Oil source-fingerprinting in support of polarimetric radar mapping of Macondo-252 oil in Gulf Coast marshes

Ramsey, Elijah W.; Meyer, Buffy M.; Rangoonwala, Amina; Overton, Edward B.; Jones, Cathleen E.; Bannister, Terri

doi:10.1016/j.marpolbul.2014.10.032

Cited by 25 publications

(17 citation statements)

References 22 publications

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“…Although the high changes in marsh structure were outside the core impact area, moderate increases in LAI also occurred in core oil impact marshes. Shoreline oiling and concurrent backscatter change were confirmed, as was the presence of low concentrations of oil in core interior marshes at the time of the documented backscatter change [81]. However, the causal mechanism of the backscatter change in the core interior marshes located straight inland of the oiled shorelines was not determined.…”

Section: Applying the Regression Models To Map Marsh Canopy Lai And Ladmentioning

confidence: 96%

Structural Classification of Marshes with Polarimetric SAR Highlighting the Temporal Mapping of Marshes Exposed to Oil

2015

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Abstract:Empirical relationships between field-derived Leaf Area Index (LAI) and Leaf Angle Distribution (LAD) and polarimetric synthetic aperture radar (PolSAR) based biophysical indicators were created and applied to map S. alterniflora marsh canopy structure. PolSAR and field data were collected near concurrently in the summers of 2010, 2011, and 2012 in coastal marshes, and PolSAR data alone were acquired in 2009. Regression analyses showed that LAI correspondence with the PolSAR biophysical indicator variables equaled or exceeded those of vegetation water content (VWC) correspondences. In the final six regressor model, the ratio HV/VV explained 49% of the total 77% explained LAI variance, and the HH-VV coherence and phase information accounted for the remainder. HV/HH dominated the two regressor LAD relationship, and spatial heterogeneity and backscatter mechanism followed by coherence information dominated the final three regressor model that explained 74% of the LAD variance. Regression results applied to 2009 through 2012 PolSAR images showed substantial changes in marsh LAI and LAD. Although the direct cause was not substantiated, following a release of freshwater in response to the 2010 Deepwater Horizon oil spill, the fairly uniform interior marsh structure of 2009 was more vertical and dense shortly after the oil spill cessation. After 2010, marsh structure generally progressed back toward the 2009 uniformity; however, the trend was more disjointed in oil impact marshes.

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Section: Applying the Regression Models To Map Marsh Canopy Lai And Ladmentioning

confidence: 96%

Structural Classification of Marshes with Polarimetric SAR Highlighting the Temporal Mapping of Marshes Exposed to Oil

2015

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“…The DWH spill started on 20 April 2010, and oil was transported into northern Barataria Bay first in mid‐May 2010 and again in early June, prior to the 2010 UAVSAR acquisition [ Ramsey et al ., ]. Spectral and oil fingerprinting analyses confirmed that oil persisted on the shorelines for at least a year [ Ramsey et al ., ; Peterson et al ., ].…”

Section: Data and Study Areamentioning

confidence: 99%

“…Upper Barataria Bay, the site of the heaviest Deepwater Horizon (DWH) oiling, had widespread oiling along many of its marsh margins (Figure ). Oiling of vegetation and soil has been documented previously along shorelines and in some inshore locations in this area [ Kokaly et al ., ; Ramsey et al ., ; Peterson et al ., ], and some of the oiled shorelines were later subject to remediation treatment [ Zengel and Michel , ; Zengel et al ., ]. Because shoreline stability directly influences the adjacent interior marsh status [ Knutson , ], the widespread and often severe impact of oil on the near‐shore marshes in southeastern Louisiana raised concern that oil contamination and the subsequent remediation activities could intensify shoreline retreat and marsh degradation and loss [ Pezeshki and DeLaune , ].…”

Section: Introductionmentioning

confidence: 99%

Wetland shoreline recession in the Mississippi River Delta from petroleum oiling and cyclonic storms

