Detecting Oil Spill Contamination Using Airborne Hyperspectral Data in the River Nile, Egypt


Egypt is a highly populated country of about 85 million inhabitants that are concentrated on the Nile Delta and on the flood plain of the Nile River. More than 90% of this population relies on the Nile River in their water demand for domestic use. Currently, Egypt is facing a problem with the trans-boundary water budget coming from the Nile basin. This urges for managing the water quantity and quality to secure the water needs. This paper discusses the potential use of airborne hyperspectral data for water quality management in the form of detecting the oil contamination in the Nile River in integration with in-situ measurements including ASD spectroradiometer and eco-sounder multi-probe devices. The eco-sounder multi-probe device measured most of the water quality parameters and detected the existence of oil contamination at 1200 bb downstream of the study area. The airborne hyperspectral images were analyzed and calibrated with the spectral library determined from the in-situ spectroradiometer to map the patches of the oil contamination. The details of the findings and learning lessons are fully discussed in the paper.

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El-Magd, I. , Kafrawy, S. and Farag, I. (2014) Detecting Oil Spill Contamination Using Airborne Hyperspectral Data in the River Nile, Egypt. Open Journal of Marine Science, 4, 140-150. doi: 10.4236/ojms.2014.42014.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Food and Agriculture Organization (2011) Information Products for Nile Basin Water Resources Management. Project GCP/INT/945/ITA 2004 to 2009, FAO, Rome.
[2] Carroll, K. (2011) China Dams in Africa: A Case Study May 2011. Culture and Conflict Studies Program, China Studies Center.
[3] El-Sheekh, M. (2009) River Nile Pollutants and Their Effect on Life Forms and Water Quality. In: Dumont, H.J., Ed., The Nile: Origin, Environments, Limnology and Human Use, Series: Monographiae Biologicae, Vol. 89, Springer, Dordrecht, 395-406.
[4] Ramirez-Marquez, J.E. and Sauser, B.J. (2009) System Development Planning via System Maturity Optimization. IEEE Transactions on Engineering Management, 56, 533-548.
[5] Brekke, C. and Solberg, A.H.S. (2005) Oil Spill Detection by Satellite Remote Sensing. Remote Sensing of Environment, 95, 1-13.
[6] Fingas, M.F. and Brown, C.E. (1997) Review of Oil Spill Remote Sensing. Spill Science & Technology Bulletin, 4, 199-208.
[7] Shen, S.S. and Lewis, P.E. (2011) Deepwater Horizon Oil Spill Monitoring Using Airborne Multispectral Infrared Imagery. SPIE, Orlando.
[8] Migliaccio, M., Gambardella, A. and Nunziata, F. (2009) The PALSAR Polarimetric Mode for Sea Oil Slick Observation. IEEE Transactions on Geosciences and Remote Sensing, 47, 4032-4041.
[9] Swayze, G.A., Furlong, E.T. and Livo, K.E. (2007) Mapping Pollution Plumes in Areas Impacted by Hurricane Katrina with Imaging Spectroscopy. Fall Meeting, H31L-07, American Geophysical Union, San Francisco.
[10] ASD (2008) Technical Manual of the ASD Spectroradiometer Device, USA, 106p.
[11] Clark, R.N. (1999) Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy. In: Rencz, A.N., Ed., Manual of Remote Sensing, Remote Sensing for the Earth Sciences, John Wiley and Sons, New York, 3-58.
[12] Lammoglia, T. and De Souza Filho, C.R. (2011) Spectroscopic Characterization of Oils Yielded from Brazilian Offshore Basins: Potential Applications of Remote Sensing. Remote Sensing of Environment, 115, 2525-2535.
[13] Horig, B., Kühn, F., Oschütz, F. and Lehmann, F. (2001) HyMap Hyperspectral Remote Sensing to Detect Hydrocarbons. International Journal of Remote Sensing, 22, 1413-1422.
[14] Kühn, F., Oppermann, K. and Horig, B. (2004) Hydrocarbon Index an Algorithm for Hyperspectral Detection of Hydrocarbons. International Journal of Remote Sensing, 25, 2467-2473.
[15] Byfield, V. and Boxall, S.R. (1999) Thickness Estimates and Classification of Surface Oil Using Passive Sensing at Visible and Near-Infrared Wavelengths. IEEE 1999 International Geosciences and Remote Sensing Symposium, IGARSS '99 Proceedings, 3, 1475-1477.
[16] Wettle, M., Daniel, P.J., Logan, G.A. and Thankappan, M. (2009) Assessing the Effect of Hydrocarbon Oil Type and Thickness on a Remote Sensing Signal: A Sensitivity Study Based on the Optical Properties of Two Different Oil Types and the HYMAP and Quickbird Sensors. Remote Sensing of Environment, 113, 2000-2010.
[17] Eureka Environmental Engineering (2008) Technical Manual of the Manta 2 Water Quality Multiprobe. Austin, 51.

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