The detection of oil spill in SAR imagery relies on the decrease of the backscattering from the sea surface, due to the increased viscosity, resulting in a dark formation that contrasts with the brightness of the surrounding area. Most of the use of SAR images for oil spill detection is done by visual interpretation. Trained interpreters scan the image, and mark areas of low backscatter and where shape is a-symmetrical. It is very difficult to apply this method for a wide area. In contrast to visual interpretation, automatic detection algorithms were suggested and are mainly based on scanning dark formations, extracting features, and applying big data analysis. We propose a new algorithm that applies a nonlinear spatial filter that detects dark formations and is not susceptible to noises, such as internal or speckle. The advantages of this algorithm are both in run time and the results retrieved. The algorithm was tested in genesimulations as well as on COSMO-SkyMed images, detecting the Deep Horizon oil spill in the Gulf of Mexico (occurred on 20/4/2010). The simulation results show that even in a noisy environment, oil spill is detected. Applying the algorithm to the Deep Horizon oil spill, the algorithm classified the oil spill better than focusing on dark formation algorithm. Furthermore, the results were validated by the National Oceanic and Atmospheric Administration (NOAA) data.

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Detection of Floating Oil Anomalies From the Deepwater Horizon Oil Spill With Synthetic Aperture Radar

Abstract: S p e c i a l i S S u e O N O c e a N R e m O t e S e N S i N g w i t h S y N t h e t i c a p e R t u R e R a d a

Cited by 107 publications

References 25 publications

Fallout plume of submerged oil from Deepwater Horizon

Fallout plume of submerged oil from Deepwater Horizon

Detection of Oil near Shorelines during the Deepwater Horizon Oil Spill Using Synthetic Aperture Radar (SAR)

Large Oil Spill Classification Using Sar Images Based on Spatial Histogram

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