Oil Spill Detection, Identification, and Tracing [1 ed.] 0443137781, 9780443137785

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OIL SPILL DETECTION, IDENTIFICATION, AND TRACING YING LI

Professor, Dalian Maritime University, China

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright Ó 2024 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-443-13778-5

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Preface The ocean occupies more than 70 percent of the earth’s surface and contains rich mineral resources. It is said that the ocean is the cradle of life and also a huge blue space for the sustainable survival and development of human beings. Many countries in the world attach great importance to safeguarding maritime rights and interests. As the main power source of maritime transportation and marine engineering, the increasing use of petroleum also increases the risk of oil spills from ships, ports, channels, and drilling platforms. The occurrence of oil spill accidents has a significant impact on the marine ecological environment and marine transportation. Therefore, it is particularly important to improve and strengthen the monitoring and prevention of marine oil spill accidents. The author and her team have carried out key technology and application research in the field of remote monitoring of marine targets for over 30 years. They have accumulated a rich theoretical basis and long-term data in their research of oil spill detection at sea. As a summary of the research achievements of the author’s team, this book provides the readers with information on currently applicable technical methods from the various aspects, including early warning monitoring of trace oil films in ports, remote sensing monitoring of sea surface oil spills, and source tracing of petroleum pollutants. There are 13 chapters in this book, which can be generally separated into three parts. The first part includes Chapters 1 and 2 and provides the background and necessary terminology for discussing oil spill detection and tracing. The second part extends from Chapter 3 to 8, which discusses various remote sensing technologies, especially the principles and methods of emerging remote sensing technologies for oil spill monitoring, such as fluorescent remote sensing, marine radar, and GNSS-R. The third part illustrates the application of detection, identification, and tracing technology in marine oil spill research with actual application cases. This book has extensive application value in the fields of marine ecological environment monitoring, marine resource development and transportation, and marine target information detection and identification.

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Preface

For many years, the author of this book and her team have been closely participating in the formulation and implementation of various conventions and rules of the International Maritime Organization (IMO), such as MARPOL 73/78. By submitting multiple proposals to the IMO, they have provided a decisionmaking basis for the IMO’s pollution prevention initiative. I believe that the publication of this book will further promote international communications in the field of oil spill emergencies, provide norms and methodological guidance for offshore oil spill monitoring, and support the construction of the world’s oil spill emergency system.

Dr. Huilin Jiang Academician of the Chinese Academy of Engineering Changchun, Jilin, China June 1, 2023

Acknowledgments The author takes the opportunity to express her sense of gratitude to the following publishers (in alphabetic order): Elsevier, MDPI, and Optical Society of America, for granting free copyright permission on the reuse of prepublished figures in this book. The author would also like to thank all the researchers who published their work in the public domain, and which are partly reused in this book under the CC-BY license. The author would like to thank the National Aeronautics and Space Administration, European Space Agency, and National Satellite Ocean Application Centre for providing the remote sensing data. The author thanks Dr. Ming Xie, Dr. Bingxin Liu, Dr. Zhenduo Zhang, Dr. Peng Liu, Dr. Xuanyuan Zhu, and Dr. Haixia Wang from Dalian Maritime University, Dr. Yongchao Hou from Yantai University, and Dr. Guannan Li from Ludong University, for sharing their expertise. The author would also like to extend her thanks to Fran Kennedy-Ellis, Teddy A. Lewis, Erragounta Saibabu Rao, and the other editors and managers from the Elsevier Editorial Group for their warm work during the process of manuscript preparation and publication. The author would also like to thank the anonymous reviewers for providing comments and suggestions on this work. This work was funded in part by the National Key R&D Program of China (grant number 2020YFE0201500), the Fundamental Research Funds for the Central Universities (grant number 3132023507), the Dalian High-Level Talent Innovation Program (grant number 2022RG02), and the Liaoning Revitalization Talents Program (grant number XLYC2001002).

1 Introduction 1.1 Oil spill and its impacts Ocean contains various important resources for human life and development. Under the pressures of population expansion, resource shortage, and environmental degradation, coastal countries pay more attentions to the ocean than ever before, and the development and utilization of the ocean resources has become a consensus. With the increasing pressure of land energy and the continuous development of global economic integration, offshore oil exploitation and transportation have become an important way for human beings to obtain fossil energy. As a consequence, marine oil spill pollution occurs from time to time. The situation of marine oil spill pollution is becoming more and more serious (Leifer et al., 2012; Dong et al., 2022), which has caused serious harm to marine ecological security and marine environmental protection (Kingston, 2002). Marine pollution caused by theoil spill is a problem that all coastal countries may face. According to the statistics from the International Tanker Owners Pollution Federation (ITOPF, 2021), thanks to the formulation of international cooperations and scientific and technological progress, the number of oil spills caused by ship accidents has decreased year by year. In the past 40 years, the number of large-scale oil spills of more than 700 t has decreased significantly from an average of 9.3 times a year in the 1980s to 7.8 times a year in the 1990s, and only 3.3 times a year in the 2000s. The average number of large-scale oil spills in 2010e12 was only 1.7 times a year. However, some large-scale oil spill accidents continued to occur. For example, the oil spill accident of “Prestige” in 2002 caused 63,000 tons of oil to flow into the ocean, resulting in serious pollution to the marine environment. Meanwhile, with the continuous advancement of offshore oil exploitation to the deep sea and polar regions, oil platforms gradually enter the storm and icy sea, and its risk of accident and potential oil spill are increasing. For example, Russia, Nigeria Delta, and northeast Amazon near the Arctic circle have gradually Oil Spill Detection, Identification, and Tracing. https://doi.org/10.1016/B978-0-443-13778-5.00010-2 Copyright © 2024 Elsevier Inc. All rights reserved.

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become the potential areas of oil spill pollution. Major oil spill pollution caused by multiple accidents in drilling platforms and oil pipeline facilities has occurred. At present, there is no clear correlation between the number of pipeline bursts and the amount of oil spills. But it is obvious that the oil spills caused by deep-sea operation platforms or pipelines exist for a long time on the sea surface and are difficult to clean up. The recovery of the ocean environment caused by oil spills could also be costly and time-consuming. For example, the explosion of the Deepwater Horizon oil drilling platform in the Gulf of Mexico in 2010 led to a large amount of crude oil leaking into the Gulf of Mexico for 3 months, resulting in the death of a large number of marine organisms and disastrous damage to local tourism and fisheries. This makes it the largest oil spill pollution accident after the Mina al Ahmadi oil spill in the first Gulf War. In addition to the oil spill pollution caused by ship accidents and offshore oil exploitation, the construction and operation of port facilities, such as coastal oil storage and transportation bases, also increase the risk of marine oil spill pollution. The oil spill risk caused by these facilities not only comes from the improper operation of staff or the aging of relevant equipment, but also from man-made intentional damage, such as military strike. These accidents often result in a great amount of oil pollution. For example, in the pollution accident caused by the explosion of Dalian Xingang oil tank in July 2010, which was due to the misoperation of the staff, the spilled oil drifted on the sea surface with an area of 183 km2, which seriously affected the water quality of Dalian Bay and nearby sea areas. The oil spill accident in Lebanon in 2006 was caused by the Israeli Army bombing the Jiyyeh power plant in the south of Beirut, resulting in about 15,000 tons of fuel oil entering the Mediterranean, which floated to Syrian under the effect of ocean currents and formed a pollution belt about 140 km long and 15 km wide. In addition to the natural leakage or accidental leakage above, man-made intentional discharge of oil sewage is also one of the main sources of marine oil pollution. Oil discharge can be received and treated through receiving facilities at the port, but this cost was sometimes avoided through illegal discharge. Research shows that the risk of being fined for illegal discharge is lower than the cost of operating in accordance with the regulations, and the possibility of being monitored is also very small especially the possibility of being found at night is almost zero (Mizukoshi et al., 2020; Liu et al., 2021). Investigation shows that in the past, ships in North Sea illegally discharged 500,000 L of oil every year at night alone (Camphuysen and Vollaard, 2015).

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The regional factors related to oil pollution are the sea areas near the main routes and offshore facilities. There are about 10,000 such oil discharges in the Baltic Sea every year.

1.2 Sources of oil spills and their characteristics There are many sources of oil spills in the marine environment, mainly including offshore oil production and transportation, land source oil leakage, shipwrecks, natural leakage, the pollution of offshore exploitation units, etc. (1) Offshore oil production: with the sustained and rapid development of economy, offshore oil exploitation activities are becoming frequent, as the oil spill accidents derived from oil and gas fields that caused great harm to the marine environment. On November 1, 2009, Australian “Mondala” drilling platform bursted. Millions of liters of crude oil flowed into the sea, causing a major marine ecological disaster and endangering thousands of seabirds and marine lives. The oil spill covered an area of 25,000 km2 and threatened the marine ecological protection zone near Ashmo Island, Indonesia. On April 20, 2010, the Deepwater Horizon drilling platform in the Gulf of Mexico exploded. Millions of gallons of crude oil gushed out of the deep seabed into the Gulf of Mexico, covering an area of 9900 km2 and polluting the coasts of Louisiana, Alabama, Mississippi, and part of Florida. It has become the most serious oil spill in American history. Although the plugging and decontamination of the oil spill in the Gulf of Mexico have been completed, the marine environmental pollution and ecosystem damage may affect more than 10 years (Beyer et al., 2016). The oil spill occurred in Penglai 19e3 oilfield in Bohai Sea in June 2011 caused the leakage of about 700 barrels of crude oil onto the sea surface of the Bohai Sea, as well as the leakage and deposition of about 2500 barrels of mineral oil-based mud on the seabed. The accident has polluted 5500 km2 of sea water, roughly equivalent to 7% of the area of the Bohai Sea. (2) Oil transportation: with the progress of shipbuilding technology and the increasing trend of large ships in recent years, more oil tankers enter and leave the port than ever before. According to statistics, from 1973 to 2012, 3035 oil spills occurred along the coast of China and were caused by ships, with the average case of 76 in each year. In recent years, the navigation environment has become very complex, and the risk of oil spill

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pollution caused by ship collision has become greater than ever. In November 1983, the Panamanian oil tanker “East Ambassador” ran aground in Qingdao port, resulting in the leakage of about 3000 tons of crude oil and serious pollution of the sea area and more than 230 km of coastline near Qingdao. In December 2004, the Panamanian container ship “Modern Promotion” and the German container ship “Elena” collided in the Pearl River Estuary and caused the leakage of 450 tons of heavy oil spilling from the fuel tank. (3) Land source oil spills: the processing and utilizing of oil production must go through the process of loading, unloading, storage and transportation. Accidents such as explosion and leakage in coastal terminals, tanks, or oil pipeline can easily flow into nearby coastal water and cause serious environmental pollution. For example, the explosion and oil spill accident of Dalian Xingang oil pipeline on July 16, 2010 triggered a fire and caused crude oil leakage into the sea, resulting in a huge ecological disaster. On November 22, 2013, the explosion of Sinopec oil pipeline in Qingdao caused oil spill accidents. The frequent oil spill accidents sounded an alarm for the prevention, control, and management of oil spill risk.