Rangoonwala

Jones

Ramsey

2016

Geophysical Research Letters

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We evaluate the relative impact of petroleum spill and storm surge on near‐shore wetland loss by quantifying the lateral movement of coastal shores in upper Barataria Bay, Louisiana (USA), between June 2009 and October 2012, a study period that extends from the year prior to the Deepwater Horizon spill to 2.5 years following the spill. We document a distinctly different pattern of shoreline loss in the 2 years following the spill, both from that observed in the year prior to the spill, during which there was no major cyclonic storm, and from change related to Hurricane Isaac, which made landfall in August 2012. Shoreline erosion following oiling was far more spatially extensive and included loss in areas protected from wave‐induced erosion. We conclude that petroleum exposure can substantially increase shoreline recession particularly in areas protected from storm‐induced degradation and disproportionally alters small oil‐exposed barrier islands relative to natural erosion.

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“…An aliquot of 1 mL of each extract was subjected to analysis. Saturated hydrocarbon fractions were eluted with nhexane, and then analyzed by gas chromatograph-mass spectrometer (GC-MS) (Mello et al, 2012;Di Gregorio et al, 2014;Ramsey et al, 2014;Adhikari et al, 2015).…”

Section: Extraction and Instrumental Analysismentioning

confidence: 99%

“…A reliable estimate of the standard deviation is required to calculate appropriate repeatability limits. The repeatability limit (r) is reported as , which gives the likely limits within which 95% of measurements should occur (Riley et al, 1980;Ramsey et al, 2014). In the present study, it can be considered that these two ratios are parallel ratios if the absolute difference between any two diagnostic ratios of oil fingerprinting is less than r. The evaluation results are "Y", meaning that these diagnostic ratios are fit for oil fingerprints identification, when the absolute difference between maxima and minima values of diagnostic is smaller than r. The evaluation results are "N", meaning these diagnostic ratios not suitable, when the absolute difference between Maxima and minima values is larger than repeatability.…”

Section: Variation Of Polycyclic Aromatic Hydrocarbonsmentioning

confidence: 99%

Evaluation of diagnostic ratios of medium and serious weathered oils from five different oil sources

Wang

et al. 2016

Acta Oceanol. Sin.

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Laboratory experiments were conducted to simulate oil weathering process, a medium to long term weathering process for 210-d, using samples collected from five different oil resources. Based on relative deviation and repeatability limit analysis about indexes of these samples, the results show there had been significant changes in diagnostic ratios among the initial and weathered samples of different oils during this process. Changes of selected n-alkane diagnostic ratios of all oil samples displayed more obviously than diagnostic ratios of terpanes, steranes and PAHs in this process. Almost all selected diagnostic ratios of terpanes, steranes and PAHs can be efficiently used in tracking sources of hydrocarbon pollution, differentiating from the n-alkane diagnostic ratios. In these efficient diagnostic ratios, only four ratios maintained good stability in the weathering processes and are more suitable because their relative deviation (RSD) are lower than 5%.Key words: identification of spilled oils, medium to long term weathering process, simulation experiment, biomarker ratios Citation: He Shijie, Wang Chuanyuan, Li Yantai, Yu Hongjun, Han Bin. 2016. Evaluation of diagnostic ratios of medium and serious weathered oils from five different oil sources. Acta Oceanologica Sinica, 35(4): 1-8,

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Oil source-fingerprinting in support of polarimetric radar mapping of Macondo-252 oil in Gulf Coast marshes

Cited by 25 publications

References 22 publications

Structural Classification of Marshes with Polarimetric SAR Highlighting the Temporal Mapping of Marshes Exposed to Oil

Structural Classification of Marshes with Polarimetric SAR Highlighting the Temporal Mapping of Marshes Exposed to Oil

Wetland shoreline recession in the Mississippi River Delta from petroleum oiling and cyclonic storms

Evaluation of diagnostic ratios of medium and serious weathered oils from five different oil sources

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