SAR-based oil spill detection and impact assessment on coastal and marine environments
-
Abstract: 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.
-
Key words:
- oil spill detection /
- vegetation cover /
- air quality assessment /
- sentinel /
- synthetic aperture radar
-
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).
Table 1. Sentinel-1 operational product characteristics (IW, SM, EW, Az, FR, HR, MR and Rz).
Data mode Resolution class Resolution (Rz × Az)/m Pixel Spacing (Rz × Az) /m) No. of Looks (Rz × Az) Swath/km SM FR 9 × 9 4 × 4 2 × 2 80 HR 23 × 23 10 × 10 6 × 6 MR 84 × 84 40 × 40 22 × 22 IW HR 20 × 22 10 × 10 5 × 1 250 MR 88 × 89 40 × 40 22 × 5 EW HR 50 × 50 25 × 25 3 × 1 400 MR 93 × 87 40 × 40 6 × 2 
下载: 导出CSV
Table 2. Geographic location and oil type of spill events.
Sr. No. Location Latitude Longitude Event date Pre-acquisition date Post-acquisition date Oil spill volume Type of Oil 1 East China Sea 28.634°N 125.617°E 6 January 2018 27 December 2017 20 January 2018 111000 (mt)/
1900 tULHFCO/BO 2 Balikpapan Bay
(Indonesia)01.830°N 116.507°E 29 March 2018 8 March 2018 1 April 2018 - CO 3 Red Sea
(Jeddah)20.447°N 38.339°E 11 October 2019 1 September 2019 13 October 2019 - CO 4 Mauritius coast 20.221°S 57.617°E 6 August 2020 17 July 2020 10 August 2020 4100 (mt)LSF 5 Colombo
(Sri Lanka)06.847°N 79.271°E 26 May 2021 3 May 2021 8 June 2021 350(t) BO
下载: 导出CSV
Table 3. Event parameters and data acquisition dates for oil spill analysis.
Sr. No. Effected coastal regions Event location Buffer size (km) Pre-acquisition dates Post-acquisition dates 1 Japan Coast East China Sea 40 05 May 2016
09 October 201709 October 2018
09 October 20192 Balikpapan Bay Balikpapan Bay 5 07 August 2016
31 August 201731 August 2018
31 August 20193 Jeddah coast Red Sea 20 23 March 2017
23 March 201822 March 2020
22 March 20214 Mauritius Coast Mauritius Coast 1 29 January 2018
29 January 201928 January 2021
28 January 20225 Colombo Coast Colombo Coast 15 17 November 2019
21 November 202021 November 2021
26 November 2022
下载: 导出CSV
-
Adamu B, Tansey K, Ogutu B. 2018. Remote sensing for detection and monitoring of vegetation affected by oil spills. International Journal of Remote Sensing, 39(11): 3628–3645, doi: 10.1080/01431161.2018.1448483 Ajadi O A, Meyer F J, Tello M, et al. 2018. Oil spill detection in synthetic aperture radar images using Lipschitz-regularity and multiscale techniques. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(7): 2389–2405, doi: 10.1109/JSTARS.2018.2827996 Alaa El-Din G, Amer A A, Malsh G, et al. 2018. Study on the use of banana peels for oil spill removal. Alexandria Engineering Journal, 57(3): 2061–2068, doi: 10.1016/j.aej.2017.05.020 Alpers W, Holt B, Zeng Kan. 2017. Oil spill detection by imaging radars: challenges and pitfalls. Remote Sensing of Environment, 201: 133–147, doi: 10.1016/j.rse.2017.09.002 Arellano P, Tansey K, Balzter H, et al. 2015. Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. Environmental Pollution, 205: 225–239, doi: 10.1016/j.envpol.2015.05.041 Bhatnagar S, Gill L, Regan S, et al. 2020. Mapping vegetation communities inside wetlands using sentinel-2 imagery in Ireland. International Journal of Applied Earth Observation and Geoinformation, 88: 102083, doi: 10.1016/j.jag.2020.102083 Brekke C, Solberg A H S. 2005. Oil spill detection by satellite remote sensing. Remote Sensing of Environment, 95(1): 1–13, doi: 10.1016/j.rse.2004.11.015 Cantorna D, Dafonte C, Iglesias A, et al. 2019. Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms. Applied Soft Computing, 84: 105716, doi: 10.1016/j.asoc.2019.105716 Cervantes-Hernández P, Celis-Hernández O, Ahumada-Sempoal M A, et al. 2024. Combined use of SAR images and numerical simulations to identify the source and trajectories of oil spills in coastal environments. Marine Pollution Bulletin, 199: 115981, doi: 10.1016/j.marpolbul.2023.115981 Chiu C M, Huang Ching-Jer, Wu Li-Chung, et al. 2018. Forecasting of oil-spill trajectories by using SCHISM and X-band radar. Marine Pollution Bulletin, 137: 566–581, doi: 10.1016/j.marpolbul.2018.10.060 Clevers J G P W, Gitelson A A. 2013. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on sentinel-2 and-3. International Journal of Applied Earth Observation and Geoinformation, 23: 344–351, doi: 10.1016/j.jag.2012.10.008 Delegido J, Verrelst J, Alonso L, et al. 2011. Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7): 7063–7081, doi: 10.3390/s110707063 Drusch M, Del Bello U, Carlier S, et al. 2012. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120: 25–36, doi: 10.1016/j.rse.2011.11.026 Eskes H, Huijnen V, Arola A, et al. 2015. Validation of reactive gases and aerosols in the MACC global analysis and forecast system. Geoscientific Model Development, 8(11): 3523–3543, doi: 10.5194/gmd-8-3523-2015 Evans D D, Mulholland G W, Baum H R, et al. 2001. In situ burning of oil spills. Journal of Research of the National Institute of Standards and Technology, 106(1): 231–278, doi: 10.6028/jres.106.009 Ewing B R, Hawkins T R, Wiedmann T O, et al. 2012. Integrating ecological and water footprint accounting in a multi-regional input-output framework. Ecological Indicators, 23: 1–8, doi: 10.1016/j.ecolind.2012.02.025 Fan Jianchao, Zhang Fengshou, Zhao Dongzhi, et al. 2015. Oil spill monitoring based on SAR remote sensing imagery. Aquatic Procedia, 3: 112–118, doi: 10.1016/j.aqpro.2015.02.234 Fingas M, Brown C. 2014. Review of oil spill remote sensing. Marine Pollution Bulletin, 83(1): 9–23, doi: 10.1016/j.marpolbul.2014.03.059 Fingas M, Brown C E. 2018. A review of oil spill remote sensing. Sensors, 18(1): 91 Fiscella B, Giancaspro A, Nirchio F, et al. 2000. Oil spill detection using marine SAR images. International Journal of Remote Sensing, 21(18): 3561–3566, doi: 10.1080/014311600750037589 Frampton W J, Dash J, Watmough G, et al. 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82: 83–92, doi: 10.1016/j.isprsjprs.2013.04.007 Freedman B. 1995. The ecological effects of pollution, disturbance, and other stresses. In: Freedman B, ed. Environmental Ecology. 2nd ed. Amsterdam: Elsevier, 1–10 Ganjirad M, Bagheri H. 2024. Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery. Ecological Informatics, 80: 102498, doi: 10.1016/j.ecoinf.2024.102498 Guanter L, Aben I, Tol P, et al. 2015. Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence. Atmospheric Measurement Techniques, 8(3): 1337–1352, doi: 10.5194/amt-8-1337-2015 Huete A, Didan K, Miura T, et al. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2): 195–213, doi: 10.1016/S0034-4257(02)00096-2 Jana A, Maiti S, Biswas A. 2016. Seasonal change monitoring and mapping of coastal vegetation types along Midnapur-Balasore coast, bay of Bengal using multi-temporal Landsat data. Modeling Earth Systems and Environment, 2(1): 7, doi: 10.1007/s40808-015-0062-x Khanna S, Santos M J, Ustin S L, et al. 2013. Detection of salt marsh vegetation stress and recovery after the Deepwater horizon oil spill in Barataria Bay, Gulf of Mexico using AVIRIS data. PLoS One, 8(11): e78989, doi: 10.1371/journal.pone.0078989 Lee J S, Wen J H, Ainsworth T L, et al. 2008. Improved sigma filter for speckle filtering of SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 47: 202–213 Lehr W J, Fraga R J, Belen M S, et al. 1984. A new technique to estimate initial spill size using a modified Fay-type spreading formula. Marine Pollution Bulletin, 15(9): 326–329, doi: 10.1016/0025-326X(84)90488-0 Li Lin, Ustin S L, Lay M. 2005. Application of AVIRIS data in detection of oil-induced vegetation stress and cover change at Jornada, New Mexico. Remote Sensing of Environment, 94(1): 1–16, doi: 10.1016/j.rse.2004.08.010 Lin Qianxin, Mendelssohn I A. 2012. Impacts and recovery of the Deepwater horizon oil spill on vegetation structure and function of coastal salt marshes in the northern gulf of Mexico. Environmental Science & Technology, 46(7): 3737–3743 Lu Jiang. 