1.3 Oil spill remote sensing and tracing 1.3.1 Oil spill detection and identification technology With the development of sensor technology and data mining, remote sensing technology starts to play an important role in offshore oil film detection and identification (Fingas and Brown, 2013, 2014, 2018). The marine oil spill accidents would cover the sea surface with oil films of different thickness and area. There are obvious differences in physical and chemical properties between the sea and the sea, as well as the absorption, scattering, and reflection of electromagnetic waves in different bands, which are the theoretical basis of remote sensing detection and identification of marine oil films (Hu et al., 2021). The research on remote sensing detection and recognition of offshore oil film can be mainly classified into two categories, i.e., passive remote sensing and active remote sensing technology, depending on whether the sensing system has active radiation. Passive remote sensing technology includes visible/optical remote sensing, infrared (IR) remote sensing, ultraviolet (UV) remote sensing, microwave radiation remote sensing, etc. active remote sensing mainly includes radar systems and laser-induced fluorescence (LIF).

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Passive optical remote sensing technology is a common offshore oil film detection and identification technology (Hu et al., 2021). The related spaceborne, airborne, and shipborne instruments and equipment are also relatively mature. Since there is a significant difference in the reflectance between offshore oil film and seawater in visible light band, oil spill can be detected by analyzing static image or dynamic video information in the visible band (Lammoglia and Filho, 2011). With the development of multi-spectral and hyperspectral technology, spectral refinement and multispectral sensors have emerged (Green et al., 1998; Clark et al., 2003). However, because the offshore oil film has no obvious characteristic peak in the visible band, and there is only an overall difference between the spectral information of oil film and seawater in the visible band (Otremba, 2000), the visible remote sensing technology still has difficulties in identifying oil species and retrieving oil film thickness. Moreover, visible remote sensing relies on sunlight as the light source, which will be affected by fog, cloud, and other weather conditions. For the same reason, passive optical remote sensing technology generally can not be used at night. Marine oil spill forms an oil film with a certain thickness to cover the seawater surface, which radiates energy in the IR band by self-radiation or absorbing solar radiation (Salisbury et al., 1993; Grierson, 1998; Shih and Andrews, 2008a). Through the IR imaging instrument, it is found that there is a certain mathematical relationship between the radiation energy of offshore oil film and its thickness (Shih and Andrews, 2008b; Lu et al., 2016; Guo et al., 2020). Thus, oil thickness can be estimated through IR remote sensing. However, the IR remote sensing method is based on the inversion of thermal radiation, which has defects in the identification of oil species. The IR band is easily disturbed by other targets in the ocean, including algae, floating debris, and marine garbage. As the IR sensing technology gets relatively mature and the commercialization level of IR remote sensing instrument gets high, it has become a commonly used sensing instrument for offshore oil film detection at present (Liu et al., 2022). The offshore oil film has a strong reflection of sunlight in the UV band. Due to the high sensitivity of the reflectivity in the UV band, thin oil film on the sea surface can be easily detected using UV detector (Suo et al., 2021; Xie and Li, 2022). Yin et al. (2010) designed a push broom offshore oil film UV detection sensor, which not only expanded the detection range but also significantly superior to the IR imaging detection in detection accuracy. However, at present, UV remote sensing technology is not widely used in offshore oil film detection. The main problem is that the

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atmosphere has strong absorption in UV band, which results in a low signal-to-noise ratio (SNR) in UV band signal (Shi et al., 2015). Additionally, the wind and waves on the sea surface can easily produce false target in the UV images and affect the detection accuracy. Microwave radiation remote sensing is also a passive remote sensing method for oil spill detection. Any object, including seawater and oil film, is radiating out microwave. The microwave emissivity of oil film is higher than that of seawater, so the oil film can be detected by analyzing the change of microwave radiation on the sea surface. Offshore oil film will form air-oil-water threelayer medium. According to the basic principle of microwave radiation for three-layer medium, it can be deduced that the microwave radiation brightness of oil film changes periodically with the increase of oil film thickness. By using the multi-band microwave radiation brightness measurement method, it can not only detect the oil film but also measure the oil thickness. The airborne microwave radiation oil spill detection equipment developed by German company Optimare can detect oil film that has a thickness of 0.05e3 mm. Nevertheless, the practical application of passive microwave radiation detection method is limited by the marine environment and interference targets. More detailed physical parameters of oil film are required in the inversion of oil film thickness (Fingas, 2018), and its universality still needs further research and exploration. Radar is an active microwave remote sensor. It uses the coherent signal radiated by the constant velocity mobile antenna to obtain high-resolution microwave images by coherently processing the echo signals received at different positions. It can penetrate clouds and fog, and thus observes the marine environment all-day and all-weather without being limited by light and climate conditions. Airborne or spaceborne synthetic aperture radar (SAR) is commonly used for oil detection (Solberg et al., 2007; Zhang et al., 2011; Skrunes et al., 2014; Singha and Ressel, 2016). Resources and environment satellites with SAR sensors have been launched by agencies from many countries, such as KOMPSAT-5 radar imaging satellite launched by South Korea, Sentinel-1A and Sentinel-1B satellites launched by the Copernicus Mission in Europe, and GF-3 spaceborne multi-polarization SAR satellite launched by China’s National Space Administration (Leifer et al., 2012; Sun et al., 2017). These spaceborne SAR systems provide the basis for multi-angle and multi-band detection method for marine oil spill monitoring research. However, due to the limitation of transit period, the spaceborne sensor can not meet the needs of real-time and long-term monitoring.

Chapter 1 Introduction

Furthermore, it lacks judgment for oil spill types and is vulnerable to the interference of false targets such as offshore low wind speed area. LIF is based on the fact that the substances with photon absorption ability in petroleum substances can emit fluorescence longer than the excitation wavelength (usually in UV band) (Patra, 2003; Brown and Fingas, 2003). Oil pollutants can be identified by analyzing the characteristic peak and intensity information of fluorometric spectrum (Hou et al., 2019, 2021; Li et al., 2022). The fluorometric spectra of chlorophyll and other substances contained in phytoplankton are significantly different from those of oil. The main peak of chlorophyll a fluorescence spectrum is about 680 nm, which has little impact on the oil spill detection based on LIF. The yellow substance in the ocean produces the main peak of fluorometric spectrum near 420 nm. But when the offshore oil film exceeds 20 mm, the influence of yellow substance disappears. Thus, LIF method has higher accuracy for the detection and identification of oil film in seawater (Kumke et al., 1995; Apicella et al., 2004). In addition, the design of the optical structure of LIF device can be designed to eliminate the fluorescence signal emitted by the interfering substances. Furthermore, oil film can also be accurately detected by denoising and fitting the fluorometric spectrum data (Hou et al., 2021; Li et al., 2022). In short, the fluorometric spectra of different oil species usually have different characteristic peaks and intensity differences. Thus, LIF has strong feasibility in accurate detection of oil film and potential of oil species identification through the characteristic analysis of fluorometric spectra of different targets in marine environment.

1.3.2 Oil spill tracing The identification of chemical fingerprints of offshore oil spills is mainly to conduct instrumental analysis of oil spills and one or several suspicious oil source samples, obtain the chemical fingerprints of the samples, and then further compare them with the oil fingerprint database, or use mathematical statistical methods to obtain the characteristics of each oil sample and find the source of oil spills. Common analysis techniques of spilled oil fingerprints mainly include infrared spectroscopy, fluorescence spectroscopy, gas chromatography, gas chromatography-mass spectrometry, stable isotope mass spectrometry, etc.

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1.3.2.1 Infrared spectroscopy Infrared spectroscopy refers to the method of using infrared light to irradiate substances. Different substance structures have different absorption intensities of infrared light, resulting in different absorption spectra at different wavelengths. These differences are used to identify oil samples. This method can detect CeC, CeH, C ¼ O, SeO, and other groups in oil. Because the types and contents of functional groups in different oil samples are different, infrared spectroscopy can effectively identify different oil products. Infrared spectroscopy is divided into near -infrared (NIR), mid-infrared (MIR), far infrared (LIR), and attenuated total reflection infrared (ATR) (Pang, 2006).

1.3.2.2 Fluorescence spectroscopy Oil contains fluorescent substances such as aromatic compounds, so oil samples will produce fluorescence under the irradiation of ultraviolet light (or short-wave visible light). This method is called fluorescence spectrum analysis by using the fluorescence characteristics and intensity of oil products. Different oil samples also have different components and contents of fluorescent substances, which can be used to distinguish and identify oil samples (Hou et al., 2019, 2021). Compounds such as high ring number polycyclic aromatic hydrocarbons in oil have UV fluorescence. The solubility and volatility of these compounds are small, so the fluorescence spectrum of weathered oil samples is also relatively complete. Due to its high sensitivity, synchronous ultraviolet fluorescence (SUVF) is very suitable for screening aromatic petroleum and is applied in the field of petroleum geochemistry combined with other technologies. Threedimensional fluorescence spectroscopy is also widely used in oil sample characterization and source identification. Li et al. (2022) compared the identification and analysis effects of synchronous fluorescence spectroscopy and excitation-emission matrix (EEM) on the spilled oil around a wharf. The results showed that although synchronous fluorescence spectroscopy can classify similar spilled oil, it can not distinguish different oil samples with similar properties, and EEM can be used to identify oil products in detail.

1.3.2.3 Gas chromatography Gas chromatography is a common instrumental analysis method in the field of oil spill tracing. Chromatography refers to the technology of separating the components in the mixture based on the principle that there are differences in the partition

Chapter 1 Introduction

coefficients of various substances in different phase states. Among them, if the mobile phase (the forward-moving phase in the two phases) is gas, it is gas chromatography. The common detectors of gas chromatography are flame ionization detector (FID) and thermal conductivity detector (TCD), among which the detection of substances to be tested by FID is destructive, while TCD is nondestructive. Because FID is more sensitive to hydrocarbons, FID is mainly used for the analysis and detection of petroleum hydrocarbons. Gas chromatography has the characteristics of high sensitivity, good separation effect, and high selectivity. GC-FID has been widely used in the field of oil fingerprint identification (Fernandez-Varela et al., 2008). However, since the research target of gas chromatography is mainly n-alkanes, this method is usually not able to identify severely weathered oil spills and needs to be combined with other methods (Staniloac et al., 2001).

1.3.2.4 Gas chromatography-mass spectrometry More abundant and stable fingerprint information can be obtained by analyzing various oil samples by gas chromatographymass spectrometry (GC-MS). This method can better analyze n-alkanes, polycyclic aromatic hydrocarbons, benzene series, and biomarkers in various oil samples (Texeria et al., 2014). Under natural conditions, oil samples are prone to weathering. Sometimes the weathering is serious, which will affect the determination of material content in oil samples (Li et al., 2018). This kind of chemical substance has stable properties and is relatively less affected by weathering. Therefore, more stable fingerprint information can be obtained through determination, so as to identify its source more accurately. This method has the advantages of high sensitivity, high selectivity, and high qualitative ability. Thus, it is one of the commonly used methods for “oil fingerprint” identification and analysis in the laboratory (Bayona et al., 2015). There are four common mass separators for mass spectrometry detection by GC-MS: quadrupole mass analyzer, fan-shaped mass analyzer, double-focusing mass analyzer, and ion trap detector. Each has its own advantages and disadvantages. At present, the fan-shaped mass separator is commonly used.

