PROCESSING SENTINEL-1 SAR DATA FOR DETECTING OIL SPILLS IN THE CASPIAN SEA USING GOOGLE EARTH ENGINE
DOI:
https://doi.org/10.54668/2789-6323-2024-112-1-100-109Keywords:
oil spill, SAR image, Google Earth Engine, monitoringAbstract
Hydrocarbon pollution of the water surface is one of the most important environmental problems in the Caspian Sea. There are ongoing developments in the identification of contamination pollution using remote sensing data for environmental situations. In recent years, there has been a significant increase in satellite data and, consequently, an opportunity to increase the frequency of observations. The ability to handle increased amounts of data can be achieved through cloud-based processing. The purpose of this work was to update oil spill monitoring technology by utilizing advanced computing resources based on the Google Earth Engine (GEE) platform and radar satellite images Sentinel-1. An oil spill detection technology was developed by the study using only data archives available in the GEE environment.
References
Закарин Э.А., Балакай Л.А., Бостанбеков К.А., Дедова Т.В., Ким Д.К., Кобегенова С.С., Миркаримова Б.М., Нурсеитов Д.Б. Моделирование экологических рисков при нефтяном загрязнении акватории Северо-восточного Каспия: монография. - Алматы, 2016. – 256 с.
Лаврова О.Ю., Митягина М.И., Уваров И.А., Лупян Е.А. Текущие возможности и опыт использования информационной системы See the Sea для изучения и мониторинга явлений и процессов на морской поверхности // Современные проблемы дистанционного зондирования Земли из космоса. – 2019. – Т.16. – №3. – С.266-287. DOI: 10.21046/2070-7401-2019-16-3-266-287.
Тайжанова Л. Влияние нефтесодержащих производственных сточных вод на прибрежные воды Каспийского моря // Гидрометеорология и экология. – 2023. – N.1. – P.27–35. DOI:https://doi.org/10.54668/2789-6323-2021-100-1-27-35.
Alpers W., Espedal H.A. Oils and Surfactants. – 2004. – Ch. 11.
Barzegar F., Seyd, S. T., Farzaneh S., Sharifi M. A. Oil spill detection in the Caspian sea with a SAR image using a DENSENET model // ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. – 2023. – V.X-4/W1-2022. – P.95–100. DOI: https://doi.org/10.5194/isprs-annals-X-4-W1-2022-95-2023.
Del Frate F., Petrocchi A., Lichtenegger J., Calabresi G. Neural networks for oil spill detection using ERS-SAR data // IEEE Trans. Geosci. Remote Sens. – 2000. – V.5. – 2282-2287.
Gorelick N., Hancher M., Dixon M., Ilyushchenko S., Thau D., Moore R. Google Earth engine: planetary-scale geospatial analysis for everyone // Remote Sens. Environ. – 2017. – V. 202. – P.18–27. DOI: https://doi.org/10.1016/J.RSE.2017.06.031.
Hamoun J., Mehran P., Mohammad R., Golriz E.S. An Overview of Oil Pollution in the Caspian Sea // Journal of Environmental Research and Technology. – 2018. – V.3. – N.2. – P.33-39.
Holstein A., Kappas M., Propastin P. et al. Oil spill detection in the Kazakhstan sector of the Caspian Sea with the help of ENVISAT ASAR data // Environ Earth Sci. – 2018. – V.77. – N.198. DOI: https://doi.org/10.1007/s12665-018-7347-0.
Mityagina M. I., Lavrova O. Y., Kostianoy A. G. Main pattern of the Caspian sea surface oil pollution revealed by satellite data // Ecologica Montenegrina. – 2019. – V. 25. – P.91–105. DOI: 10.37828/em.2019.25.9
Murakami H. ATBD of GCOM-C chlorophyll-a concentration algorithm. – 2020. https://suzaku.eorc.jaxa.jp/GCOM_C/data/ATBD/ver2/V2ATBD_O3AB_Chla_Murakami.pdf
Otsu N. A threshold selection method from gray-level histograms. // IEEE Trans Syst Man Cyber. – 1997. – V.SMC-9. – N.1. – P.62–66. DOI:https://doi.org/10.1109/tsmc.1979.4310076.
Saha S., Moorthi S., Pan H., Wu X. et al. The NCEP Climate Forecast System Reanalysis // Bulletin of the American Meteorological Society. – 2010. – V.91. – N.8. – P.1015-1058. DOI: https://doi.org/10.1175/2010BAMS3001.1
Solberg A., Storvik G., Solberg R., Volden E. Automatic Detection of Oil Spills in ERS SAR Images // IEEE Trans. Geosci. Remote Sens. - 1999. - V.37. - P.1916-1924.
Topouzelis K., Karathanassi, V., Pavlakis P., Rokos D. Detection and discrimination between oil spills and look-alike phenomena through neural networks // ISPRS J. Photogramm. Remote Sens. – 2007. – V.62. – P.264-270.
Yi-Jie Yang, Singha S., Mayerle R. A deep learning based oil spill detector using Sentinel-1 SAR imagery // International Journal of Remote Sensing. – 2022. – V.43:11. – P.4287-4314. DOI: 10.1080/01431161.2022.2109445.
