Study of relationship between thickness of oil slicks and band reflectance of Landsat TM/ETM

doi:10.6046/gtzyyg.2019.04.10

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Abstract

Oil slick thickness is a key parameter in estimating oil spill volume. In order to determine the feasibility of detecting oil slick thickness on water surface by Landsat TM/ETM remote sensing, the authors used heavy crude oil, light crude oil, diesel oil and gasoline as experimental oils, quartz brine as a simulated solar light source, ASD Field Spec 3 portable spectrometer as a detection instrument, and carried out oil film simulation with different thicknesses and pectral measurement experiments. By calculating the correlation coefficient of oil slick thickness and its reflectance in the range from 350 to 2 500 nm, four characteristic response spectra for heavy crude oil, seven characteristic response spectra for light crude oil, six characteristic response spectra for diesel oil and four characteristic response spectra for gasoline were determined. For Landsat TM/ETM data,characteristic multispectral indices were found by scatter plot of oil slicks thickness-multispectral indices (band reflectance and band ratio), and oil slick thickness estimation model was established. For heavy crude oil, band B4 and band ratio B4/B5 are better spectral indices; for light oil, band ratio B1/B2 and B1/B3 are good spectral indicators; for diesel fuel, band B1, B2 and band ratio B1/B2, B1/B3, B1/B4, B2/B3, B2/B4 and B3/B4 are good spectral indicators; for gasoline, all spectral indicators have segmentation characteristics, with no good spectral indicators. The results show that the Landsat TM/ETM has the capability of detecting the oil slick thickness of heavy crude oil, light crude oil and diesel oil on the water surface, and thus can be used to estimate the oil spill volume on the water surface.

Keywords oil slick thickness spectral reflection correlation coefficient curve fitting Landsat TM/ETM data

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Issue Date: 03 December 2019

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	Xuewen XING
	Song LIU
	Kaijun QIAN

Cite this article:

Xuewen XING,Song LIU,Kaijun QIAN. Study of relationship between thickness of oil slicks and band reflectance of Landsat TM/ETM[J]. Remote Sensing for Land & Resources, 2019, 31(4): 69-78.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.04.10 OR https://www.gtzyyg.com/EN/Y2019/V31/I4/69

Fig.1 Reflectance spectra of heavy crude 0:1, light crude 0:1, diesel 0:1 and gasoline slicks in 350~2 500 nm