2003. Marine oil spill detection, statistics and mapping with ERS SAR imagery in south-east Asia. International Journal of Remote Sensing, 24(15): 3013–3032, doi: 10.1080/01431160110076216 Mahindapala W K M. 2020. Oil spill detection in the east of Sri Lanka with sentinel-1 SAR. E3S Web of Conferences, 211: 02013, doi: 10.1051/e3sconf/202021102013 Malenovský Z, Rott H, Cihlar J, et al. 2012. Sentinels for science: potential of sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land. Remote Sensing of Environment, 120: 91–101, doi: 10.1016/j.rse.2011.09.026 Marghany M, Van Genderen J. 2014. Entropy algorithm for automatic detection of oil spill from radarsat-2 SAR data. IOP Conference Series: Earth and Environmental Science, 18: 012051, doi: 10.1088/1755-1315/18/1/012051 Mdakane L W, Kleynhans W. 2022. Feature selection and classification of oil spill from vessels using sentinel-1 wide–swath synthetic aperture radar data. IEEE Geoscience and Remote Sensing Letters, 19: 4002505 Mera D, Cotos J M, Varela-Pet J, et al. 2012. Adaptive thresholding algorithm based on SAR images and wind data to segment oil spills along the northwest coast of the Iberian Peninsula. Marine Pollution Bulletin, 64(10): 2090–2096, doi: 10.1016/j.marpolbul.2012.07.018 Migliaccio M, Nunziata F, Buono A. 2015. SAR polarimetry for sea oil slick observation. International Journal of Remote Sensing, 36(12): 3243–3273, doi: 10.1080/01431161.2015.1057301 Misra A, Balaji R. 2017. Simple approaches to oil spill detection using sentinel application platform (SNAP)-ocean application tools and texture analysis: a comparative study. Journal of the Indian Society of Remote Sensing, 45(6): 1065–1075, doi: 10.1007/s12524-016-0658-2 Naz S, Iqbal M F, Mahmood I, et al. 2021. Marine oil spill detection using synthetic aperture radar over Indian Ocean. Marine Pollution Bulletin, 162: 111921, doi: 10.1016/j.marpolbul.2020.111921 Noomen M F, Skidmore A K. 2009. The effects of high soil CO2 concentrations on leaf reflectance of maize plants. International Journal of Remote Sensing, 30(2): 481–497, doi: 10.1080/01431160802339431 Nukapothula S, Wu Jie, Chen Chuqun, et al. 2021. Potential impact of the extensive oil spill on primary productivity in the Red Sea waters. Continental Shelf Research, 222: 104437, doi: 10.1016/j.csr.2021.104437 Nur A A, Mandang I, Mubarrok S, et al. 2018. The changes of water mass characteristics using 3-dimensional Regional Ocean Modeling System (ROMS) in Balikpapan bay, Indonesia. IOP Conference Series: Earth and Environmental Science, 162: 012006, doi: 10.1088/1755-1315/162/1/012006 Ozigis M S, Kaduk J D, Jarvis C H, et al. 2020. Detection of oil pollution impacts on vegetation using multifrequency SAR, multispectral images with fuzzy forest and random forest methods. Environmental Pollution, 256: 113360, doi: 10.1016/j.envpol.2019.113360 Peng Wenfu, Kuang Tingting, Tao Shuai. 