Multi-remote sensing of spilled oils from A Symphony tanker collision in the Yellow Sea
-
摘要: 溢油是海洋生态环境监测的重点对象。合成孔径雷达、光学遥感与热红外遥感等卫星技术开展海洋溢油监测的机理已得到阐明，发挥多源遥感的技术特点和应用优势，实现海洋溢油的精准监测与量化评估，为海洋环境保护提供重要的技术支撑。2021年4月27日巴拿马籍“义海”轮与利比亚籍“交响乐”油轮在青岛外海发生碰撞，导致约
9400 t船载货油泄漏入海。本文利用多源卫星遥感数据，监测并分析了该事故海域的溢油污染覆盖状况及其乳化溢油分布特征。基于溢油多源遥感响应机理与响应特征，优化了多源卫星遥感数据的处理流程，实现了溢油覆盖区域的识别与多种溢油污染类型的分类。结果表明：2021年5月1日至5月22日，“交响乐”轮溢油污染事件累积溢油像元覆盖面积为2368.7 km2，其中乳化溢油像元覆盖面积为1019.3 km2，乳化油面积占比达43.0%，单日最大溢油像元面积达734 km2；多源遥感监测结果可以互为验证，光学遥感更具备识别不同溢油污染的能力，其中乳化溢油代表了污染危害的关键所在，从而提高了海洋溢油污染的监测评估精度，为溢油污染事件的危害评估与精细化监测提供可靠的技术与方法参考。Abstract: Oil spill is one of the critical target of marine environmental monitoring. Synthetic Aperture Radar (SAR), thermal infrared remote sensing, and optical remote sensing for monitoring of marine oil spills have been elucidated, and it is crucial for marine environmental protection to utilize the features and advantages of multi-source remote sensing to achieve accurate monitoring and quantitative assessment of marine oil spills. On April 27, 2021, the collision between the Panamanian vessel Sea Justice and the Liberian oil tanker A Symphony resulted in an estimated 9400 t of cargo oil seeping into the sea. Here, we monitor and analyze the oil spill pollution coverage and emulsified oil spill characteristics in this accident using multi-source satellite remote sensing data. Based on the response mechanism and characteristics of oil spill multi-source remote sensing, the processing of multi-source data is optimized to realize the identification of oil spill and the classification of multiple oil spill types. The findings indicate that from May 1 to May 22, 2021, the cumulative pixel coverage area of oil spills from A Symphony tanker was2368.7 km2, of which the emulsified oil pixel coverage area was1019.3 km2, accounting for 43.0%. The maximum daily oil spill pixel area reached 734 km2. The results of multi-remote sensing monitoring mutually validated each other, and optical remote sensing is more capable of identifying different oil spill pollution, in which the emulsified oil represents the key of pollution hazards. It improves the accuracy of monitoring and assessment of marine oil spill pollution, and provides reliable technical and methodological references for the hazard assessment and refined monitoring of oil pollution events. -
图 1 “交响乐”轮溢油事件研究区域
a. “交响乐”轮碰撞点及溢油污染最大覆盖范围；b. 5月1日13时18分研究区域内HY-1D CZI传感器观测结果, 研究区域内首次发现溢油痕迹；c. 5月25日10时41分研究区域内HY-1 C CZI传感器观测结果, 研究区域内已无明显溢油；d，e. 分别对应图b和c中白色虚线区域，d为海面低风速区，e可观察到“交响乐”轮及明显的海面溢油
Fig. 1 Study area for A Symphony tanker collision event
a. Collision point of A Symphony tanker and maximum coverage of oil spill pollution; b. the HY-1D CZI image at 13:18 on 1 May, and oil spill traces were first discovered in the study area; c. the HY-1C CZI image at 10:41 on 25 May, and no apparent oil spill was observed in the study area; d and e correspond to the white dashed areas in b and c, respectively; d is a low-wind zone on the sea surface; e shows A Symphony tanker and the obvious oil spill traces
图 2 2021年4月27日至5月22日“交响乐”轮碰撞事件时间线及多源卫星遥感数据
Fig. 2 Timeline of A Symphony tanker collision event from April 27 to May 22, 2021 with corresponding multi- remote sensing data
图 3 多源遥感数据溢油定量监测处理流程
Fig. 3 Flow chart for quantitative oil spill monitoring of multi-source remote sensing data
图 4 光学遥感数据在不同耀光条件下的溢油响应特征
a1、b1、c1. 5月21日准同步Sentinel-2（S2A） MSI（R：655 nm,，G：560 nm，B：492 nm），HJ-2（R：660 nm，G：560 nm，B：470 nm），HY-1 C（R：650 nm，G：560 nm，B：460 nm）光学真彩色合成数据；a2、b2、c2. 不同传感器油膜反射率特征差异；a3、b3、c3. 溢油光学识别分类结果