1.3.2.5 Stable isotope mass spectrometry In recent years, stable isotope mass spectrometry (GC-IRMS) has gradually developed, using radioisotopes or stable isotopes as tracers for traceability or fingerprint identification. Stable isotope technology has been widely used in medicine, chemistry,

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life science, and other fields, and more and more in environmental science, especially in oil traceability and identification (Philp, 2007). In the refining process of some oil products, some biomarkers will be lost with the oil processing, which will hinder the “oil fingerprint” identification of GC-MS. This method has strong weathering resistance and can obtain stable isotope ratio, making the identification results more accurate (Zhang, 2011).

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Shih, W.-C., Andrews, A.B., 2008b. Modeling of thickness dependent infrared radiance contrast of native and crude oil covered water surface. Opt Express 16 (14), 10535e10542. Salisbury, J.W., Aria, D.M., Sabins Jr., F.F., 1993. Thermal infrared remote sensing of crude oil slicks. Remote Sens. Environ. 45, 225e231. Singha, S., Ressel, R., 2016. Offshore platform sourced pollution monitoring using space-S fully polarimetric C and X band synthetic aperture radar. Mar. Pollut. Bull. 112 (1), 327e340. Skrunes, S., Brekke, C., Eltoft, T., 2014. Characterization of marine surface slicks by Radarsat-2 multipolarization features. IEEE Trans. Geosci. Rem. Sens. 52 (9), 5302e5319. Solberg, A.H.S., Brekke, C., Husoy, P.O., 2007. Oil spill detection in Radarsat and Envisat SAR images. IEEE Trans. Geosci. Rem. Sens. 45 (3), 746e755. Staniloac, D., Petrescu, B., Patroeseu, C., 2001. Pattern recognition based software for oil spills identification by gas-chromatography and IR spectrophotometry. Environ. Forensics 2 (4), 363e366. Suo, Z., Lu, Y., Liu, J., Ding, J., Yin, D., Xu, F., Jiao, J., 2021. Ultraviolet remote sensing of marine oil spills: a new approach of Haiyang-1C satellite. Opt Express 29 (9), 13486e13495. Sun, J., Yu, W., Deng, Y., 2017. The SAR payload design and performance for the GF-3 mission. Sensors 17 (10), 2419. Texeira, C.C., Santos Siqueira, C.Y., Aquino Neto, F.R., Miranda, F.P., Cerqueira, J.R., Vasconcelos, A.O., Landau, L., Herrera, M., Bannermaman, K., 2014. Source identification of sea surface oil with geochemical data in Cantarell, Mexico. Microchem. J. 117, 202e213. Xie, M., Li, Y., 2022. Experimental analysis on the ultraviolet imaging of oil film on water surface: implication for the optimal band for oil film detection using ultraviolet imaging. Arch. Environ. Contam. Toxicol. 83, 109e115. Yin, D., Huang, X., Qian, W., Huang, X., Li, Y., Feng, Q., 2010. Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance. Proc. SPIE-Int. Soc. Opt. Eng. 7825. Zhang, B., Perrie, W., Li, X., Pichel, W.G., 2011. Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image. Geophys. Res. Lett. 38 (10), 602. Zhang, S., 2011. The Carbon Stable Isotopes Features of Oil Spill Fingerprint. Master Thesis. Dalian Maritime University (in Chinese).

2 Theoretical basis 2.1 Basics of electromagnetic radiance 2.1.1 Properties of electromagnetic waves As stated in Chapter 1, oil pollutants have different characteristics of responses to electromagnetic waves than clean sea water, which are the theoretical basis of oil spill remote sensing (Otremba, 2000; Hu et al., 2021). Therefore, it is necessary to introduce some theoretical basis for electromagnetic waves in this chapter before discussing the specific types oil spill remote sensing technologies. Electromagnetic wave refers to the electric field and magnetic field that oscillate in phase and are perpendicular to each other. It transmits energy and momentum in the form of a wave in space, and its propagation direction is perpendicular to the oscillation direction of the electric field and magnetic field. Electromagnetic wave does not rely on medium to propagate, and its propagation speed in vacuum is the speed of light.

2.1.1.1 Maxwell equations Based on the previous electromagnetic researches, Maxwell summarized and generalized the basic laws of constant electromagnetic field and quasi stable electromagnetic field, as well as the law of electromagnetic field in the case of time-varying field. These laws were reduced to a set of expressions, called Maxwell equations. It is expressed as follows: V,D ¼ r

(2.1)

V,B ¼ 0

(2.2)

VE ¼ 

vB vt

VH ¼ j þ

vD vt

(2.3) (2.4)

where D, E, B, and H represent the inductance strength, electric field strength, magnetic induction strength, and magnetic field Oil Spill Detection, Identification, and Tracing. https://doi.org/10.1016/B978-0-443-13778-5.00006-0 Copyright © 2024 Elsevier Inc. All rights reserved.

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strength, respectively; r is the charge density in the closed surface represents the conduction current on the integrated closed loop; vD is the displacement current density. vt Maxwell equations generalize the properties of electrostatic field and quasi-steady current magnetic field and the relationship between electric field and magnetic field in the case of timevarying field. Eq. (2.1) is the Gauss law of electric field, which means that the electric field can be an active field. At this time, the power line must be emitted from the positive charge and terminate in the negative charge. Eq. (2.2) is the law of continuity of magnetic flux, that is, the magnetic flux passing through a closed surface is equal to zero, indicating that (1) the number of magnetic lines passing through any closed surface is equal; (2) the magnetic field is a passive field; and (3) the magnetic lines are always closed. Eq. (2.3) is Faraday’s law of electromagnetic induction, which points out that a changing magnetic field will produce an induced electric field, which is a vortex field with a closed magnetic line. It is different from the case when there is charge in the closed surface. Maxwell pointed out that as long as the magnetic flux in the limited area changes, whether there is a conductor or not, it must be accompanied by a changing electric field. Eq. (2.4) is Ampere’s full current law, which shows that in the case of alternating electromagnetic field, the magnetic field includes both the magnetic field generated by conduction current and the magnetic field generated by displacement current. Maxwell believed that in terms of exciting magnetic field, the change of electric field is equivalent to a kind of current, which is called displacement current. The displacement current is different from the conduction current generated by charge flow. It is generated by changing electric field, but they are equivalent in generating magnetic effect. The introduction of displacement current further reveals the close relationship between electric field and magnetic field.

2.1.1.2 Mass equation Maxwell equations in the case of time-varying fields can be used to describe the variation law of electromagnetic field. But when dealing with practical problems, since the electromagnetic field always propagates in the medium, and the properties of the medium will affect the propagation of electromagnetic field.

Chapter 2 Theoretical basis

The relationship describing the characteristics of matter under the action of the field is called the material equation. The material equation in a static and isotropic medium (the physical properties of each point of matter do not change with the direction) has the following simple relationship (Iizuka, 2008): j ¼ sE

(2.6)

D ¼ εE

(2.7)

B ¼ mH

(2.8)

where s is conductivity; ε and m are permittivity of electricity and magnify, respectively. The mass equation gives the electrical and magnetic properties of the medium, which are the results of the average interaction of a large number of molecules in the medium when light interacts with matter. In this way, Maxwell equations and material equations form a complete set of equations to describe the general law of electromagnetic field in the case of time-varying field. It is used to deal with specific optical problems under appropriate boundary value conditions.

2.1.1.3 Fluctuation of electromagnetic field It is known from Maxwell’s equations that the time-varying electric field generates a vortex magnetic field in the surrounding space, and then the time-varying magnetic field generates a vortex electric field in the surrounding space. They excite each other and generate alternately to form a unified field and electromagnetic field in space. The alternating electromagnetic field propagates from near to far at a certain speed in space to form electromagnetic waves. Maxwell’s equation proved that the propagation of electromagnetic field has volatility. For simplicity, the case of infinite isotropic homogeneous medium (ε and m are constance and s ¼ 0) is discussed. After theoretical calculation, Maxwell predicted that alternating electric and magnetic fields produce electromagnetic waves, and light waves are electromagnetic waves. Maxwell’s prediction was confirmed experimentally by Hertz 20 years later. Hertz discovered the electromagnetic standing wave and proved that the electromagnetic wave has the same reflection, refraction,

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coherence, propagation speed, line emission, and polarization characteristics as the light wave. After that, the electromagnetic theory of light was really accepted by people. The electric field and magnetic field follow the superposition principle. Because electric field and magnetic field are vector fields, all electric field vectors and magnetic field vectors are suitable for vector addition. For example, when a traveling electromagnetic wave is an incident on a medium, it will cause electrons in the medium to oscillate, so that they themselves also emit electromagnetic waves, resulting in phenomena such as refraction or diffraction. In nonlinear media (for example, some crystals), electromagnetic waves will interact with electric or magnetic fields, including Faraday effect, Kerr effect, and so on. When the electromagnetic wave is incident from one medium to another, if the refractive indexes of the two media are not equal, refraction will occur. The direction and velocity of the electromagnetic wave will also change. Snell’s law specifically describes the physical behavior of refraction. It is assumed that the light wave composed of many electromagnetic waves with different frequencies is incident on the prism from the air. Because the refractive index of the material in the prism is related to the frequency of the electromagnetic wave, dispersion will occur: the light wave will be dispersed into a group of observable electromagnetic spectrum. Wave is composed of many successive peaks and troughs. The distance between two adjacent peaks or troughs is called wavelength. The wavelengths of electromagnetic waves come in many different sizes, from very long radio waves to very short gamma rays. A very important physical parameter to describe light wave is frequency. The frequency of a wave is its oscillation rate, and its SI unit is hertz. The frequency at which a clock oscillates once per second is 1 hertz. The frequency is inversely proportional to the wavelength: C ¼ fl, where, C is the wave speed (in vacuum, it is the speed of light; in other media, it is less than the speed of light), f is the frequency and l is the wavelength. When a wave propagates from one medium to another, the wave velocity changes, but the frequency does not change. Interference is the superposition of two or more waves to form a new wave pattern. If the electric field and the magnetic field of these electromagnetic waves are in the same direction, the interference is mutually long interference; On the contrary, it is destructive interference. The energy of electromagnetic waves is also known as radiant energy. Half of this energy is stored in the electric field and the other half in the magnetic field. It can be expressed by the following equations (Iizuka, 2008):

Chapter 2 Theoretical basis

u ¼

1 2 ε0 2 B þ E 2m0 2

V$D ¼ rV$B ¼ 0V  E ¼  VH ¼ jþ

vB vB VE ¼  vt vt

(2.9)

vD j ¼ sED ¼ εEB ¼ mHs ¼ 0r ¼ 0j ¼ 0 vt

where, u is the energy per unit volume; E is the value of electric field; B is the value of magnetic field; ε0 is the electric constant; u0 is the magnetic constant.

2.1.1.4 Propagation speed An accelerating charge or an electromagnetic field that changes over time will produce electromagnetic waves. In free space, electromagnetic waves travel at the speed of light. To accurately calculate its physical behavior, we must refer to the concept of delay time. This increases the complexity of the expressions for the electric and magnetic field. These additional items describe in detail the physical behavior of electromagnetic waves. When any wire (or other conductors, such as antenna) conducts alternating current, electromagnetic waves of the same frequency will also be emitted. Electromagnetic waves must obey a rule: no matter how fast or slow the observer is, compared with the observer, electromagnetic waves always travel in the vacuum at the speed of light. Einstein developed special relativity from this insight and became the second basic principle of special relativity. In other media different from a vacuum, the speed of electromagnetic wave propagation will be less than the speed of light. The refractive index n of a medium is the ratio of the speed of light C to the speed V of electromagnetic wave propagating in the medium: n ¼ vc .

