Früher|Nächster

Optical extraction of oil spills based on sunglint reflection difference in HY-1C CZI images

  • role： First author第一作者
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210046, China

  • Email：ZHUxiaobo1992@gmaic.com
  • Introduction：朱小波，研究方向为海洋环境遥感。E-mail： ZHUxiaobo1992@gmaic.com
ZHU Xiaobo1,  
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210046, China

SHEN Yafeng1,  
  • Affiliation：

    National Satellite Ocean Application Center, Beijing 100081, China

    Key Laboratory of Space Ocean Remote Sensing and Applications, Ministry of Natural Resources, Beijing 100081, China

LIU Jianqiang23,  
  • Affiliation：

    National Satellite Ocean Application Center, Beijing 100081, China

    Key Laboratory of Space Ocean Remote Sensing and Applications, Ministry of Natural Resources, Beijing 100081, China

DING Jing23,  
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210046, China

JIAO Junnan1,  
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210046, China

JU Weimin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210046, China

  • Email：luyc@nju.edu.cn
  • Introduction：陆应诚，研究方向为海洋环境遥感。E-mail： luyc@nju.edu.cn
LU Yingcheng1*

Resümee

After an oil spill accident, it is essential to quickly detect the location, spatial coverage, pollution type, and amount of oil spills, so as to measure the impacts of different oil spill types, clean up the oil, and help the ocean recover. Although the mechanism and characteristics of optical remote sensing of oil spills have been basically clarified, the research on automatic oil spill extraction algorithms is still insufficient, which still needs to be addressed. The main challenge is that the significant variation of sunglint is helpful to the oil spill identification, but also brings many uncertainties to the extraction. Hence, the marine remote-sensing community is always committed to developing remote-sensing methods for improving the performance of aspects, such as preprocessing, segmentation, and classification. The scale of extraction is deemed the key to eliminating sunglint interference. As the conventional extraction method cannot be applied on the optical images directly for the reasons outlined in the context, a new man-machine interactive oil spill extraction method (more specific an oil–water mixture detector), which is able to eliminate sunglint interference was introduced. It accomplishes this by splitting the oil spill global region into adjacent sub-windows, as the sunglint can be considered constant in a small region. The proposed method and density-based clustering is used cooperatively in the method. The detector first discretizes different types of oil spills in images based on the specific optical detection principle of oil spills. Then the clustering uses the auxiliary information of multispectral images to achieve high confidence clustering output. The Coastal Zone Imager (CZI) onboard China’s HaiYang-1C/D (HY-1C/D) satellites can provide multispectral images with high spatial resolution and wide coverage for operational monitoring of oil spills. The proposed method was applied to HY/CZI oil spill dataset and other optical images of several oil spill events. The spatial differentiation of sunglint and remote sensing response characteristics of different oil spills in CZI were analyzed, and the accurate identification and extraction of oil spills in CZI images were realized. The results show that the optical remote sensing extraction method considering the variation of sunglint can effectively identify and extract the oil spills, and has good anti-interference ability. Based on testing of CZI data covering different areas, the method was proved to be effective in removing interference caused by sunglint, image interference (e.g. cloud, rough surface texture, ship wakes, illumination, shadowing), and so on. In addition, the method can further distinguish oil slicks and oil emulsions under the condition of weak sunglint, showing the ability to identify different oil spills, which can provide a reference for the operational application in oil spill monitoring. The results show stable variable-scale extraction accuracies of approximately 90.24% and 80.55% for oil slicks and oil emulsions, respectively. It is also applicable for the optical images with lower resolution, but the effect is inferior to that in CZI because of the influence of mixed pixels. As aforementioned, the accurate results are attributed to appropriate parameter adjustment under different spatial resolutions, oil spil types, and sunglint reflections. The satisfactory transfer applicability is mainly attributed to the variable-scale detector for sunglint reflection differences. In summary, the precondition for accurate oil spill extraction is to eliminate the sunglint interference, which depends on not only the sunglint model but also appropriate local scale, not global. Without a doubt, to what extent the coordination and utilization of sunglint and spectral information is the key to breakthrough for accurate oil spill extraction and quantification.

