Scholarly record
MONITORING OF OIL POLLUTIONS IN THE CASPIAN SEA USING SENTINEL-1 AND SENTINEL-2 IMAGES
Abstract
Currently, the issue of pollution of the Caspian Sea, which is saturated with oil industry facilities, is highly relevant. Monitoring of such objects and phenomena that pose a potential and real threat of natural and man-made emergencies, such as emergency oil spills that entail significant environmental damage, is important for Kazakhstan. The main purpose of this paper is to evaluate the capabilities of SAR and optical images in the task of detecting oil spills, having tested them in the task of mapping zones with a high frequency of occurrence of oil spills for three years. Due to its many advantages, SAR imagery is currently the most common remote sensing tool for oil spills. However, using SAR images, it is sometimes difficult to identify man-made oil spills and false objects similar to them, which are commonly called ?look-alikes? and include windless areas, organic slicks of natural origin, algal blooms, etc. The combined use of SAR and optical data improves the reliability of oil pollution detection. Using optical images to detect oil spills, we rely on the spectral characteristics of objects, and although the reflectivity varies depending on the thickness and composition of the spot, in most cases oil spills can be distinguished from similar spurious objects. The large amount of data made it possible to generalize and obtain statistically reliable results on the spatial and temporal variability of the manifestations of slick pollution of various types on satellite images of the sea surface. From April 2018 to December 2019 alone, 1303 satellite images of the sea surface from the Sentinel 1 and 2 satellites were received and analyzed. The total area of the detected oil spills in the Kazakhstan part of the Caspian Sea reached 68.4 km2. The main source of surface pollution of open areas of the Caspian Sea identified during monitoring are leaks and discharges from ships of oil-contaminated waters along the main shipping lanes.
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References12
Solberg A.H.S. et al. Automatic detection of oil spills in ERS SAR images // IEEE Transactions on geoscience and remote sensing. – 1999. – Vol. 37. – № 4. – Р. 1916-1924.
de Souza, Danilo L., Adri?o DD Neto, and Wilson da Mata. "Intelligent system for feature extraction of oil slick in SAR images: Speckle filter analysis." In International Conference on Neural Information Processing. Springer, Berlin, Heidelberg (2006): 729-736
Del Frate F. et al. Neural networks for oil spill detection using ERS–SAR data // IEEE Transactions on geoscience and remote sensing. – 2000. – Vol. 38. – № 5. – Р. 2282-2287.
Montali A. et al. Supervised pattern classification techniques for oil spill classification in SAR images: preliminary results // SEASAR2006 Workshop, ESAESRIN, Frascati, Italy, January. – 2006. – P. 23-26.
Solberg, Anne HS, and Camilla Brekke. "Oil spill detection in Northern European Waters: approaches and algorithms." In Remote Sensing of the European Seas, Springer, Dordrecht (2008): 359-370.
Ivanov, A. Yu, M. Yu Dostovalov, and A. A. Sineva. "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, no. 9 (2012): 1014-1026.
Zhantayev Zh.Sh., Chepashev D.V., Kaldybaev A.A., Nurakynov S.M., Ungarov K.M. Mapping frequency of oil spills in the Caspian Sea using sentinel-2 optical images // News of the national academy of sciences of the republic of Kazakhstan-series physico-mathematical. – 2020. – Vol. 3. – pp.183-190.
Solberg A. S. et al. Algorithms for oil spill detection in Radarsat and ENVISAT SAR images // IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium. – IEEE, 2004. – Vol. 7. – P. 4909-4912.
Carnesecchi F. et al. An optical model for the interpretation of remotely sensed multispectral images of oil spill //Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2008. – International Society for Optics and Photonics, 2008. – Vol. 7105. – P. 704.
Klemas V. Tracking oil slicks and predicting their trajectories using remote sensors and models: case studies of the Sea Princess and Deepwater Horizon oil spills // Journal of Coastal Research. – 2010. – Р. 789-797.
Corucci L., Nardelli F., Cococcioni M. Oil spill classification from multi–spectral satellite images: exploring different machine learning techniques // Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2010. – International Society for Optics and Photonics, 2010. – Vol. 7825. – P. 782509.
Adamo M. et al. Detection and tracking of oil slicks on sun–glittered visible and near infrared satellite imagery //International Journal of Remote Sensing. – 2009. – Vol. 30. – №. 24. – P. 6403-6427.
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