Detection of oil spill hotspots in the Caspian Sea using remote sensing (case study: Baku oil extraction facility)

Document Type : Original Article


1 Department of Environment Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran

2 Department of Geomatics Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran


Due to the presence of oil resources in the seas, the exploration, extraction, and transportation of petroleum products lead to the formation of hydrocarbon spills on the surface waters, resulting in a decrease in the quality of these waters. Oil leakage into the sea has irreparable environmental consequences and disrupts coastal and offshore ecosystems. As a result, identifying the location and time of oil accidents and recognizing the extent and magnitude of contamination is of great importance for monitoring and protecting the health of the environment and is now facilitated and possible by remote sensing data using optical and radar satellites. In this study, to enhance the detection of oil-contaminated areas in Azerbaijan oil facilities in the Caspian Sea, we simultaneously used the optical satellites LANDSAT8, SENTINEL2, and radar satellite SENTINEL1 because of the high spatial resolution and close period.
Material and methods:
In this study, oil contamination caused by Oil Rocks facilities was investigated by satellite images between April and June 2017. After detecting contamination spots on the surface of the water around the facility in radar images, to ensure that the identified spots were caused by oil spills , detecting oil contamination from optical images using band ratio method. was used Then, the feature extraction method was applied to band ratio images to distinguish their complications.
Results and discussion:
The area of oil spills in April 2017 increased within 12 days and considering the covering percentage of classes of oil and oily water in the results, the increase in the spread of oil spills through currents, and the continuation of leakage from its source was evident. Also, looking at the optical images of Landsat 8 and Sentinel 2 on June 5, 2017 showed the same results in oil contamination areas.
The results of this study showed that the band rationing method is suitable for quick detection of oil leakage. To identify the details of the area of contamination, the feature extraction method was used to classify the band ratio images to the identified classes. Also, from the environmental point of view, the Oil Rocks settlement put the Caspian seawater in the Republic of Azerbaijan in unfavorable conditions. The northwestern coast of Iran is also exposed to contamination because of current directions in that region. Therefore, actions must be taken to collect and clean up oil spills around this oil facility. In order to do so, oil contamination on the water surface must be removed using existing physical and biological methods.


  1. Akkartal, A. and Sunar, F., 2008. The usage of radar images in oil spill detection. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 37, 271-276.
  2. CSNRC, 2011. Caspian Sea State of the Environment. Caspian Environment Program, Interim Secretariat of the Framework Convention for the Protection of the Marine Environment of the Caspian Sea. Available online at:
  3. Carnesecchi, F., Byfield, V., Cipollini, P., Corsini, G. and Diani, M., 2008. An optical model for the interpretation of remotely sensed multispectral images of oil spill. Proceedings of Remote Sensing Of The Ocean, Sea Ice, and LargeWater Regions, 15th September, Cardiff, Wales, United Kingdom.
  4. Cococcioni, M., Corucci, L. and Lazzerini, B., 2009. Issues and preliminary results in oil spill detection using optical remotely sensed images. In Proceeding of Oceans -Europe, 11th - 14th May, Germany.
  5. Girard-Ardhuin, F., Mercier, G. and Garello, R., 2003. Oil slick detection by SAR imagery: potential and limitation. Oceans. 1, 164-169.
  6. Howari, F., 2004. Investigation of hydrocarbon pollution in the vicinity of United Arab Emirates coasts using visible and near infrared remote sensing data. Journal of Coastal Research. 20(4), 1089-1095.
  7. Ivanov, A.Y., Dostovalov, M.Y. and Sineva, A.A., 2012. Characterization of oil pollution around the oil rocks production site in the Caspian Sea using spaceborne polarimetric SAR imagery. Izvestiya, Atmospheric and Oceanic Physics. 48(9), 1014-1026.
  8. Kolokoussis, P. and Karathanassi, V., 2018. Oil spill detection and mapping using sentinel 2 imagery. Journal of Marine Science and Engineering. 6(1), 1-12.
  9. Lee, M.S., Park, K.A., Lee, H.R., Park, J.J., Kang, C.K. and Lee, M., 2016. Detection and dispersion of oil spills from satellite optical images in a coastal bay. International Geoscience and Remote Sensing Symposium (IGARSS), 10th - 15th July, Beijing, China.
  10. Mityagina, M. and Lavrova, O., 2016. Satellite survey of inner seas: oil pollution in the Black and Caspian seas. Remote Sensing. 8(10), 1-24.
  11. Majidi Nezhad, M., Groppi, D., Laneve, G., Marzialetti, V. and Piras, G., 2018. Oil spill detection analyzing “Sentinel 2” satellite images: a Persian Gulf case study. In Proceeding of the 3rd World Congress on Civil, Structural and Environmental Engineering, 8th - 10th April, Budapest, Hungary.
  12. Pavlakis, P., Sieber, A. and Alexandry, S., 1996. Monitoring oil-spill pollution in the Mediterranean with ERS SAR. European space agency. Earth Observation Quarterly. 52, 8-11.
  13. Raygani, B., Najafi Yasuri, M., Bodagh Jamali, J. and Sarkheil, H., 2019. Detection of oil spill hotspots using time-series MODIS Data (case study: Persian Gulf). Petroleum Research. 29(98-5), 97-106.
  14. Taravat, A. and Del Frate, F., 2012. Development of band ratioing algorithms and neural networks to detection of oil spills using Landsat ETM+ data. EURASIP Journal on Advances in Signal Processing .107, 1-8.
  15. Topouzelis, K., 2008. Oil spill detection by SAR images: dark formation detection, feature extraction and classification algorithms. Sensors. 8(10), 6642-6659.
  16. Topouzelis, K. and Singha, S., 2016. Oil spill detection: past and future trends. ESA Living Planet Symposium, 9th - 13th May, Prague, Czech Republic.
  17. United States Environmental Protection Agency, Office of Emergency and Remedial Response., 1999. Understanding oil spills and oil spill response.