Schlüsselwort

HY-1C/D;sunglint reflection;oil spills;optical remote sensing;segmentation scale

References

  1. 1.
    Brekke C and Solberg A H S. 2005. Oil spill detection by satellite remote sensing. Remote Sensing of Environment, 95(1): 1-13
  2. 2.
    Clark R N, Swayze G A, Leifer I, Livo K E, Kokaly R, Hoefen T, Lundeen S, Eastwood M, Green R O, Pearson N, Sarture C, McCubbin I, Roberts D, Bradley E, Steele D, Ryan T, Dominguez R and The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team. 2010. A Method for Quantitative Mapping of Thick Oil Spills Using Imaging Spectroscopy//Open-File Report 2010—1167. SurveyUGeological.S.: 1-51.
  3. 3.
    Ester M, Kriegel H P, Sander J and Xu X W. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise//Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press: 226-231
  4. 4.
    Hu C M, Feng L, Holmes J, Swayze G A, Leifer I, Melton C, Garcia O, MacDonald I, Hess M, Muller-Karger F, Graettinger G and Green R. 2018. Remote sensing estimation of surface oil volume during the 2010 Deepwater Horizon oil blowout in the Gulf of Mexico: scaling up AVIRIS observations with MODIS measurements. Journal of Applied Remote Sensing, 12(2): 026008
  5. 5.
    Hu C M, Li X F, Pichel W G and Muller-Karger F E. 2009. Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery. Geophysical Research Letters, 36(1): L01604
  6. 6.
    Hu C M, Lu Y C, Sun S J and Liu Y X. 2021. Optical remote sensing of oil spills in the ocean: what is really possible? Journal of Remote Sensing, 2021: 9141902
  7. 7.
    Jackson C R and Alpers W. 2010. The role of the critical angle in brightness reversals on sunglint images of the sea surface. Journal of Geophysical Research: Oceans, 115(C9): C09019
  8. 8.
    Kessler J D, Valentine D L, Redmond M C, Du M R, Chan E W, Mendes S D, Quiroz E W, Villanueva C J, Shusta S S, Werra L M, Yvon-Lewis S A and Weber T C. 2011. A persistent oxygen anomaly reveals the fate of spilled methane in the deep Gulf of Mexico. Science, 331(6015): 312-315
  9. 9.
    Kolokoussis P and Karathanassi V. 2018. Oil spill detection and mapping using sentinel 2 imagery. Journal of Marine Science and Engineering, 6(1): 4
  10. 10.
    Kvenvolden K A and Cooper C K. 2003. Natural seepage of crude oil into the marine environment. Geo-Marine Letters, 23(3/4): 140-146
  11. 11.
    Leifer I, Lehr W J, Simecek-Beatty D, Bradley E, Clark R, Dennison P, Hu Y X, Matheson S, Jones C E, Holt B, Reif M, Roberts D A, Svejkovsky J, Swayze G and Wozencraft J. 2012. State of the art satellite and airborne marine oil spill remote sensing: application to the BP Deepwater Horizon oil spill. Remote Sensing of Environment, 124: 185-209
  12. 12.
    Lu Y C, Hu C M, Sun S J, Zhang M W, Zhou Y, Shi J and Wen Y S. 2016. Overview of optical remote sensing of marine oil spills and hydrocarbon seepage. Journal of Remote Sensing, 20(5): 1259-1269
  13. 13.
    Lu Y C, Li X, Tian Q J and Han W C. 2012. An optical remote sensing model for estimating oil slick thickness based on two-beam interference theory. Optics Express, 20(22): 24496-24504
  14. 14.
    Lu Y C, Li X, Tian Q J, Zheng G, Sun S J, Liu Y X and Yang Q. 2013. Progress in marine oil spill optical remote sensing: detected targets, spectral response characteristics, and theories. Marine Geodesy, 36(3): 334-346
  15. 15.
    Lu Y C, Liu J Q, Ding J, Shi J, Chen J Y and Ye X M. 2019. Optical remote identification of spilled oils from the SANCHI oil tanker collision in the East China Sea. Chinese Science Bulletin, 64(31): 3213-3222
  16. 16.
    Lu Y C, Shi J, Hu C M, Zhang M W, Sun S J and Liu Y X. 2020. Optical interpretation of oil emulsions in the ocean – part II: applications to multi-band coarse-resolution imagery. Remote Sensing of Environment, 242: 111778
  17. 17.
    Lu Y C, Shi J, Wen Y S, Hu C M, Zhou Y, Sun S J, Zhang M W, Mao Z H and Liu Y X. 2019. Optical interpretation of oil emulsions in the ocean – part I: laboratory measurements and proof-of-concept with AVIRIS observations. Remote Sensing of Environment, 230: 111183
  18. 18.
    Lu Y C, Sun S J, Zhang M W, Murch B and Hu C M. 2016a. Refinement of the critical angle calculation for the contrast reversal of oil slicks under sunglint. Journal of Geophysical Research: Oceans, 121(1): 148-161