2.1.2 Electromagnetic wavebands According to the length of wavelength, starting from long wave, electromagnetic wave can be divided into radio wave, microwave, infrared (IR), visible (VIS) light, ultraviolet (UV), X-ray, gamma ray, and so on. UV light has a wavelength range of 200e380 nm and a central wavelength of 365 nm. VIS has a wavelength range of 380e760 nm, and a central wavelength of 550 nm. IR has a wavelength range of 760e2500 nm and a central wavelength of 950 nm. Human eyes can observe electromagnetic waves with wavelengths between about 400 and 700 nm, which is the VIS light.

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2.1.3 Basic law of electromagnetic radiation The first expression of Kirchhoff’s law is that if the medium is in the condition of local thermal equilibrium, its rate of absorbing energy is equal to that of radiating energy: eðlÞ ¼ aðlÞ

(2.10)

Eq. (2.10) is the most commonly applicable expression of Kirchhoff’s law. It can be applied to both media and the interior of a medium. In Eq. (2.10), e is the emissivity of the medium and a is the absorption rate. If this formula is not satisfied, the medium will become either hotter or colder, which violates the condition of geothermal dynamic equilibrium. Another equivalent expression of Kirchhoff’s law is given as Eq. (2.11): Mðl; T Þ ¼ aðlÞMðl; T Þjblack

(2.11)

where M is the emissivity, and Eq. (2.11) expresses that the emissivity of gray body is equal to the product of its grayscale and the emissivity of blackbody with the same temperature. For the gray body, its gray level is equal to the absorptivity and emissivity. Comparing the definition formula of emissivity, it can be found that the absorptivity is equal to the emissivity. It can be seen that Eq. (2.11) indirectly expresses the meaning of Eq. (2.10). Some components in the atmosphere have a strong absorption capacity for electromagnetic waves in specific bands, so the atmospheric absorption band is formed. In these absorption bands, the sum of atmospheric absorptivity, transmittance, and diffusion is equal to 1, and in most cases, the atmospheric diffusion is about 0. If the diffusion of the atmosphere is ignored, the sum of atmospheric absorptivity and transmittance is equal to 1. According to Eq. (2.11), the atmospheric emissivity is equal to the atmospheric absorptivity. Therefore, the sum of atmospheric emissivity and transmittance is also equal to 1, thus: eA ðl; qÞ ¼ 1  tðl; qÞ

(2.12)

Eq. (2.12) is an expression of Kirchhoff’s law applicable only to the interior of a medium. It also indirectly describes that the emissivity in a medium is equal to its absorptivity. Eq. (2.12) shows the emissivity characteristics of atmospheric absorption zone, which can be applied to remote sensing of temperature and humidity in atmospheric vertical profile. Kirchhoff’s law is an important law in ocean remote sensing. It is one of the bases of the remote sensing mechanism of sea surface physical quantities. The time scale of the Earth’s surface temperature change is much larger than that required for one

Chapter 2 Theoretical basis

measurement by remote sensors. Therefore, in remote sensing calculation, the local geothermal dynamic equilibrium conditions of the Earth’s surface are generally met.

2.2 Basic terms of remote sensing Remote sensing is usually referred as the technology that gets information about the target from a certain distance and determines the attributes of the target and the relationship between the targets through the analysis and research of the information. Modern remote sensing technology mainly refers to electromagnetic wave remote sensing technology. As for the detection technologies of gravity, magnetic force, seismic wave, and sound wave, they are generally not included in modern remote sensing technology. The basic operation process of modern remote sensing technology is: at an altitude of thousands of meters, hundreds of kilometers, or even thousands of meters from the ground, taking aircraft and satellites as observation platforms, using optical, electronic, and electronic optical detection instruments to receive the electromagnetic radiation energy reflected, scattered and emitted by the target, recording them in the form of image film or digital magnetic carrier, transmitting these data to the ground receiving station, and then processing the received data into remote sensing data products required by users. Remote sensing technology can be applied to surveying and mapping, natural resources investigation, and marine environment monitoring. The electromagnetic bands used in remote sensing technology mainly include UV, VIS, IR, and microwave bands. The wavelength of UV band is 0.2e0.4 mm. Located outside the violet light in the visible band. Because the electromagnetic wave of a wavelength less than 0.3 mm is absorbed by ozone in the atmosphere, only UV light of a wavelength between 0.3 and 0.4 mm can be transmitted through the atmosphere. UV imaging can monitor gas pollution (Zhang et al., 2020, 2022) and thin oil film pollution (Yin et al., 2010; Shi et al., 2015; Suo et al., 2021; Xie and Li, 2022) on the sea surface. However, the scattering effect of the atmosphere in this band is very strong, and it is rarely used in practice. The wavelength of VIS light is 0.4e0.7 mm. It is the only band that people’s eyes can see within the electromagnetic spectrum. The VIS light can be further divided into seven colors: red, orange, yellow, green, green, blue, and purple. It can be collected and recorded by visible light film and photoelectric detector. The IR band locate outside the red light in the VIS band, and its

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wavelength of IR band is 0.7e1.0 mm. It can be subdivided into near-infrared (0.7e1.3 mm), mid-infrared (1.3e3 mm), thermal infrared (3e15 mm), and far infrared (15e1000 mm). Nearinfrared light and mid-infrared light come from the solar radiation reflected by the earth, so this band is also called reflected infrared. The near infrared radiation information can be obtained by photography (film), so this band is also called “photographic infrared.” The photographic infrared sensor has a special effect on detecting vegetation and water body. The thermal infrared sensor can detect the thermal radiation of the object. However, the thermal infrared radiation information on the ground cannot be detected by photography. It needs to be obtained by optical machinery through scanning. At present, thermal infrared radiometer mainly uses the two bands of 3e5 mm and 10e13 mm. Thermal infrared radiometer can operate at night. In addition to military reconnaissance, it can also be used to investigate sea surface temperature, shallow groundwater, urban heat island, water pollution, forest fire detection, and distinguish rock types. Most of the far-infrared radiation, of which the wavelength is greater than 15 mm, is absorbed by the atmosphere. The wavelength of microwave is 0.1e100 cm, and microwave can be divided into millimeter wave, centimeter wave, and decimeter wave. Microwave is characterized by its ability to penetrate clouds and work all day. According to the spectrum of electromagnetic wave, remote sensing can be divided into visible light and infrared reflection remote sensing, thermal infrared remote sensing, and microwave remote sensing; according to the energy source of the target, it can be divided into active remote sensing and passive remote sensing; according to the platform used by the sensor, it can be divided into satellite remote sensing, airborne remote sensing and ground remote sensing; according to the spatial scale, it can be divided into global remote sensing, regional remote sensing and urban remote sensing; according to the application field, it can be divided into resource remote sensing and environmental remote sensing; according to the research object, it can be divided into meteorological remote sensing, marine remote sensing and land remote sensing; according to the application purpose, it can be divided into land water resources remote sensing, land resources remote sensing, vegetation resources remote sensing, marine environment remote sensing, marine resources remote sensing, geological survey remote sensing, urban planning and management remote sensing, surveying and mapping remote sensing, archeological survey remote sensing, comprehensive environmental monitoring remote sensing, and planning management remote sensing.

Chapter 2 Theoretical basis

Remote sensing technology includes sensor technology, information transmission technology, information processing technology, information extraction and application technology, target information feature analysis technology, and so on. Remote sensing technology system includes spatial information acquisition system (including remote sensing platform and sensor), ground receiving and preprocessing system (including atmospheric radiation correction and geometric correction), ground fact-finding system (such as collecting environmental and meteorological data), and information analysis application system (Khorram et al., 2016). The recording forms of remote sensing information can be divided into image mode and nonimage mode. Image processing involves various operations that can process photos or digital images, including image compression, image storage, image enhancement, processing quantization, spatial filtering, and image pattern recognition.

2.2.1 Observation angle Fig. 2.1 shows the definition of solid angle. As shown in the figure, A represents the wave source and dA represents the differential area element of radiated electromagnetic. Suppose that the electromagnetic wave spontaneously radiates from the wave source dA, and a beam reaching the sphere with radius R corresponds to a solid angle differential element. The small area element corresponding to the solid angle differential element is: dS ¼ BE,BC ¼ R2 sin qdqd4

(2.13)

Differentiation of solid angle dU can be expressed as Eq. (2.14)

Figure 2.1 Definition of solid angle.

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dU ¼ sin qdqd4

(2.14)

The unit of the solid angle U is sr, and the solid angle of the sphere is 4p sr. In ocean remote sensing, if the satellite sensor is located at the solid angle shown in Fig. 2.1, the observation azimuth angle represents the angle between the sea surface projection of the observation direction and the X direction. Fig. 2.2 shows the angle of the satellite observation direction and the angle of the sun relative to the satellite scanning point. In Fig. 2.2, a is the view angle, b is the satellite zenith angle, and c is the solar zenith angle. The observation angle of the satellite describes the angle between the observation direction of the satellite and the sea surface normal of the subsatellite point. Satellite zenith angle describes the angle between the satellite observation direction and the observed sea surface normal, also known as the observed zenith angle. Incidence angle and satellite zenith angle are the same concepts. They are approximately equal to the observation angle of the satellite, so these two terms are often confused. The solar zenith angle represents the angle between the beam direction from the sun to the observed sea surface and the normal of the observed sea surface. In airborne remote sensing and ground-based radar measurement, the observation angle is often called pitching angle. The grazing angle is also used to describe the angle between the observation direction and the horizontal direction of the observation platform. Ground-based radar often uses a low grazing angle to observe the sea surface. Figure 2.2 The angle of the satellite observation direction and the angle of the sun relative to the satellite scanning point.

