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The Challenges of Remotely Measuring Oil Slick Thickness
Open AccessArticle

Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images

1
Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China
2
College of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450046, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 421; https://doi.org/10.3390/rs10030421
Received: 5 December 2017 / Revised: 27 January 2018 / Accepted: 7 March 2018 / Published: 9 March 2018
(This article belongs to the Special Issue Oil Spill Remote Sensing)
It is important to detect floating oil slicks after spill accidents, and hyperspectral remote sensing technology is capable of achieving this task. Traditional methods mainly utilize the spectral indices of hydrocarbons to detect floating oil slicks, but are poor at distinguishing the thickness of oil slicks and cannot detect sheens. Since the spectra of oil slicks should be affected by seawater as well as oil, this paper investigated the use of spectral indices of hydrocarbons and seawater to identify different thicknesses of oil slicks. In this research, a measurement, called index separability (IS), was proposed for quantitatively evaluating the identification ability of these spectral indices. Based on the evaluation results, experiments were conducted to validate the applicability of these spectral indices. The results show that the spectral indices of hydrocarbons are more suitable for detecting continuous true color oil slicks and emulsions and that spectral indices of seawater are more suitable for sheens and seawater. In addition, the spectral indices of hydrocarbons and seawater are complementary for detecting oil slicks. Finally, combining the spectral indices of hydrocarbons and seawater is conducive to achieving more accurate oil slick recognition results. View Full-Text
Keywords: oil slick; hyperspectral remote sensing; spectral indices; evaluation measurement oil slick; hyperspectral remote sensing; spectral indices; evaluation measurement
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MDPI and ACS Style

Zhao, D.; Cheng, X.; Zhang, H.; Niu, Y.; Qi, Y.; Zhang, H. Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images. Remote Sens. 2018, 10, 421.

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