  19. 19.
    Lu Y C, Tian Q J and Li X. 2011. The remote sensing inversion theory of offshore oil slick thickness based on a two-beam interference model. Science China: Earth Sciences, 54(5): 678-685
  20. 20.
    Lu Y C, Tian Q J, Song P F and Li S S. 2009. Study on extraction methods of offshore oil stick by hyperspectral remote sensing. Journal of Remote Sensing, 13(4): 686-695
  21. 21.
    Lu Y C, Tian Q J, Wang J J, Wang X C and Qi X P. 2008. Experimental study on spectral responses of offshore oil slick. Chinese Science Bulletin, 53(24): 3937-3941
  22. 22.
    Lu Y C, Zhan W F and Hu C M. 2016b. Detecting and quantifying oil slick thickness by thermal remote sensing: a ground-based experiment. Remote Sensing of Environment, 181: 207-217
  23. 23.
    Mariano A J, Kourafalou V H, Srinivasan A, Kang H, Halliwell G R, Ryan E H and Roffer M. 2011. On the modeling of the 2010 Gulf of Mexico oil spill. Dynamics of Atmospheres and Oceans, 52(1/2): 322-340
  24. 24.
    Mou L, Zou H P, Wu S Q, Song J, Li H, Xu L L and Zhao R X. 2011. Numerical model research on the ocean oil spill. Marine Science Bulletin, 30(4): 473-480
  25. 25.
    Shen Y F, Liu J Q, Ding J, Jiao J N, Sun S J and Lu Y C. 2020. HY-1C COCTS and CZI observation of marine oil spills in the South China Sea. Journal of Remote Sensing, 24(8): 933-944
  26. 26.
    Shi J, Jiao J N, Lu Y C, Zhang M W, Mao Z H and Liu Y X. 2018. Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 251-259
  27. 27.
    Sun S J and Hu C M. 2019. The challenges of interpreting oil–water spatial and spectral contrasts for the estimation of oil thickness: examples from satellite and airborne measurements of the Deepwater Horizon oil spill. IEEE Transactions on Geoscience and Remote Sensing, 57(5): 2643-2658
  28. 28.
    Sun S J and Hu C M. 2016. Sun glint requirement for the remote detection of surface oil films. Geophysical Research Letters, 43(1): 309-316
  29. 29.
    Sun S J, Hu C M, Feng L, Swayze G A, Holmes J, Graettinger G, MacDonald I, Garcia O and Leifer I. 2016. Oil slick morphology derived from AVIRIS measurements of the Deepwater Horizon oil spill: implications for spatial resolution requirements of remote sensors. Marine Pollution Bulletin, 103(1/2): 276-285
  30. 30.
    Sun S J, Hu C M, Garcia-Pineda O, Kourafalou V, Le Hénaff M and Androulidakis Y. 2018a. Remote sensing assessment of oil spills near a damaged platform in the Gulf of Mexico. Marine Pollution Bulletin, 136: 141-151
  31. 31.
    Sun S J, Hu C M and Tunnell Jr J W. 2015. Surface oil footprint and trajectory of the Ixtoc-I oil spill determined from Landsat/MSS and CZCS observations. Marine Pollution Bulletin, 101(2): 632-641
  32. 32.
    Sun S J, Lu Y C, Liu Y X, Wang M Q and Hu C M. 2018b. Tracking an oil tanker collision and spilled oils in the East China Sea using multisensor day and night satellite imagery. Geophysical Research Letters, 45(7): 3212-3220
  33. 33.
    Suo Z Y, Lu Y C, Liu J Q, Ding J, Yin D Y, Xu F F and Jiao J N. 2021. Ultraviolet remote sensing of marine oil spills: a new approach of Haiyang-1C satellite. Optics Express, 29(9): 13486-13495
  34. 34.
    Wen Y S, Wang M Q, Lu Y C, Sun S J, Zhang M W, Mao Z H, Shi J and Liu Y X. 2018. An alternative approach to determine critical angle of contrast reversal and surface roughness of oil slicks under sunglint. International Journal of Digital Earth, 11(9): 972-979
  35. 35.
    Wettle M, Daniel P J, Logan G A and Thankappan M. 2009. Assessing the effect of hydrocarbon oil type and thickness on a remote sensing signal: a sensitivity study based on the optical properties of two different oil types and the HYMAP and Quickbird sensors. Remote Sensing of Environment, 113(9): 2000-2010
  36. 36.
    Zhong Z X and You F Q. 2011. Oil spill response planning with consideration of physicochemical evolution of the oil slick: a multi objective optimization approach. Computers and Chemical Engineering, 35(8): 1614-1630
  37. 37.
    Zhu X B, Lu Y C, Liu J Q, Ju W M, Ding J, Li M C, Suo Z Y, Jiao J J and Wang L F. 2022. Optical Extraction of Oil Spills from Satellite Images under Different Sunglint Reflections. IEEE Transactions on Geoscience and Remote Sensing.

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website