Sensor Sun

Sub-satellite point

Satellite scanning point

Chapter 2 Theoretical basis

2.2.2 Radiation terminology (1) Radiant energy Q represents the amount of radiant energy, and its unit is J. (2) Rradiant flux F represents the amount of energy passing through an area in unit time, thus F ¼ dQ dt , and its unit is W. (3) Radiant intensity I represents the radiation flux per unit solid angle of a point light source in a specific direction, thus I ¼ dF, and its unit is W$sr1. dU (4) Radiance L represents the radiation flux per unit area and per unit solid angle along the radiation direction, thus d2 F Lðq; 4Þ ¼ dAdU cos q, where q is the observation zenith angle. dAcosq is the unit area perpendicular to the beam direction. The unit of radiance is W.m2sr1. “Spectral” or “monochromatic” radiance represents the energy distribution of radiance relative to wavelength or frequency. Their definition can be given as Eqs. (2.15) and (2.16): Lðl; q; 4Þ ¼

dLðq; 4Þ dl

(2.15)

Lðf ; q; 4Þ ¼

dLðq; 4Þ df

(2.16)

Spectral radiance represents the radiation flux per unit area and per unit solid angle in the unit wave band (referring to unit wavelength or unit frequency) along the radiation direction. The term off-water radiance in watercolor remote sensing is an example of spectral radiance. (5) Brightness B represents the radiation flux per unit area and per unit solid angle along the radiation direction. It differs from radiance in the definition. Brightness is the measurement of radiance, while radiance is the measurement of radiant energy. (6) Irradiabce E represents the radiation flux per unit area, thus E ¼ dF dA . According to the definitions, the relation between irradiance E and radiance L is Z E ¼ Lðq; 4Þcos qdU (2.17) U

“Spectral” or “monochromatic” irradiance represents the energy distribution of irradiance relative to wavelength or frequency. Their definition can be given as Eqs. (2.18) and (2.19):

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Chapter 2 Theoretical basis

EðlÞ ¼

dE dl

(2.15)

Eðf Þ ¼

dE df

(2.16)

(7) Emittance (or exitance) M specifically represents the spontaneous emission of the source of radiation. It can be described using radiance L if it is related to solid angle, and represents the spontaneous emission flux of the radiation source on the unit wave band, unit area, and unit solid angle along the radiation direction. It can also be described using irradiance E if it is not related to solid angle and represents the spontaneous emission flux of the radiation source on the unit wave band and unit area along the radiation direction. (8) Absorptance a is defined as aðlÞ ¼ EEai . The absorptance defined using the ratio of irradiance is also called hemispherical absorptance. Reflectance r is defined as rðlÞ ¼ EEri . The reflectance defined using the ratio of irradiance is also called hemispherical reflectance. Transmittance t is defined as tðlÞ ¼ EEti . The transmittance defined using the ratio of irradiance is also called hemispherical transmittance. According to the law of conservation of energy, for incident “spectral” irradiance, Ei(l) ¼ Er(l)þEa(l)þEt(l). There is also a conservation relation among absorptivity, reflectivity, and transmittance in the medium: r(l)þa(l)þt(l) ¼ 1.

2.2.3 Polarization If all of the electric field vectors of a plane electromagnetic wave are oscillating in one plane, it is called linearly polarized. Any linearly polarized plane electromagnetic wave can be decomposed into two parts as horizontal polarization and vertical polarization. The polarization state is defined according to the relationship between the electric field direction and the reference plane; Whether it is horizontal polarization or vertical polarization involves the relationship between the direction of the electric field and the reference plane. Taking a reference plane determined by two straight lines as an example, one of the straight lines represents where the electromagnetic beam incident or leaves the sea surface, and the other is the vertical line of the sea surface. For linearly polarized radiation, the horizontally polarized electric field is perpendicular to the reference plane, and the vertically polarized electric field is parallel to the

Chapter 2 Theoretical basis

Figure 2.3 Horizontal polarization (left) and vertical polarization (right) of electromagnetic radiation.

Reference plain

Surface of the object

Surface of the object

reference plane. Fig. 2.3 shows a side view of horizontally and vertically polarized electromagnetic radiation. In the figure, the Ox axis is located on the target surface, the O axis represents the normal direction of the target surface, the thick dotted line represents the propagation direction of the electromagnetic beam, and the thin dotted line represents the reference plane composed of the propagation direction of the electromagnetic beam and the normal direction of the target surface. In the left figure that represents horizontal polarization, the solid black spot represents that the electric field vector E is perpendicular to the reference plane; in the right figure that represents vertical polarization, the arrow represents that the electric field vector Ev is located in the reference plane. Four Stokes parameters can be used to comprehensively describe the properties of plane electromagnetic wave in any polarization state (Carozzi et al., 2000). The first and second Stokes parameters are used to describe the vertical polarization and horizontal polarization components of electromagnetic radiation, respectively. The third and fourth Stokes parameters are used to describe the correlation coefficient between the vertical polarization and horizontal polarization components of the electric field. The third Stokes parameter is directly proportional to the real part of the correlation coefficient, and the fourth Stokes parameter is directly proportional to the imaginary part of the correlation between the two components. Microwave radiometer detects spontaneous radiation rather than not natural light from the earth’s surface (land, ocean, or atmosphere). The sea surface wind can change the slope of the local sea surface, and then change the polarization state of

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Chapter 2 Theoretical basis

the electromagnetic wave spontaneously emitted from the sea surface. Especially in the microwave range, the emissivity of the rough sea surface is closely related to the polarization state of electromagnetic wave. Therefore, in a microwave radiometer, one band may correspond to two channels: vertical polarization and horizontal polarization. For a microwave radiometer, the frequency of the instrument (in GHz) and the polarization state (V for vertical polarization and H for horizontal polarization) are two elements of a channel. For example, 37V and 37H represent two channels corresponding to vertical polarization and horizontal polarization respectively, and their center frequency of the band is 37 GHz. Active microwave radar, such as scatterometer, altimeter, and synthetic aperture radar (SAR), detects the electromagnetic wave that is previously emitted by the radar and then reflected and backscattered by the sea surface. The polarization state of electromagnetic waves reflected and backscattered by the sea surface is also affected by the change in local sea surface slope. Therefore, the polarization state is also an important parameter of active microwave radar. Different from microwave remote sensing instruments, VIS and NIR radiometers generally do not need to consider too much polarization. First, VIS and NIR radiometers detect the reflected or scattered natural light originally from the sun, because the natural light is isotropic and the energy in both horizontal and vertical polarization directions is the same. Secondly, VIS and NIR radiometers have a degree of polarization index in design. For example, the degree of polarization of China Ocean Color and Temperature Scanner (COCTS) is less than 5% (Sun et al., 2010). That is, the ratio of the deviation between the response of COCTS to the horizontal polarization signal and the response to the vertical polarization signal to the total signal response is less than 5%. A small degree of polarization can ensure that the response of the instrument to horizontal polarization and vertical polarization electromagnetic waves is basically the same. Since there is no polarization factor, each band corresponds to a unique channel, the concepts of channel and band can be used in both VIS and NIR radiometers (Shen et al., 2011). Thermal infrared radiometer detects spontaneous radiation from the earth’s surface (land radiation, ocean, or atmosphere). In the thermal infrared band, the electromagnetic wave emitted by the sea surface is isotropic, and its polarization component relative to the sea surface is barely affected by the wind. The polarization state generally does not appear as a parameter in the thermal infrared radiometer. However, for active remote sensing

Chapter 2 Theoretical basis

instruments using visible or infrared frequency lasers (nonnatural light), polarization may need to be considered (Talmage and Curran, 1986).

References Carozzi, T., Karlsson, R., Bergman, J., 2000. Parameters characterizing electromagnetic wave polarization. Phys. Rev. E. 61, 2024. Hu, C., Lu, Y., Sun, S., Liu, Y., 2021. Optical remote sensing of oil spills in the ocean: what is really possible? J. Remote Sens. 2021, 9141902. Iizuka, K., 2008. Engineering Optics, third ed. Springer. Khorram, S., Nelson, S.A.C., van der Wiele, C.F., Cakir, H., 2016. Fundamentals of remote sensing imaging and preliminary analysis. In: Pelton, J.N., Madry, S., Camacho-Lara, S. (Eds.), Handbook of Satellite Applications. Springer, New York, NY. Otremba, Z., 2000. The impact on the reflectance in VIS of a type of crude oil film floating on the water surface. Opt Express 7 (3), 129e134. Shen, H.-Y., Zhou, P.-C., Feng, S.-R., 2011. Research on multi-angle near infrared spectral-polarimetric characteristic for polluted water by spilled oil. Proc. SPIE 8193, 81930M. Shi, Z., Yu, L., Cao, D., Wu, Q., Yu, X., Lin, G., 2015. Airborne ultraviolet imaging system for oil slick surveillance: oileseawater contrast, imaging concept, signal-to-noise ratio, optical design, and optomechanical model. Appl. Opt. 54 (25), 7648e7655. Sun, Z., Zhao, Y., Yan, G., Li, S., 2010. Study on the hyperspectral polarized reflection characteristics of oil slicks on sea surfaces. Chin. Sci. Bull. 55 (27e28), 2771e2776. Suo, Z., Lu, Y., Liu, J., Ding, J., Yin, D., Xu, F., Jiao, J., 2021. Ultraviolet remote sensing of marine oil spills: a new approach of Haiyang-1C satellite. Opt Express 29 (9), 13486e13495. Talmage, D.A., Curran, P.J., 1986. Remote sensing using partially polarized light. Int. J. Remote Sens. 7 (1), 47e64. Xie, M., Li, Y., 2022. Experimental analysis on the ultraviolet imaging of oil film on water surface: implication for the optimal band for oil film detection using ultraviolet imaging. Arch. Environ. Contam. Toxicol. 83 (1), 109e115. Yin, D., Huang, X., Qian, W., Huang, X., Li, Y., Feng, Q., 2010. Airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance. Proc. SPIE, Int. Soc. Opt. Eng. 7825. Zhang, Z., Zheng, W., Cao, K., Li, Y., Xie, M., 2020. Simulation analysis on the optimal imaging detection wavelength of SO2 concentration in ship exhaust. Atmosphere 11 (10), 1119. Zhang, Z., Zheng, W., Cao, K., Li, Y., Xie, M., 2022. An improved method for optimizing detection bands of marine exhaust SO2 concentration in ultraviolet dual-band measurements based on signal-to-noise ratio. Atmos. Pollut. Res. 13 (7), 101479.

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3 Passive optical remote sensing technology for oil spill detection 3.1 Remote sensors and sensing platforms Passive optical remote sensing technology has the ability to identify different types of oil spill pollution and plays an important role in marine oil spill monitoring. The oil spill on the water surface has its unique absorption band and optical characteristics that are different from water in the visible (VIS) and near infrared (NIR) bands (Otremba, 2000; Hu et al., 2021). These different factors vary with the types of oil, evaporation, and emulsification of oil, background water, wind condition, air humidity, and sunlight. Many scholars used different bands to detect oil spills and summarized the spectral characteristics of the oil spill in the VIS/ NIR band (Lammoglia and Filho, 2011; Clark et al., 2009). Optical sensors can provide multi-spectral images and truecolor images of the Earth’s surface. They can also provide NIR and other bands to provide more information for target detection. Some commonly-used satellite-based hyperspectral sensors include Landsat-8, Sentinel-2, GF-2, ZY-3, MODIS, etc. Their spatial resolution ranges from 0.5 to 1,000, and temporal resolution ranges from 1 to 16 days. The hyperspectral images from different sensors can support the offshore target monitoring requirements under different scenarios and in different short periods. The technical parameters of some sensors are shown in Table 3.1.

3.2 Theoretical basis for the oil spill remote sensing using visible bands 3.2.1 Solar radiation Passive optical remote sensing uses the sun as the radiation source. Solar radiation refers to the energy emitted by the sun in the form of electromagnetic waves and particle flow. The energy transmitted by solar radiation is called solar radiant energy. Oil Spill Detection, Identification, and Tracing. https://doi.org/10.1016/B978-0-443-13778-5.00001-1 Copyright © 2024 Elsevier Inc. All rights reserved.

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Table 3.1 Technical parameters of some commonly-used satellite-based hyperspectral sensors.

