The proposed study focuses on the reported oil spill detection and assessments of oil impacts on marine ecosystems. Five selected oil spills, including those in East China Sea, Balikpapan Bay, Red Sea, Mauritius coast, and Colombo coast were detected using the Sentinel-1 (S-1) satellite dataset. Sentinel-2 (S-2)/ Landsat 8 (OLI), and Sentinel-5 Precursor (S-5P) satellite datasets were utilized to observe the impacts of oil spills on vegetation cover and air quality respectively. Synthetic Aperture Radar (SAR)-based oil spill detection techniques are effective in monitoring oil pollution. Impacts of oil spills on vegetation are monitored via different Vegetation Indices (VI). The East China Sea spill moved around 190 km from the source point. The area of vegetation cover impacted by the Balikpapan Bay oil spill was 118 km2. Near real-time (NRT) data of different toxic gases from S-5P were analyzed for Sri Lanka and the Red Sea using the Google Earth Engine (GEE). It is concluded that wind speed was between the range of 3 to 9 m/s that is favorable for the oil spill detection, and it is also observed that wind direction had impacts on oil spill movement as well. VI provides highly reliable results for the four events but the Red Sea oil spill findings were not satisfactory due to low vegetation cover in this area.
Figure 1. Study area map showing oil spillage incident locations.
Figure 2. East China Sea oil spill: a. pre-oil spill image (2017−12−27), b. post-oil spill image (2018−01−20), c. classified oil spill image (wind direction: south).
Figure 4. Red Sea oil spill incident: a. pre-oil spill image (2019−09−01), b. post-oil spill image (2019−10−13) with wind direction (south), c. classified oil spill image.
Figure 5. Mauritius oil spill: a. pre-oil spill image (2020−07−17), b. post-oil spill image (2020−08−10) with wind direction (blue arrow), c. classified image.
Figure 8. Wind rose diagrams for oil spill event locations: a. East China Sea, b. Balikpapan Bay, c. Red Sea, d. Mauritius, and e. Colombo.
Figure 9. Normalized difference vegetation index (NDVI) based assessment for oil spill impacts: time−series NDVI maps showing vegetation changes in the coastal regions affected by oil spills. Subplots a. East China Sea (2016−05−05), b. East China Sea (2017−10−09), c. East China Sea (2018−10−09), d. East China Sea (2019−10−09), e. Balikpapan Bay (2016−08−07), f. Balikpapan Bay (2017−08−31), g. Balikpapan Bay (2018−08−31), h. Balikpapan Bay (2019−08−31), i. Red Sea Jeddah (2017−03−23), j. Red Sea Jeddah (2018−03−23), k. Red Sea Jeddah (2020−03−22), l. Red Sea Jeddah (2021−03−22), m. Mauritius (2018−01−29), n. Mauritius (2019−01−29), o. Mauritius (2021−01−28), p. Mauritius (2022−01−28), q. Colombo (2019−11−17), r. Colombo (2020−11−21), s. Colombo (2021−11−21), t. Colombo (2022−11−26).
Figure 10. Enhanced vegetation index (EVI) based assessment for oil spill impacts: time−series evi maps showing vegetation health in the coastal regions affected by oil spills. Subplots a. East China Sea (2016−05−05), b. East China Sea (2017−10−09), c. East China Sea (2018−10−09), d. East China Sea (2019−10−09), e. Balikpapan Bay (2016−08−07), f. Balikpapan Bay (2017−08−31), g. Balikpapan Bay (2018−08−31), h. Balikpapan Bay (2019−08−31), i. Red Sea Jeddah (2017−03−23), j. Red Sea Jeddah (2018−03−23), k. Red Sea Jeddah (2020−03−22), l. Red Sea Jeddah (2021−03−22), m. Mauritius (2018−01−29), n. Mauritius (2019−01−29), o. Mauritius (2021−01−28), p. Mauritius (2022−01−28), q. Colombo (2019−11−17), r. Colombo (2020−11−21), s. Colombo (2021−11−21), t. Colombo (2022−11−26).
Figure 11. Leaf Chlorophyll Index (LCI) based assessment for oil spill impacts: time−series lci maps showing chlorophyll content in the vegetation of the coastal regions affected by oil spills. Subplots a. East China Sea (2016−05−05), b. East China Sea (2017−10−09), c. East China Sea (2018−10−09), d. East China Sea (2019−10−09), e. Balikpapan Bay (2016−08−07), f. Balikpapan Bay (2017−08−31), g. Balikpapan Bay (2018−08−31), h. Balikpapan Bay (2019−08−31), i. Red Sea Jeddah (2017−03−23), j. Red Sea Jeddah (2018−03−23), k. Red Sea Jeddah (2020−03−22), l. Red Sea Jeddah (2021−03−22), m. Mauritius (2018−01−29), n. Mauritius (2019−01−29), o. Mauritius (2021−01−28), p. Mauritius (2022−01−28), q. Colombo (2019−11−17), r. Colombo (2020−11−21), s. Colombo (2021−11−21), t. Colombo (2022−11−26).
Figure 12. Normalized difference water index (NDWI) based assessment for oil spill impacts: time−series NDWI maps highlighting changes in water presence and potential oil contamination in the coastal regions. Subplots a. East China Sea (2016−05−05), b. East China Sea (2017−10−09), c. East China Sea (2018−10−09), d. East China Sea (2019−10−09), e. Balikpapan Bay (2016−08−07), f. Balikpapan Bay (2017−08−31), g. Balikpapan Bay (2018−08−31), h. Balikpapan Bay (2019−08−31), i. Red Sea Jeddah (2017−03−23), j. Red Sea Jeddah (2018−03−23), k. Red Sea Jeddah (2020−03−22), l. Red Sea Jeddah (2021−03−22), m. Mauritius (2018−01−29), n. Mauritius (2019−01−29), o. Mauritius (2021−01−28), p. Mauritius (2022−01−28), q. Colombo (2019−11−17), r. Colombo (2020−11−21), s. Colombo (2021−11−21), t. Colombo (2022−11−26).
Figure 13. Air quality impact assessment of the red sea oil spill: a. temporal variation in carbon monoxide (CO) levels, b. changes in the mean concentration of nitrogen dioxide (NO2), and c. concentration of sulfur dioxide (SO2).
Figure 14. Air quality impact assessment of the Sri Lanka oil spill: a. temporal variation in carbon monoxide (CO) levels, b. Changes in the mean concentration of nitrogen dioxide (NO2), and c. concentration of sulfur dioxide (SO2).
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