Fig. 4 Oil spill response characteristics of optical remote sensing under different sunglint conditions
a1, b1, c1. Quasi-synchronous Sentinel-2 (S2A) MSI (R: 655 nm, G: 560 nm, B: 492 nm), HJ-2 (R: 660 nm, G: 560 nm, B: 470 nm) and HY-1C (R: 650 nm, G: 560 nm, B: 460 nm) optical true-color composite data from May 21; a2, b2, c2. differences in reflectance characteristics of oil films for different sensors; a3, b3, c3. optical identification and classification of oil spill
图 5 多源遥感数据的光学与SAR溢油响应特征
a1、b1、c1、d1. 5月2日准同步HY-1 D（R：650 nm，G：560 nm，B：460 nm），HJ-2（R：660 nm，G：560 nm，B：470nm），GF-1（R：660 nm，G：555 nm，B：485 nm）光学真彩色合成数据及GF-3 SAR数据；a2、b2、c2、d2. 溢油识别分类结果
Fig. 5 Oil spill response characteristics of optical remote sensing and SAR
a1, b1, c1, d1. Quasi-simultaneous HY-1D (R: 650 nm, G: 560 nm, B: 460 nm), HJ-2 (R: 660 nm, G: 560 nm, B: 470 nm) and GF-1 (R: 660 nm, G: 555 nm, B: 485 nm) optical true-color composites and GF-3 on May 2 SAR data; a2, b2, c2, d2. identification and classification of oil spill in a1, b1, c1, d1
图 6 Landsat 8 光学数据及热红外数据的溢油响应特征
a1. 5月13日Landsat 8 OLI（R：655 nm，G：563 nm，B：483 nm）光学真彩色合成数据；b1. 5月22日Landsat 8 OLI光学真彩色合成数据；c1. 5月13日Landsat 8 TIRS热红外数据；d1. 5月22日Landsat 8 TIRS热红外数据；a2、b2、c2、d2. 溢油识别分类结果
Fig. 6 Oil spill response characteristics of Landsat 8 optical and thermal infrared data
a1. Landsat 8 OLI (R: 655 nm, G: 563 nm, B: 483 nm) optical true-color composite data on 13 May; b1. Landsat 8 OLI optical true-color composite data on 22 May; c1. Landsat 8 TIRS thermal infrared data on 13 May; d1. Landsat 8 TIRS thermal infrared data on 22 May; a2, b2, c2, d2. identification and classification of oil spill in a1, b1, c1, d1
图 7 基于多源遥感数据的识别分类结果
Fig. 7 Identification and classification of oil spill based on multi-remote sensing data
图 8 “交响乐”轮溢油像元面积统计结果
Fig. 8 Statistical results of pixel area of oil spill for A Symphony tanker
图 9 a. Sentinel-2 MSI光学真彩色合成数据及采样点分布；b. 光谱响应特征；c. 溢油识别提取结果；d. 乳化油归一化浓度估算结果
Fig. 9 a. Optical true-color composite of Sentinel-2 MSI and sampling sites distribution; b. spectral response characteristics; c. Identification and classification of oil spill; d. the estimation of emulsified normalized oil concentration
-
[1] Crone T J, Tolstoy M. Magnitude of the 2010 Gulf of Mexico oil leak[J]. Science, 2010, 330(6004): 634. doi: 10.1126/science.1195840 [2] Leifer I, Lehr W J, Simecek-Beatty D, et al. State of the art satellite and airborne marine oil spill remote sensing: application to the BP Deepwater Horizon oil spill[J]. Remote Sensing of Environment, 2012, 124: 185−209. doi: 10.1016/j.rse.2012.03.024 [3] 曹阳. 海上油污损害的救济途径研究——以墨西哥湾漏油事件为例[D]. 大连: 大连海事大学, 2014.Cao Yang. On remedies of ocean oil damage—A case study of the Gulf of Mexico oil spill[D]. Dalian: Dalian Maritime University, 2014. [4] Dong Yanzhu, Liu Yongxue, Hu Chuanmin, et al. Chronic oiling in global oceans[J]. Science, 2022, 376(6599): 1300−1304. doi: 10.1126/science.abm5940 [5] Kvenvolden K A, Cooper C K. Natural seepage of crude oil into the marine environment[J]. Geo-Marine Letters, 2003, 23(3): 140−146. [6] Zhong Zhixia, You Fengqi. Oil spill response planning with consideration of physicochemical evolution of the oil slick: a multiobjective optimization approach[J]. Computers & Chemical Engineering, 2011, 35(8): 1614−1630. [7] Lu Yingcheng, Tian Qingjiu, Wang Xinyuan, et al. Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China[J]. International Journal of Digital Earth, 2013, 6(1): 76−93. doi: 10.1080/17538947.2012.695404 [8] 马媛, 高振会, 杨应斌, 等. 海上石油开采导致生态环境变化实例研究[J]. 海洋学报, 2005, 27(5): 54−59. doi: 10.3321/j.issn:0253-4193.2005.05.008Ma Yuan, Gao Zhenhui, Yang Yingbin, et al. Eco-environment changes in the Chengbei Oil Field due to the marine petroleum exploration[J]. Hauyang Xuebao, 2005, 27(5): 54−59. doi: 10.3321/j.issn:0253-4193.2005.05.008 [9] 沈南南, 李纯厚, 王晓伟. 石油污染对海洋浮游生物的影响[J]. 生物技术通报, 2006(S1): 95−99. doi: 10.3969/j.issn.1002-5464.2006.z1.021Shen Nannan, Li Chunhou, Wang Xiaowei. Effects of oil pollution on marine planktons[J]. Biotechnology Bulletin, 2006(S1): 95−99. doi: 10.3969/j.issn.1002-5464.2006.z1.021 [10] Fingas M F, Brown C E. Review of oil spill remote sensing[J]. Spill Science & Technology Bulletin, 1997, 4(4): 199−208. [11] Brekke C, Solberg A H S. Oil spill detection by satellite remote sensing[J]. Remote Sensing of Environment, 2005, 95(1): 1−13. doi: 10.1016/j.rse.2004.11.015 [12] Zhang Biao, Perrie W, Li Xiaofeng, et al. Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image[J]. Geophysical Research Letters, 2011, 38(10): L10602. [13] Li Chenglei, Kim D J, Park S, et al. A self-evolving deep learning algorithm for automatic oil spill detection in Sentinel-1 SAR images[J]. Remote Sensing of Environment, 2023, 299: 113872. doi: 10.1016/j.rse.2023.113872 [14] Salisbury J W, D'Aria D M, Sabins Jr F F. Thermal infrared remote sensing of crude oil slicks[J]. Remote Sensing of Environment, 1993, 45(2): 225−231. doi: 10.1016/0034-4257(93)90044-X [15] Tseng W Y, Chiu L S. AVHRR observations of Persian Gulf oil spills[C]//Proceedings of IGARSS'94-1994 IEEE International Geoscience and Remote Sensing Symposium. Pasadena: IEEE, 1994: 779−782. [16] Svejkovsky J, Lehr W, Muskat J, et al. Operational utilization of aerial multispectral remote sensing during oil spill response[J]. Photogrammetric Engineering & Remote Sensing, 2012, 78(10): 1089−1102. [17] Shih W C, Andrews A B. Modeling of thickness dependent infrared radiance contrast of native and crude oil covered water surfaces[J]. Optics Express, 2008, 16(14): 10535−10542. doi: 10.1364/OE.16.010535 [18] Lu Yingcheng, Zhan Wenfeng, Hu Chuanmin. Detecting and quantifying oil slick thickness by thermal remote sensing: a ground-based experiment[J]. Remote Sensing of Environment, 2016, 181: 207−217. doi: 10.1016/j.rse.2016.04.007 [19] Jiao Junnan, Lu Yingcheng, Hu Chuanmin, et al. Quantifying ocean surface oil thickness using thermal remote sensing[J]. Remote Sensing of Environment, 2021, 261: 112513. doi: 10.1016/j.rse.2021.112513 [20] 刘建强. 全球海面油膜遥感[J]. 科学通报, 2022, 67(33): 3897−3899. doi: 10.1360/TB-2022-0692Liu Jianqiang. Remote sensing of oil slicks in global oceans[J]. Chinese Science Bulletin, 2022, 67(33): 3897−3899. doi: 10.1360/TB-2022-0692 [21] 刘建强, 陆应诚, 丁静, 等. 中国海洋水色业务卫星揭示我国近海溢油污染状况[J]. 科学通报, 2022, 67(33): 3997−4008. doi: 10.1360/TB-2021-0992Liu Jianqiang, Lu Yingcheng, Ding Jing, et al. Oil spills in China Seas revealed by the national ocean color satellites[J]. Chinese Science Bulletin, 2022, 67(33): 3997−4008. doi: 10.1360/TB-2021-0992 [22] Sun Shaojie, Lu Yingcheng, Liu Yongxue, et al. Tracking an oil tanker collision and spilled oils in the East China Sea using multisensor day and night satellite imagery[J]. Geophysical Research Letters, 2018, 45(7): 3212−3220. doi: 10.1002/2018GL077433 [23] 陆应诚, 刘建强, 丁静, 等. 中国东海“桑吉”轮溢油污染类型的光学遥感识别[J]. 