Satellite (sensors)

Number of bands/Spectral range (um) Spatial spectral (m)

Revisit period (d)

Terra/AuqaMODIS Landsat8-OLI HY-1C/D

36/0.4e14.4

250/500/1000

1

9/0.433e1.390 Coastal imaging: 4/0.42e0.89 Watercolor and temperature scanner:10/0.402e12.5

30、15(Pan) 16 Coastal imaging: 50m; Coastal imaging: 3 (single Watercolor and temperature satellite); scanner:
1100m Watercolor and temperature scanner: 1 (single satellite) 2 (pan), 8, 16 2e4 1 (pan), 4 5 2 (pan). 8 4 0.5 (pan), 2 2

GF1 GF2 GF6 GF-multi-model satellite ZY1-02C ZY1-02D ZY3

5/0.45e0.89 5/0.45e0.89 5/0.45e0.90 9/0.45e1.04 4/0.52e0.89 9/0.452e1.047 5/0.50e0.89

Sentinel-2

12/0.44e22.02

5 (pan), 10 2.5 (pan), 10 2.1 (pan), 5.8 2.5e3.5 (front and back view) 10、20、60

3 3 5

5

Although the solar radiation energy received by the Earth is only one-2.2 billion of the total radiation energy from the sun to space, it is not only the main energy source of the Earth’s atmospheric movement but also the main source of the Earth’s light and heat energy. Solar radiation from the sun on the ground plane consists of two parts: direct solar radiation and diffused solar radiation. When solar radiation passes through the atmosphere and reaches the ground, the absorption, reflection, and scattering of solar radiation by air molecules, water vapor, and dust in the atmosphere would not only weaken the radiation intensity but also change the direction of radiation and the spectral distribution of radiation. Therefore, the actual solar radiation reaching the ground usually consists of direct radiation and diffused radiation. Direct radiation refers to the radiation directly from the sun without changing its direction; Diffusion is the solar radiation in which

Chapter 3 Passive optical remote sensing technology for oil spill detection

the direction has changed after being reflected and scattered by the atmosphere. It consists of three parts: scattering around the sun (skylight around the solar surface), horizon scattering (skylight or dark light around the horizon), and another skyscattering radiation. In addition, the nonhorizontal plane also receives reflected radiation from the ground. Direct solar radiation, diffuse solar radiation, and reflected solar radiation are the total solar radiation (global solar radiation). Direct sunlight can be focused by a lens or reflector. If the condensing rate is very high, a high energy density can be obtained, but the diffused solar radiation is lost. If the focus is low, part of the diffuse sunlight around the sun can also be condensed. The diffused solar radiation could vary widely. When the sky is clear and cloudless, the diffused solar radiation is about 10% of the total solar radiation. But when the sky is covered with dark clouds and the sun cannot be seen, the total solar radiation is equal to diffused solar radiation. Therefore, the energy collected by the aggregate collector is usually much less than that collected by the nonaggregate collector. The reflected solar radiation is generally very weak, but when the ground is covered with ice and snow, the reflected solar radiation on the vertical plane can reach 40% of the total solar radiation. The solar radiation reaching the ground is mainly affected by the thickness of the atmosphere. The thicker the atmosphere, the more significant the absorption, reflection, and scattering of solar radiation, and the less solar radiation reaches the ground. In addition, the state and quality of the atmosphere also affect the solar radiation reaching the ground. Obviously, the length of the path of solar radiation through the atmosphere is related to its incident direction. The wavelength distribution of solar energy can be simulated by blackbody radiation with a temperature of 5800 K. The wavelength of solar energy is distributed in UV, VIS, and IR bands. These bands are affected by atmospheric attenuation at a different level. Most of the VIS radiation reaches the ground, but ozone in the upper atmosphere absorbs most of the UV radiation. Due to the thinning of the ozone layer, especially in the Antarctic and Arctic regions, more UV radiation reaches the ground. Part of the incident IR radiation is absorbed by carbon dioxide, water vapor, and other gases, while most of the longer wavelength IR radiation from the earth’s surface is transmitted to outer space at night. The accumulation of greenhouse gases in the upper atmosphere may increase the atmospheric absorption capacity,

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resulting in global warming. Although ozone reduction has little impact on solar collectors, the greenhouse effect may increase scattered radiation and significantly affect the role of heat collectors. The physical quantity indicating the intensity of solar radiation is called solar radiation intensity, of which the unit is J/ (cm2$min). It is defined as the solar radiation energy vertically projected onto the per unit area in per unit time. The solar radiation intensity in the upper atmospheric boundary depends on the solar altitude angle, solar-terrestrial distance, and time of radiation. The greater the solar altitude angle, the greater the solar radiation intensity. Because when the same beam of light is directly irradiated, the irradiation area is the smallest, and the solar radiation per unit area is the most, and vice versa. The solar elevation angle varies with time and place. Generally speaking, the solar elevation angle at noon is greater than that in the morning and evening; that in summer is greater than that in winter; that in low latitude areas is greater than that in high latitude areas. When the Earth revolves around the sun in an elliptic orbit, the distance between the sun and the Earth changes constantly. The intensity of solar radiation obtained on the Earth is inversely proportional to the square of the distance between the sun and the Earth. When the Earth is at perihelion, the solar radiation is greater than that at aphelion. When the Earth passes through the perihelion in early January, the solar radiation per unit area of the surface is 7% more than that when it passes through the aphelion in early July. The intensity of solar radiation is directly proportional to the time of radiation. The length of sunshine varies with latitude and season. The solar radiation energy reaching the Earth is only a small part of the total solar radiation energy, but its role is quite large. Solar radiation is the main source of energy on the Earth’s surface. Firstly, solar radiation affects the geographic environment in both direct effects (such as weathering of rocks affected by temperature changes) and indirect effects (the development and changes of the Earth’s atmosphere, water, organisms, and other geographical environment elements are driven by the sun). The earth’s surface is divided into five zones according to the solar heat obtained in different latitudes. For example, the tropics get the direct incident sunlight in the whole year, and thus the most heat; the cold zone has a low sun elevation angle and a long polar night, so it gets the least heat. Thus, there are differences in the heat obtained in different places on the Earth. Nevertheless, in places with surplus heat, such as the equator, the

Chapter 3 Passive optical remote sensing technology for oil spill detection

temperature is not getting higher and higher, but remains relatively stable, and vice versa. For the whole Earth’s surface, the heat is transferred and balanced between excess and insufficient areas. Secondly, solar radiation provides energy for the lives of Earth. The growth of plants needs light and heat. The fossil fuels, such as coal and oil that are widely used in industry, are converted from solar energy and are also called “stored solar energy.” The sum of direct and scattered solar radiation reaching the ground after atmospheric weakening is called total solar radiation. On the global average, total solar radiation accounts for only 45% of the solar radiation reaching the upper limit of the atmosphere. The total radiation increases with the decrease in latitude and the increase in altitude. The total solar radiation is maximum around noon in the day, and 0 at night. The radiation energy in VIS (0.4e0.76 mm), IR (>0.76) mm), and UV ( > > Z1 ¼ a11 X1 þ a21 X2 þ / þ ap1 Xp ¼ a1 X > > > < Z2 ¼ a12 X1 þ a22 X2 þ / þ ap2 Xp ¼ a2T X (3.19) > // > > > > > : Zp ¼ a1p X1 þ a2p X2 þ / þ app Xp ¼ aT X p It is expected that the p variables can be replaced by Z1, which requires Z1 to reflect the information in p variables as much as possible. Under the condition that aT1 a1 ¼ 1, the large the variance of Z1 (Var(Z1)) is, the more information it contains. When Var(Z1) reaches the maximum value, Z1 is called the first principal component. If the first principal component is not enough to replace the information of p variables, the second linear combination of the variable X should be considered, and the second principal component Z2 can be calculated under the same constrain condition. And the other principal components can be calculated in the same way. • Minimum noise fraction Minimum noise fraction (MNF) is a noise removal and feature extraction method proposed by Green et al. (1988). It is essentially a PCA transformation including two overlapping processes. Different from the PCA that sorts the transformed components according to the size of variance, MNF sorts them according to the signal-to-noise ratio (SNR). Therefore, through MNF transformation, the components with the highest SNR are ranked first, while the bands with more noise often do not enter the next operation. Thus, MNF can not only effectively reduce the dimension of data, but also effectively remove noise. The first transformation is based on the estimated covariance matrix of noise, which is used to separate and readjust the noise in the data, which is also called “noise whitening.” This operation minimizes the variance of the transformed noise data and has no correlation between bands. By filtering the whole image or image data block with the same property, the covariance matrix of noise can be obtained as CN ¼ SN, and it can be diagonalized into the matrix DN: DN ¼ U T CN U

(3.20)

where DN is the diagonalized matrix with eigenvalues arranged in descending order; U is the orthogonal matrix composed of eigenvectors corresponding to eigenvalues.

Chapter 3 Passive optical remote sensing technology for oil spill detection

Based on Eq. (3.20), construct the matrix P ¼ UD1/2 , then: N I ¼ P T CN P

(3.21)

where I is the unit matrix. When P is applied to hyperspectral image data X, the original data can be projected into a new space through transformation. The noise in the transformed data has unit variance and is uncorrelated between bands. The second step is to perform a standard principal component transform on the noise whitening data. the image total covariance matrix is transformed through the matrix P constructed in the first step and the noise-adjusted total covariance matrix CDobj is obtained as Eq. (3.22): P T CD P ¼ CDobj

(3.22)

Calculate the eigenvector matrix V of the covariance matrix CDobj so that VTCDobjV ¼ DDobj, where is the diagonalized matrix with eigenvalues of eigenvector matrix V arranged in descending order, and VTV ¼ I. The MNF transformed matrix TMNF ¼ PV. The image data after MNF transformation are sorted according to SNR. Those with large SNR are in the front, which means that the first component after transformation concentrates a large amount of information, while the information contained in subsequent components decreases in turn, and the image quality decreases gradually (Liu et al., 2016). • Wavelet analysis Wavelet analysis is a time-frequency domain local analysis method developed based on Fourier transformation. Fourier analysis decomposes the signal into several sinusoidal functions with different frequencies, while wavelet decomposes the signal into the representation of wavelet generating function under different scales and translations. Compared with a sinusoidal curve, the wavelet curve is irregular and asymmetric. The main advantage of a wavelet is that it can carry out local analysis of a certain part within a large signal. For example, for a sinusoidal signal with a small discontinuity, a wavelet can be used to analyze its discontinuity and singularity, which is difficult to achieve using traditional Fourier transformation. Wavelet transform can analyze signals with multi-resolution, so it has great advantages in feature extraction. Projecting hyperspectral signals onto wavelet basis functions can separate fine-scale and large-scale signals. The basic operator in wavelet transform is called the wavelet generating function or wavelet basis function. Any function 4(x) can be used as a wavelet basis

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function as long as it fits the following conditions indicated from Eq. (3.23) to (3.25): Z þN 2 (3.23) j4ðxÞj dx ¼ 1 N

Z þN N

j4ðxÞjdx < N

Z þN N

4ðxÞdx ¼ 0

(3.24) (3.25)

