Oceanography The Official Magazine of
The Oceanography Society
Volume 26 Issue 02

View Issue TOC
Volume 26, No. 2
Pages 124 - 137

OpenAccess

Detection of Floating Oil Anomalies from the Deepwater Horizon Oil Spill with Synthetic Aperture Radar

By Oscar Garcia-Pineda , Ian MacDonald , Chuanmin Hu, Jan Svejkovsky, Mark Hess , Dmitry Dukhovskoy, and Steven L. Morey 
Jump to
Article Abstract Citation References Copyright & Usage
Article Abstract

Detection of oil floating on the ocean surface, and particularly thick layers of it, is crucial for emergency response to oil spills. While detection of oil on the ocean surface is possible under moderate sea-state conditions using a variety of remote-sensing methods, estimation of oil layer thickness is technically challenging. In this paper, we used synthetic aperture radar (SAR) imagery collected during the Deepwater Horizon oil spill and the Texture Classifier Neural Network Algorithm (TCNNA) to identify the spill’s extent. We then developed an oil emulsion detection algorithm using TCNNA outputs to enhance the contrast of pixels within the oil slick in order to identify SAR image signatures that may correspond to regions of thick, emulsified oil. These locations were found to be largely consistent with ship-based observations and optical and thermal remote-sensing instrument data. The detection method identifies regions of increased radar backscattering within larger areas of oil-covered water. Detection was dependent on SAR incident angles and SAR beam mode configuration. L-band SAR was found to have the largest window of incidence angles (19–38° off nadir) useful for detecting oil emulsions. C-band SAR showed a narrower window (20–32° off nadir) than L-band, while X-band SAR had the narrowest window (20–31° off nadir). The results suggest that in case of future spills in the ocean, SAR data may be used to identify oil emulsions to help make management decisions.

Citation

Garcia-Pineda, O., I. MacDonald, C. Hu, J. Svejkovsky, M. Hess, D. Dukhovskoy, and S.L. Morey. 2013. Detection of floating oil anomalies from the Deepwater Horizon oil spill with synthetic aperture radar. Oceanography 26(2):124–137, https://doi.org/10.5670/oceanog.2013.38.