2019. Quantifying influences of natural factors on vegetation NDVI changes based on geographical detector in Sichuan, western China. Journal of Cleaner Production, 233: 353–367, doi: 10.1016/j.jclepro.2019.05.355 Peng Wenfu, Wang Guangjie, Zhou Jieming, et al. 2016. Dynamic monitoring of fractional vegetation cover along Minjiang River from Wenchuan County to Dujiangyan City using multi-temporal landsat 5 and 8 images. Acta Ecologica Sinica, 36(7): 1975–1988 Peterson G D, Carpenter S R, Brock W A. 2003a. Uncertainty and the management of multistate ecosystems: an apparently rational route to collapse. Ecology, 84(6): 1403–1411, doi: 10.1890/0012-9658(2003)084[1403:UATMOM]2.0.CO;2 Peterson C H, Rice S D, Short J W, et al. 2003b. Long-term ecosystem response to the Exxon Valdez oil spill. Science, 302(5653): 2082–2086, doi: 10.1126/science.1084282 Prastyani R, Basith A. 2018. Utilisation of sentinel-1 SAR imagery for oil spill mapping: a case study of Balikpapan Bay oil spill. Journal of Geospatial Information Science and Engineering, 1(1): 22–26 Pu Ruiliang, Gong Peng, Yu Qian. 2008. Comparative analysis of EO-1 ALI and Hyperion, and Landsat ETM+ data for mapping forest crown closure and leaf area index. Sensors, 8(6): 3744–3766, doi: 10.3390/s8063744 Rajendran S, Vethamony P, Sadooni F N, et al. 2021. Detection of wakashio oil spill off Mauritius using sentinel-1 and 2 data: capability of sensors, image transformation methods and mapping. Environmental Pollution, 274: 116618, doi: 10.1016/j.envpol.2021.116618 Schlemmer M, Gitelson A, Schepers J, et al. 2013. Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. International Journal of Applied Earth Observation and Geoinformation, 25: 47–54, doi: 10.1016/j.jag.2013.04.003 Shu Y, Li J, Yousif H, et al. 2010. Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring. Remote Sensing of Environment, 114: 2026–2035, doi: 10.1016/j.rse.2010.04.009 Skrunes S, Brekke C, Eltoft T. 2014. Characterization of marine surface slicks by radarsat-2 multipolarization features. IEEE Transactions on Geoscience and Remote Sensing, 52(9): 5302–5319, doi: 10.1109/TGRS.2013.2287916 Somvanshi S S, Kumari M. 2020. Comparative analysis of different vegetation indices with respect to atmospheric particulate pollution using sentinel data. Applied Computing and Geosciences, 7: 100032, doi: 10.1016/j.acags.2020.100032 Topouzelis K, Singha S. 2017. Oil spill detection using space-borne sentinel-1 SAR imagery. In: Fingas M, ed. Oil Spill Science and Technology. 2nd ed. Amsterdam: Elsevier, 387–402 Vankayalapati K, Dasari H P, Langodan S, et al. 2023. Multi-mission satellite detection and tracking of October 2019 Sabiti oil spill in the Red Sea. Remote Sensing, 15(1): 38 Veefkind J P, Aben I, McMullan K, et al. 2012. TROPOMI on the ESA sentinel-5 precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sensing of Environment, 120: 70–83, doi: 10.1016/j.rse.2011.09.027 Yekeen S T, Balogun A L, Yusof K B W. 2020. A novel deep learning instance segmentation model for automated marine oil spill detection. ISPRS Journal of Photogrammetry and Remote Sensing, 167: 190–200, doi: 10.1016/j.isprsjprs.2020.07.011 Yin Liping, Zhang Min, Zhang Yuanling, et al. 2018. The long-term prediction of the oil-contaminated water from the Sanchi collision in the East China Sea. Acta Oceanologica Sinica, 37(3): 69–72, doi: 10.1007/s13131-018-1193-5 Zalik K R 2008. An efficient k-means clustering algorithm. Pattern Recognition Letters, 29, 1385–1391 Zhang Mei, Sun Xian, Xu Jilin. 2020. Heavy metal pollution in the East China Sea: a review. Marine Pollution Bulletin, 159: 111473, doi: 10.1016/j.marpolbul.2020.111473 Zhao Jun, Temimi M, Al Azhar M, et al. 2015. Satellite-based tracking of oil pollution in the Arabian gulf and the sea of Oman. Canadian Journal of Remote Sensing, 41(2): 113–125, doi: 10.1080/07038992.2015.1042543 Zheng Zihao, Yang Zhiwei, Wu Zhifeng, et al. 2019. Spatial variation of NO2 and its impact factors in China: an application of sentinel-5P products. Remote Sensing, 11(16): 1939, doi: 10.3390/rs11161939 -
点击查看大图
计量
- 文章访问数: 38
- HTML全文浏览量: 17
- 被引次数: 0