Generally speaking, a wavelet is a group of functions obtained by stretching and translation of wavelet basis function 4a,b(x)   1 xb ，a > 0; b˛R (3.26) 4a;b ðxÞ ¼ pffiffiffi 4 a a where a is the scale factor, while b is the bias factor. Continuous wavelet transform is to compare the signals with the wavelet after translation and expansion to obtain the function of two variables:   Z þN 1  ta p ffiffiffi Cða; b; f ðtÞ; 4ðtÞÞ ¼ f ðtÞ 4 dt (3.27) a a N where a indicates the scale, while b indicates the bias; 4* means conjugation. In continuous wavelet transformation, t, a, and b are continuous variables. The wavelet coefficient C(a,b) of continuous wavelet transformation (CWT) can be obtained by continuously changing the scale factor a and the bias factor b, and the signal can be reconstructed by multiplying the wavelet coefficients with the wavelets with different scales and translations. The workload of calculating wavelet coefficients at each scale and location is huge, and a lot of redundant data will be generated. It has been proved that selecting the scale and location based on the index of 2 (a ¼ 2m, b ¼ 2mn) will be more effective and accurate for data or signal analysis. The wavelet transformation thus performed is commonly referred to as discrete wavelet transform (DWT). The DWT function can be given as Eq. (3.28): 4m;n ðtÞ ¼ 2 2 4ð2m t  nÞ m

(3.28)

The DWT of any function f(t) is given as Eq. (3.29): WTf ðm; nÞ ¼

þN X N

f ðtÞ,4m;n ðtÞ

(3.29)

Chapter 3 Passive optical remote sensing technology for oil spill detection

As a short conclusion, DWT transforms the original signal into wavelet approximations and details through a series of high-pass and low-pass filters. For the purpose of feature extraction, DWT is mainly used to decompose the original signal into N layers, and the outline information curve is used as the reference spectrum for spectral matching. The influence of detailed information is ignored and the matching accuracy is improved. Moreover, the detail information curve of the Nth layer can be used to reflect the singular characteristics on the original spectral curve, calculate the singular range, singular amplitude, and singular index of the wavelet transformed curve, and establish the relationship with a certain parameter. In some studies, the wavelet coefficients of continuous wavelet transform at several scales (such as Mexican Hat) and some parameters are used to establish a model to quantitatively estimate the parameters to be obtained (Liu et al., 1997). • Envelope removal The envelope of the spectral curve is similar to the “shell” of the spectral curve. The spectral curve that is measured or extracted from remote sensing images is composed of a series of discrete sampling points. Therefore, continuous broken lines are used to approximately represent the envelope of the spectral curve. This method is applicable to the spectral data measured by a high-resolution spectrometer or the spectral data of a single pixel extracted from the image. It can extract the characteristic band for object classification and recognition or determine the position of a characteristic peak/valley.

3.4.1.2 Spectral characteristics parameters In order to describe and analyze the spectrum of features more accurately, researchers have proposed some spectral feature parameters. These parameters often play a very important role in the classification of features based on spectral features, especially the unique spectral features of a certain object that can be used as the basis for target identification. • Spectral slope In a certain spectral range, if the spectral curve can be approximately simulated by a straight line, the slope of this straight line is defined as the spectral slope. If the spectral slope is positive, the spectral curve in this wavelength range is called the positive slope, and vice versa. If the spectral slope is zero, the spectral curve is a horizontal slope. The spectral slope index (SSI) is used to represent the slope direction of the spectral curve. For positive slope direction, SSI ¼ 1; for negative slope direction,

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SSI ¼ 1; for flat slope direction, SSI ¼ 0. The schematic diagram of SSI is shown in Fig. 3.3. • Wavelet coefficient In multi-level wavelet decomposition, the original signal (spectral data) is decomposed into low-frequency and highfrequency parts through two complementary symmetric filters, and then continue to decompose the low-frequency part of the signal is with the same process. After multi-layer decomposition, the original signal can be decomposed into multiple signals. Each layer of decomposition will produce corresponding summary coefficients and detail coefficients (represented by an and dn respectively, and n is the corresponding number of layers). The high-frequency noise in the signal can be well removed by obtaining the summary coefficient. On the other hand, the analysis of the detailed coefficient can highlight the singularity of the signal (spectral data) somewhere, which is conducive to the extraction of spectral features (Fig. 3.4).

3.4.2 Oil spill extraction in hyperspectral image The studies of texture feature analysis methods of highresolution satellite images and the discussions of texture-based information mining technology have become important content of remote sensing research (Trias-Sanz et al., 2008). The combination of spectral features and texture analysis can improve the accuracy of remote sensing classification (Xie et al., 2009; Majdar and Ghassemian, 2017). Most texture feature analysis algorithms are used to analyze gray images or single-band images and rarely involve multi-spectral or hyperspectral images. In theory, multispectral images contain rich and detailed spectral information. The texture is expressed by the change of spectral characteristics, which can more accurately reflect the position and shape of ground objects, and be served as perfect data for the interpretation of ground objects. Therefore, it is necessary to strengthen the research on the comprehensive application of a variety of image analysis technologies, fully study the high-resolution satellite

Wavelength 波 a0, positive slope, SSI=1 a＞0，正向坡，SSI =1

Wavelength 波 a=0, negative slope, SSI=0 a=0， 向坡，SSI =0

Figure 3.3 Schematic diagram of spectral slope index (SSI).

Chapter 3 Passive optical remote sensing technology for oil spill detection

a.

b.

c.

d.

e.

f.

Figure 3.4 The fifth detailed coefficient for the reflectance of light diesel at different thicknesses.

image information, and improve the process of information recognition and extraction. Most importantly, the modulation theory of water surface oil film spectrum under the influence of sun glint provides a theoretical basis for spectrum þ texture analysis. The following section uses the hyperspectral data of HJ1A/1B as an example, introduces the directional texture feature analysis

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in the process of oil spill target extraction, combines the dimension reduction based on PCA with directional texture edge detection, and develops an oil spill information extraction technology (Li et al., 2017). In an optical image, the image texture indicates the representation of the distribution of ground objects in the spectral space, The texture information is shown as the spatial change or repetition of the image gray value, or the repeated local patterns (texture units) in the image and their arrangement rules. Their features can be quantitatively described as contrast, coarseness, regularity, roughness, directionality, indention, etc. In the example of this section, PCA is first used to reduce the spectral dimension. The first principal component is used to replace multiple bands with high correlation, and the spectral information is fully retained before texture processing. Directional texture feature analysis is introduced and a directional gradient operator is used (0 , 45 , 90 , and 135 ) and the resulting image is an edge intensity image (the operation is carried out in ENVI image processing software). For the image function f(x,y), the gradient at the pixel point (x,y) is defined as: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi      
2
2    vðx; yÞ   vðx; yÞ  vðx; yÞ vðx; yÞ    gradf ðx; yÞ ¼  þ þ y   vx vy vx   vy  (3.30) Taking the gradient in 0 and 90 directions as an example, the gradient operator is expressed as follows:    ðf ðx  1; y  1Þ þ f ðx  1; yÞ þ f ðx  1; y þ 1ÞÞ    jgradf ðx; yÞj ¼    ðf ðx þ 1; y  1Þ þ f ðx þ 1; yÞ þ f ðx þ 1; y þ 1ÞÞ     ðf ðx  1; y  1Þ þ f ðx; y  1 þ f ðx þ 1; y  1ÞÞ    þ   ðf ðx  1; y þ 1Þ þ f ðx; y þ 1Þ þ f ðx þ 1; y þ 1ÞÞ  (3.31) The 3  3 four-directional filter adopted in the example is shown in Fig. 3.5: The 3  3 filter described above is too detailed in edge detection. Therefore, a combination of a larger scale filter and basic filter is applied to extract the edge of the image. By expanding the 3  3 filter to 5  5 operator, the detected edge become wider to highlight some gradient edges and contours. For the purpose of oil spill distribution area extraction using HJ satellite data, the research scheme adopted here is described as follows: firstly, the data obtained from HJ1A/1B-CCD ar

Chapter 3 Passive optical remote sensing technology for oil spill detection

-1

0

1

-1.4142

-0.7071

0

-1

0

1

-0.7071

0

0.7071

-1

0

1

0

0.7071

1.4142

-0.7071

-1.4142

a) 0e

b) 45e

-1 -1

-1

0

0

0

0.7071

0

-0.7071

1

1

1

1.4142

0.7071

0

c) 90e

0

d) 135e

Figure 3.5 Four-directional filter.

preprocessed including radiation correction and region clipping. Secondly, the preliminary extraction of oil spill information is achieved based on multi-spectral analysis. PCA is carried out on the processed image to obtain the first principal component image. Then, the directional texture analysis is carried out by using the filter to obtain the edge information of the oil spill target. In order to maintain the spatial continuity of the image, a part of the original image is “added back” to the direction texture analysis result image to generate the final overlay image to extract oil spill target. Finally, the Jeffries matusita separation index (J coefficient) is used to compare and analyze the two methods based on spectral information and supplemented by texture features (Fig. 3.6). Compared with the background seawater, the oil film has high refractive index and absorption characteristics in the multispectral remote sensing image. These characteristics are affected by oil species, oil thickness, illumination and observation angle, water characteristics, and sea conditions. Therefore, optical remote sensing has the potential to distinguish the location, oil thickness, oil species (light oil, heavy oil), etc. Fig. 3.7 shows the influence of oil thickness and emulsification state on optical characterization. Fig. 3.7a is more than a week from the initial point of oil spill, so it indicates that optical remote sensing has the ability to monitor thin oil film dispersed in water, and the gray value of emulsified oil must be higher than that of background seawater. In Fig. 3.7b, ① indicates a thin oil film and maybe emulsified oil while ② indicates a thick oil film. This result shows the image target contrast of the same oil can change due to thickness and emulsification state. Fig. 3.8 also shows the influence of clouds on the optical remote sensing of oil spill. The above results show that this method can be effectively applied to the extraction of oil spill information. On the basis of multi-spectral feature analysis of oil film, directional texture

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Data preprocessing

HJ1A/B-CCD imagery

Data filtering: Cloud fraction Smed > Smin, then proceed to Step 3; otherwise, increase the size of filtering window and return to Step 1. Step 3: If Smax > Sxy > Smin, then increase the size of filtering window and return to Step 1; otherwise, Sxy ¼ Smed; Break the iteration and continue to the next pixel. Compared with a traditional median filter, adaptive median filter algorithm changes the size of filtering window and the value of the iterative steps, which makes the final denoising results more reasonable. Since the spatial resolution of marine radar image decreases along the scanning line direction and the width of the scanning line is one pixel, the filtering window is set as a slit that is perpendicular to the scanning line direction. At the same time, the algorithm generates a noise judgment image through line detection, which increases the judgment of pixel points and reduces the loss of detail. The workflow of the algorithm flow is shown in Fig. 5.5. The processing flow of adaptive median filter algorithm

Chapter 5 Oil spill detection based on marine radar

Figure 5.5 Workflow of the adaptive algorithm for the suppression of shared-frequency noise.

for shared-frequency interference is shown in Fig. 5.6 and described in detail as follows: Step 1: Obtain the noise-enhanced gray segmentation image through the binarization process according to the line detection model r ¼ [1 2 1; 1 2 1; 1 2 1] and Otsu method (Otsu, 1979), and regard it as the noise judgment image. Step 2: For each pixel Sxy, make the judgment whether it is a noise pixel based on the noise detection image generated in Step 1; if it is a noise pixel, then obtain its filtering window and evaluate its size; if it’s large than the maximum value, then Sxy ¼ Smed; otherwise, proceed to Step 3.