Fig.2 Relativity of oil slick thickness and reflectance

Fig.3 Estimation model of heavy crude oil slick thickness-TM band reflectance

Fig.4 Estimation model of light crude oil slick thickness-TM band reflectance

Fig.5 Estimation model of diesel oil slick thickness-TM band reflectance

Fig.6 Estimation model of gasoline slick thickness-TM band reflectance

Fig.7 Estimation model of heavy crude oil slick thickness-TM band ratio

Fig.8 Estimation model of light crude oil slick thickness-TM band ratio

Fig.9 Estimation model of diesel oil slick thickness-TM band ratio

Fig.10 Estimation model of gasoline slick thickness-TM band ratio

[1]	宋莎莎, 安伟, 李建伟 , 等. 海上溢油量评估方法研究综述[J]. 海岸工程, 2017,36(1):83-88.
[1]	Song S S, An W, Li J W , et al. Review on the methods for assessment of marine oil spill volume[J]. Coastal Engineering, 2017,36(1):83-88.
[2]	赵冬至, 丛丕福 . 海面溢油的可见光波段地物光谱特征研究[J]. 遥感技术与应用, 2000,15(3):160-164.
[2]	Zhao D Z, Cong P F . The research of visual light wave-band feature spectrum of sea-surface oil spill[J]. Remote Sensing Technology and Application, 2000,15(3):160-164.
[3]	Svejkovsky J, Muskat J . Real-Time Detection of Oil Slick Thickness Patterns with a Portable Multispectral Sensor[R].U.S.Minerals Management Service Contract 0105CT39144 2006.
[4]	Svejkovsky J, Muskat J, Mullin J. Mapping oil spill thickness with a portable multispectral aerial imager [C]//International Oil Spill Conference Proceedings, 2008: 131-136.
[5]	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.
[6]	张永宁, 丁倩, 高超 , 等. 油膜波谱分析与遥感监测溢油[J]. 海洋环境科学, 2000,19(3):5-10.
[6]	Zhang Y N, Ding Q, Gao C , et al. Analysis of oil film spectrum and monitoring oil spilled by remote sensing[J]. Marine Environment Science, 2000,19(3):5-10.
[7]	肖剑伟, 田庆久 . 基于生物光学模型的水面薄油膜厚度的高光谱遥感反演实验研究[J]. 光谱学与光谱分析, 2012,32(1):183-187. url: http://www.opticsjournal.net/Articles/Abstract?aid=OJ120220000134C9FbHe
[7]	Xiao J W, Tian Q J . Experimental study of offshore oil thickness hyperspectral inversion based on bio-optical model[J]. Spectroscopy and Spectral Analysis, 2012,32(1):183-187.
[8]	叶舟, 刘力, 魏传新 , 等. 多种薄油膜光谱反射率特性外场测试方法及结果分析[J]. 光谱学与光谱分析, 2015,35(6):1695-1699. url: http://www.opticsjournal.net/Articles/Abstract?aid=OJ150611000049t1w3z6
[8]	Ye Z, Liu L, Wei C X , et al. Experimental methods and result analysis of a variety of spectral reflectance properties of the thin oil film[J]. Spectroscopy and Spectral Analysis, 2015,35(6):1695-1699.
[9]	刘旭拢, 邓孺孺, 秦雁 , 等. 水面浮油膜光谱测量及光谱特征分析[J]. 海洋科学, 2016,40(10):63-70.
[9]	Liu X L, Deng R R, Qin Y , et al. Spectral measurement and characteristic analysis of an oil film floating above water[J]. Marine Sciences, 2016,40(10):63-70.
[10]	孙鹏, 宋梅萍, 安居白 . 基于光谱曲线响应特性的油膜厚度估计模型分析[J]. 光谱学与光谱分析, 2013,33(7):1881-1885. url: http://www.opticsjournal.net/Articles/Abstract?aid=OJ1309300001270FbIeL
[10]	Sun P, Song M P, An J B . Study of prediction for oil thickness based on spectral curve[J]. Spectroscopy and Spectral Analysis, 2013,33(7):1881-1885.
[11]	臧影 . 高光谱溢油图像波段选择在油膜厚度估算中的应用[D]. 大连:大连海事大学, 2010.
[11]	Zang Y . Application of Hyperspectral Band Selection in Detection of Oil Slick Thickness[D]. Dalian:Dalian Maritime University, 2010.
[12]	兰国新 . 海上溢油遥感光谱信息挖掘与应用研究[D]. 大连:大连海事大学, 2012.
[12]	Lan G X . Study on Spectral Information Mining and Application for Oil Spill Remote Sensing Monitoring[D]. Dalian:Dalian Maritime University, 2012.
[13]	刘丙新, 李颖, 张至达 , 等. 不同厚度海上油膜高光谱遥感波段敏感性研究[J]. 东北师大学报(自然科学版), 2015,47(4):156-160.
[13]	Liu B X, Li Y, Zhang Z D , et al. Study on the sensitivity of hyperspectral imagery to detect oil film with different thickness[J]. Journal of Northeast Normal University (Natural Science Edition), 2015,47(4):156-160.
[14]	邢学文, 刘松, 许德刚 , 等. 基于偏最小二乘法的高光谱水面油膜厚度估算[J]. 国土资源遥感, 2019,31(2):111-117.doi: 10.6046/gtzyyg.2019.02.16.
[14]	Xing X W, Liu S, Xu D G , et al. Thickness estimation of crude oil slick by hyperspectral data based on partial least squares regression method[J]. Remote Sensing for Land and Resources, 2019,31(2):111-117.doi: 10.6046/gtzyyg.2019.02.16.
[15]	Winkelmann K . On the Applicability of Imaging Spectrometry for the Detection and Investigation of Contaminated Sites with Particular Consideration Given to the Detection of Fuel Hydrocarbon Contaminants in Soil[D]. Berlin:BTU Cottbus, 2005.

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