科学通报, 2019, 64(31): 3213−3222.Lu Yingcheng, Liu Jianqiang, Ding Jing, et al. Optical remote identification of spilled oils from the SANCHI oil tanker collision in the East China Sea[J]. Chinese Science Bulletin, 2019, 64(31): 3213−3222. [24] Zhu Xiaobo, Lu Yingcheng, Liu Jianqiang, et al. Optical extraction of oil spills from satellite images under different sunglint reflections[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4210714. [25] Lu Yingcheng, Shi Jing, Wen Yansha, et al. Optical interpretation of oil emulsions in the ocean–Part I: laboratory measurements and proof-of-concept with AVIRIS observations[J]. Remote Sensing of Environment, 2019, 230: 111183. doi: 10.1016/j.rse.2019.05.002 [26] Lu Yingcheng, Shi Jing, Hu Chuanmin, et al. Optical interpretation of oil emulsions in the ocean–Part II: applications to multi-band coarse-resolution imagery[J]. Remote Sensing of Environment, 2020, 242: 111778. doi: 10.1016/j.rse.2020.111778 [27] Hu Chuanmin, Lu Yingcheng, Sun Shaojie, et al. Optical remote sensing of oil spills in the ocean: what is really possible?[J]. Journal of Remote Sensing, 2021, 9141902. [28] Jiao Junnan, Lu Yingcheng, Hu Chuanmin. Optical interpretation of oil emulsions in the ocean-Part III: a three-dimensional unmixing model to quantify oil concentration[J]. Remote Sensing of Environment, 2023, 296: 113719. doi: 10.1016/j.rse.2023.113719 [29] Lu Yingcheng, Zhou Yang, Liu Yongxue, et al. Using remote sensing to detect the polarized sunglint reflected from oil slicks beyond the critical angle[J]. Journal of Geophysical Research: Oceans, 2017, 122(8): 6342−6354. doi: 10.1002/2017JC012793 [30] Hu Chuanmin, Li Xiaofeng, Pichel W G, et al. Detection of natural oil slicks in the NW Gulf of Mexico using MODIS imagery[J]. Geophysical Research Letters, 2009, 36(1): L01604. [31] Jackson C R, Alpers W. The role of the critical angle in brightness reversals on sunglint images of the sea surface[J]. Journal of Geophysical Research: Oceans, 2010, 115(C9): C09019. [32] Wettle M, Daniel P J, Logan G A, et al. 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[J]. Remote Sensing of Environment, 2009, 113(9): 2000−2010. doi: 10.1016/j.rse.2009.05.010 [33] Kukhtarev N, Kukhtareva T, Gallegos S C. Holographic interferometry of oil films and droplets in water with a single-beam mirror-type scheme[J]. Applied Optics, 2011, 50(7): B53−B57. doi: 10.1364/AO.50.000B53 [34] Zhou Yang, Lu Yingcheng, Shen Yafeng, et al. Polarized remote inversion of the refractive index of marine spilled oil from PARASOL images under sunglint[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4): 2710−2719. doi: 10.1109/TGRS.2019.2953640 [35] Lu Yingcheng, Li Xiang, Tian Qingjiu, et al. Progress in marine oil spill optical remote sensing: detected targets, spectral response characteristics, and theories[J]. Marine Geodesy, 2013, 36(3): 334−346. doi: 10.1080/01490419.2013.793633 [36] Clark R N, Swayze G A, Leifer I, et al. A Method for Quantitative Mapping of Thick Oil Spills using Imaging Spectroscopy[R]. Denver: U. S. Geological Survey, 2010. [37] Kirillov A, Mintun E, Ravi N, et al. Segment anything[C]//Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Paris: IEEE, 2023: 3992−4003. [38] Ma Jun, He Yuting, Li Feifei, et al. Segment anything in medical images[J]. Nature Communications, 2024, 15(1): 654. doi: 10.1038/s41467-024-44824-z [39] Bonn A. Bonn agreement aerial operations handbook, 2016[R]. London: Bonn Agreement, 2016. -
下载:
计量
- 文章访问数: 116
- HTML全文浏览量: 54
- PDF下载量: 22
- 被引次数: 0