References
    Alpers, W., and H. Espedal. 2004. Oils and surfactants. Pp. 263–267 in Synthetic Aperture Radar Marine User’s Manual. C. Jackson and J. Apel, eds, US Department of Commerce, NOAA. Available online at: http://www.sarusersmanual.com/ManualPDF/NOAASARManual_CH11_pg263-276.pdf (accessed August 26, 2013).
  1. Alpers, W., and H. Hühnerfuss. 1988. Radar signatures of oil films floating on the sea surface and the Marangoni effect. Journal of Geophysical Research 93(C4):3,642–3,648, https://doi.org/​10.1029/JC093iC04p03642. 
  2. Atlas, R.M., and T.C. Hazen. 2011. Oil biodegradation and bioremediation: A tale of the two worst spills in US history. Environmental Science & Technology 45(16):6,709–6,715, https://doi.org/10.1021/es2013227.
  3. Belore, R., K. Trudel, and J. Morrison. 2011. Weathering, emulsification, and chemical dispersibility of Mississippi Canyon 252 crude oil: Field and laboratory studies. Proceedings of the International Oil Spill Conference 2011, Portland, OR. Available online at: http://ioscproceedings.org/doi/pdf/10.7901/2169-3358-2011-1-247 (accessed August 26, 2013).
  4. Brekke, C., and H. Solber. 2005. Oil spill detection by satellite remote sensing. Remote Sensing of Environment 95:1–13.
  5. Clark, R.N., G.A. Swayze, I. Leifer, K.E. Livo, S. Lundeen, M. Eastwood, R.O. Green, R. Kokaly, T. Hoefen, C. Sarture, and others. 2010. A Method for Qualitative Mapping of Thick Oil Spills Using Imaging Spectroscopy. USGS Open-File Report 2010-1101. Available online at: http://pubs.usgs.gov/of/2010/1101 (accessed August 26, 2013).
  6. Clemente-Colón, P., and X.-H. Yan. 2000. Low-backscatter ocean features in synthetic aperture radar imagery. Johns Hopkins APL Technical Digest 21(1):1–6. Available online at: http://www.jhuapl.edu/techdigest/TD/td2101/clemente.pdf (accessed August 26, 2013).
  7. De Beukelaer, S.M., I.R. MacDonald, N.L. Guinnasso Jr., and J.A. Murray. 2003. Distinct side-scan sonar, RADARSAT SAR, and acoustic profiler signatures of gas and oil seeps on the Gulf of Mexico slope. Geo-Marine Letters 23:177–186, https://doi.org/10.1007/s00367-003-0139-9.
  8. Del Frate, F., A. Petrocchi, J. Lichtenegger, and G. Calabresi. 2000. Neural networks for oil spill detection using ERS-SAR data. IEEE Transactions on Geoscience and Remote Sensing 38(5):2,282–2,287, https://doi.org/​10.1109/36.868885.
  9. Fingas, M.F. 2004. Preface. Journal of Hazardous Materials 107:1, https://doi.org/10.1016/​j.jhazmat.2003.11.005.
  10. Fingas, M.F., and C.E. Brown. 1997. Review of oil spill remote sensing. Spill Science & Technology Bulletin 4(4):199–208, https://doi.org/10.1016/S1353-2561(98)00023-1.
  11. Fingas, M., and B. Fieldhouse. 2012. Studies on water-in-oil products from crude oils and petroleum products. Marine Pollution Bulletin 64(2):272–283, https://doi.org/​10.1016/j.marpolbul.2011.11.019.
  12. Franceschetti, G., A. Iodice, D. Riccio, G. Ruello, and R. Siviero. 2002. SAR raw signal simulation of oil slicks in ocean environments. IEEE Transactions on Geoscience and Remote Sensing 40(9):1,935–1,949, https://doi.org/​10.1109/TGRS.2002.803798.
  13. Garcia-Pineda, O., I.R. MacDonald, X. Li, C.R. Jackson, and W.G. Pichel. 2013. Oil spill mapping and measurement in the Gulf of Mexico with Textural Classifier Neural Network Algorithm (TCNNA). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE PP(99):1–9, https://doi.org/10.1109/JSTARS.2013.2244061.
  14. Garcia-Pineda, O., I. MacDonald, B. Zimmer, B. Shedd, and H. Roberts. 2010. Remote-sensing evaluation of geophysical anomaly sites in the Outer Continental Slope, northern Gulf of Mexico. Deep Sea Research Part II 57:1,859–1,869, https://doi.org/​10.1016/j.dsr2.2010.05.005.
  15. Garcia-Pineda, O., B. Zimmer, M. Howard, W. Pichel, X. Li, and I.R. MacDonald. 2009. Using SAR images to delineate ocean oil slicks with a Texture-Classifying Neural Network Algorithm (TCNNA). Canadian Journal of Remote Sensing 35(5):411-421, https://doi.org/10.5589/m09-035.
  16. Gonzalez, R.C., R.E. Woods, and S.L. Eddins. 2004. Digital Image Processing Using Matlab. Pearson Prentice-Hall, New Jersey, 609 pp.
  17. Holt, B. 2004. SAR Imaging of the ocean surface. Pp. 25–80 in Synthetic Aperture Radar Marine User’s Manual. C. Jackson and J. Apel, eds, US Department of Commerce, NOAA/NESDIS. Available online at: http://www.sarusersmanual.com/ManualPDF/NOAASARManual_CH02_pg025-080.pdf (accessed August 26, 2013).
  18. Hu, C., X. Li, W.G. Pichel, and F.E. Muller-Karger. 2009. Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery. Geophysical Research Letters 36, L01604, https://doi.org/10.1029/2008GL036119.
  19. Hu, C., F. Muller-Karger, C. Taylor, D. Myhre, B. Murch, A.L. Odriozola, and G. Godoy. 2003. MODIS detects oil spills in Lake Maracaibo, Venezuela. Eos, Transactions, American Geophysical Union 84(33):313–319, https://doi.org/10.1029/2003EO330002.
  20. Huehnerfuss, H., W. Alpers, W.D. Garrett, P.A. Lange, and S. Stolte. 1983. Attenuation of capillary and gravity waves at sea by monomolecular organic surface films. Journal of Geophysical Research 88:9,809–9,816, https://doi.org/10.1029/JC088iC14p09809.
  21. Jackson, C.R., and W. Alpers. 2010. The role of the critical angle in brightness reversals on sunglint images of the sea surface.Journal of Geophysical Research 115, C09019, https://doi.org/10.1029/2009JC006037.
  22. Jernelöv, A., and O. Lindén. 1981. IXTOC I: A case study of the world’s largest oil spill. Ambio 10(6):299–306.
  23. Knott, E.F., J.F. Shaeffer, and M.T. Tuley. 2004. Radar Cross Section, 2nd ed. SciTech Publishing, 611 pp.
  24. Leifer, I., W.J. Lehr, D. Simecek-Beatty, E. Bradley, R. Clark, P. Dennison, Y. Hu, S. Matheson, C.E. Jones, B. Holt, and others. 2012. State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill. Remote Sensing of Environment 124:185–209, https://doi.org/​10.1016/j.rse.2012.03.024.
  25. Lewis, H.G., and M. Brown. 2001. A generalized confusion matrix for assessing area estimates from remotely sensed data. International Journal of Remote Sensing 22(16):3,223–3,235, https://doi.org/10.1080/01431160152558332.
  26. Liu, Y., R.H. Weisberg, C. Hu, and L. Zheng. 2011. Tracking the Deepwater Horizon oil spill: A modeling perspective. Eos, Transactions, American Geophysical Union 92(6):45–46, https://doi.org/10.1029/2011EO060001.
  27. MacDonald, I. 2010. Deepwater disaster: How the oil spill estimates got it wrong. Significance 7:149–154, https://doi.org/​10.1111/j.1740-9713.2010.00449.x.
  28. MacDonald, I., N. Guinasso Jr., S.G. Ackelson, J.F. Amos, R. Duckworth, R. Sassen, and J.M. Brooks. 1993. Natural oil slicks in the Gulf of Mexico visible from space. Journal of Geophysical Research 98(C9):16,351–16,364, https://doi.org/10.1029/93JC01289.
  29. Maurizio, M., N. Ferdinando, C.E. Brown, B. Holt, X. Li, W. Pichel, and M. Shimada. 2012. Polarimetric synthetic aperture radar utilized to track oil spills. Eos, Transactions, American Geophysical Union 93(16):161–162, https://doi.org/10.1029/2012EO160001.
  30. May, E.F., B.F. Graham, A.S. Chauhan, and R.B. Trengove. 2008. Shear and electrical property measurements of water-in-oil emulsions and implications for multiphase flow meters. Energy & Fuels 22:3,308–3,316, https://doi.org/10.1021/ef800453a.
  31. McNutt, M.K., R. Camilli, T.J. Crone, G.D. Guthrie, P.A. Hsieh, T.B. Reyerson, O. Savas, and F. Shaffer. 2011. Review of flow rate estimates of the Deepwater Horizon oil spill. Proceedings of the National Academy of Sciences of the United States of America, 109:20,260–20,267, https://doi.org/10.1073/pnas.1112139108.
  32. Mera, D., J.M. Cotos, J. Varela-Pet, and O. Garcia-Pineda. 2012. Adaptive thresholding algorithm based on SAR images and wind data to segment oil spills along the northwest coast of the Iberian Peninsula. Marine Pollution Bulletin 64(10):2,090–2,096, https://doi.org/​10.1016/j.marpolbul.2012.07.018.
  33. Minchew, B., C.E. Jones, and B. Holt. 2012. Polarimetric analysis of backscatter from the Deepwater Horizon oil spill using L-band synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing 50(10):3,812–3,830, https://doi.org/​10.1109/TGRS.2012.2185804.
  34. Salem, F., and M. Kafatos. 2001. Hyperspectral image analysis for oil spill mitigation. Pp. 748–753 in Proceedings of ACRS 2001. 22nd Asian Conference on Remote Sensing, Singapore, November 5–9, 2001 (Trajectory Analysis for Oil Spills).
  35. Svejkovsky, J., W. Lehr, W. Muskat, G. Graettinger, and J. Mullin. 2012. Operational utilization of aerial multispectral remote sensing during oil spill response: Lessons learned during the Deepwater Horizon (MC-252) spill. Photogrammetric Engineering & Remote Sensing 78(10):1,089–1,102.
  36. Thibodeaux, L.J., K.T. Valsaraj, V.T. John, K.D. Papadopoulos, L.R. Pratt, and N.S. Pesika. 2011. Marine oil fate: Knowledge gaps, basic research, and development needs: A perspective based on the Deepwater Horizon spill. Environmental Engineering Science 28(2):87–93, https://doi.org/10.1089/ees.2010.0276.
  37. Topouzelis, K., V. Karathanassi, P. Paviakis, and D. Rokos. 2008. Dark formation detection using neural networks. International Journal of Remote Sensing 29(16):4,705–4,720, https://doi.org/10.1080/01431160801891770.
  38. Valenzuela, G. 1978. Theories for the interaction of electromagnetic and ocean waves—A review. Boundary-Layer Meteorology Journal 13:61–85, https://doi.org/10.1007/BF00913863.
  39. Xia, Y.Q., and M.C. Boufadel. 2010. Lessons from the Exxon Valdez oil spill disaster in Alaska. Disaster Advances 3(4):270–273.
Copyright & Usage

This is an open access article made available under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format as long as users cite the materials appropriately, provide a link to the Creative Commons license, and indicate the changes that were made to the original content. Images, animations, videos, or other third-party material used in articles are included in the Creative Commons license unless indicated otherwise in a credit line to the material. If the material is not included in the article’s Creative Commons license, users will need to obtain permission directly from the license holder to reproduce the material.