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Figure 5.6 Shared-frequency noise and denoising results: (a) Unprocessed image; (b) traditional median filter; (c) adaptive median filter; (d) adaptive median filter for shared-frequency noise suppression.

Chapter 5 Oil spill detection based on marine radar

105

Step 3: Evaluate the image and remove the noise pint in the filtering window based on the noise judgment image generated in Step 1; Calculate the maximum value Smax, median value Smed, and minimum value Smin of the remaining pixels in the filtering window; if Smax > Smed > Smin, then proceed to Step 4; otherwise, increase the size of the filtering window and return to Step 1. Step 4: If Smax > Sxy > Smin, then increase the size of the filtering window and return to Step 1; otherwise, Sxy ¼ Smed; Break the iteration and continue to the next pixel. 5.3.1.2.3 Performance analysis of the shared-frequency noise suppression The unprocessed radar image is shown as Fig. 5.6a; The denoising result using traditional median filter (filtering window size: 13
13) is shown as Fig. 5.6b; The denoising result using an adaptive median filter (filtering window size: 13
13) is shown as Fig. 5.6c; The denoising result using an adaptive median filter for shared-frequency noise suppression (filtering window size: 1
13) is shown as Fig. 5.6d. As shown in Fig. 5.6, traditional and adaptive median filtering fails to completely remove the noise pixels and make the radar image blur. This is because some noise pixels are used in the filtering window. The improved adaptive median filter for sharedfrequency noise suppression includes a noise judgment image. It doesn’t only effectively remove noise pixels but also preserve the image details.

5.3.1.3 Suppressing the interference of noise pixels The radar image after suppressing the shared-frequency noise is shown as Fig. 5.7. Although the shared-frequency noise has been suppressed, many noise pixels, which are characterized as small bright dots, still exist in the radar image. There are many

Figure 5.7 Radar image after suppressing the shared-frequency noise. Reuse from Liu et al. (2019a) under CC-BY 4.0.

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Figure 5.8 Workflow of FoE model.

ways to suppress these noise pixels, and Field of Experts (FoE) is one of the effective denoising models. FoE is a high-order Markov random field model proposed by Roth and Black (2009). It has attracted the great attention from researchers for its excellent processing performance, and been applied to a series of low-level visual processing, such as grayscale and color image denoising, optical flow, image superresolution, image patching, etc. The workflow of FoE model is shown in Fig. 5.8. FoE model obtains a set of filter responses by convoluting the images with a set of filters. By inputting the filter response into the expert function corresponding to the filter, the nonlinear output is obtained, and these outputs are multiplied in the pixels. Finally, the irregular density of the image under FoE model is obtained. In the process of noise suppression in radar image, it is necessary to determine the position of small target noise first. The threshold is calculated by Otsu algorithm (Otsu, 1979), and the radar image is binarized. The results are shown in Fig. 5.9. Then small targets are identified by the size of the connected area. For example, the connected areas that are less than 200 pixels are shown in Fig. 5.10. The identified small targets are restored through the FoE model, and the restoration results are shown in Fig. 5.11.

5.3.2 Oil spill information extraction based on marine radar image There are various methods to extract oil spill information from marine radar images, the core of which is threshold segmentation. However, in marine radar images, the sea surface radar echo signal decays rapidly with the increase of distance, which makes it difficult to effectively extract the oil spill information from the direct threshold segmentation results. In order to overcome this problem, the following methods of oil spill extraction from marine radar images are introduced.

Chapter 5 Oil spill detection based on marine radar

Figure 5.9 Binarized radar image.

Figure 5.10 Connected areas that are less than 200 pixels.

Figure 5.11 Final restoration result of the radar image.

5.3.2.1 Power attenuation correction method For the same radar, the parameters such as transmission power and antenna gain are the same, and the signal power received by the radar is negatively proportional to the fourth power of the distance between the scattering element on the sea surface and the antenna, resulting in the problem of uneven distribution of radar signal power. In order to correct the attenuation of power with distance in the radar image, the radar average power distribution image of the same sea area in the

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same period is obtained by statistical method. By stretching the difference with the current image, it is possible to distinguish the background and oil spill under different sea conditions. In the actual measurement, it is found that the relationship between the sea surface echo intensity s and the distance r to the radar antenna satisfies: s ¼ R n

(5.2)

When a small number of images are selected as independent samples to generate the attenuation curve of radar echo power, the curve fluctuates greatly. With the increase in the number of images, the curve tends to be flat and the difference decreases. When 32 and 50 frames radar images in the same period are selected, the two curves almost coincide. Therefore, considering the processing time and accuracy, 32 radar images in the same period are selected and combined. The average power of the same points from the radar antenna is calculated, and all points are arranged and counted according to the distance from the radar antenna, as shown in the dotted line in Fig. 5.12. Using curve fitting, the power of the fitting curve function is 2.402 at 80e360 pixels from the radar antenna, as shown in the solid line in Fig. 5.12. The radar signal intensity is indicated as the gray value on the image. In order to avoid confusing the distant background area with the oil spill area in the extraction process, it is necessary to use the fitting function to generate the sea surface background image. Because the fitting effect is not ideal in the range of 0e50 pixels, averaged values of 32 continuous images are used to construct the background image in this range. The final generated statistical synthetic radar echo background image is shown in Fig. 5.13. The difference between the preprocessed radar image and the radar power statistical composite image is processed to obtain the radar image after power correction (Fig. 5.14) Due to the influence of radar antenna background noise, small noise bodies exist in the image. Direct gray segmentation will produce isolated black spots. There, morphological operation method is used to process the image to improve the image quality. The basic morphological operations are as follows: a. Erosion: n   o b XAs  A⨁B ¼ z B (5.3) z

Chapter 5 Oil spill detection based on marine radar

Figure 5.12 Statistical regression function of the radar image.

Figure 5.13 Radar echo background image.

Figure 5.14 Radar image after power correction.

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b. Expansion

  A⨂B ¼ zjðBÞz 4 A

(5.4)

c. Opening operation: disconnects narrow discontinuities and eliminates thin protrusions; makes the contour of the image smooth. A1B ¼ ðA⨂BÞ⨁B

(5.5)

d. Closing operation: eliminates small holes and fills the cracks of the contour line; makes the image contour smooth. A$B ¼ ðA⨁BÞ⨂B

(5.6)

After multiple opening and closing operations on the corrected radar image, the radar image shown in Fig. 5.15 is obtained. The Otsu maximum interclass variance method is used to segment the radar image after morphological processing. The obtained radar image is shown in Fig. 5.16. By analyzing the connection area, the noise object in a small area is eliminated and the large oil spill connection area is retained and shown in Fig. 5.17. In order to correspond to the actual geographical location, coordinate conversion is carried out for Fig. 5.17, as shown in Fig. 5.18. The dark area of oil spill can be obviously extracted from the figure.

5.3.2.2 Texture analysis method The gray value cooccurrence matrix is built on the statistical method of second-order combination conditional probability density. This is because the texture is formed by repeated and alternating changes in the spatial position of gray value distribution. Therefore, there must be a relationship between the gray value of two pixels in the image (the spatial correlation of gray value in the image), which is the theoretical basis of gray value

Figure 5.15 Radar image after morphological processing.

Chapter 5 Oil spill detection based on marine radar

Figure 5.16 Binarized radar image after power correction.

Figure 5.17 Oil spill area extraction result.

Figure 5.18 Coordinate transformation of the oil spill area.

cooccurrence matrix. The cooccurrence matrix is defined by the joint probability density of pixels at two positions. It not only reflects the distribution characteristics of brightness, but also the position distribution characteristics between pixels with the

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similar brightness. It is a second-order statistical feature related to the change in image brightness. For a two-dimensional digital image f(x,y) with the size of M
N, the gray value cooccurrence matrix satisfying a certain spatial relationship is: Pði，jÞ ¼ gfðx1，y1Þ，ðx2，y2Þ ˛ M
N jf ðx1， y1Þ ¼ i，f ðx2，y2Þ ¼ jg

(5.7)

where g(x) is the number of elements in set x; P is the matrix of Ng
Ng. Given that d is the distance between two pixels (x1,y1) and (x2,y2), and q is angle to the horizontal axis, the gray value co-occurrence matrix P(i,j,d,q) is the element in with the coordinate (i, j). There are usually four factors that affect the computational complexity of the gray value co-occurrence matrix: (1) direction q, (2) size of the window m, (3) distance between two pixels d, and (4) gray value level G. Direction q is usually chosen as 0 degrees, 45 degrees, 90 degrees, and 135 degrees. Distance d is usually set as 1. If the window size m is too large, the amount of calculation becomes too large; but if it is too small, the characteristics of texture become implicit, which negatively affects the segmentation result. 14 texture features can be calculated from the gray value cooccurrence matrix. Although the texture features extracted from this matrix have good classification ability, the amount of calculation is too much. Ulaby et al. (1986) claimed that the four features of entropy, correlation, angular second-order moment, and contrast are irrelevant. As a result, the classification accuracy of these four features is high. Entropy: Entropy is a measure of image randomness, which indicates the complexity of the image. Large entropy indicates strong randomness XX BNT ¼ mhk logðmhk Þ (5.8) h

k

Correlation: Correlation is used to measure the similarity of elements in the cooccurrence matrix in the row or column direction. If the pixel values of matrix elements vary greatly, the correlation value is small, and vice versa. " # XX COR ¼ ðhkmhk  ux uy Þ =sx sy (5.9) h

k

Chapter 5 Oil spill detection based on marine radar

Angular second-order moment: angular second-order distance, also known as energy, is the uniformity of image gray distribution and texture thickness. It is the sum of squares of gray value cooccurrence matrix element values. If all values of the matrix are equal, the angular second-order moment is small, and vice versa. XX ASM ¼ ðmhk Þ2 (5.10) h

k

Contrast: Contrast indicates the clarity of the image and the depth of the texture. A shallow texture indicates low contrast and vice versa. XX 2 CON ¼ ðh  kÞ mhk (5.11) h

k

The four texture features of marine radar images are shown as Fig. 5.19. As shown in Fig. 5.19, none of the four texture features can effectively characterize the oil spill area. Therefore, a texture index of oil spill extraction based on gray value co-occurrence matrix is proposed as follows (Liu et al., 2019b) I ¼ logð1  Norm ðBNTÞÞ þ logð1  Norm ðASMÞÞ þ logðNorm ðCORÞÞ þ logðNorm ðCONÞÞ;

(5.12)

where NormðMÞ ¼

M  minðMÞ . maxðMÞ  minðMÞ

(5.13)

The oil spill texture index of marine radar image according to Eq. (5.12) is shown as Fig. 5.20. The oil spill region is characterized as dark red (dark gray in print) (low index value) in the image. Although the texture index can indicate the location of the oil spill, the location is not accurate enough. Therefore, texture extraction can be used for rough extraction of oil spill information. The rough extraction of oil spill information can be realized through machine learning and global threshold, and the extracted oil spill distribution area is shown in Fig. 5.21. In order to accurately extract oil spill information, it can be achieved by adaptive threshold.

5.3.2.3 Adaptive threshold method There are several different segmentation methods of adaptive threshold (Niblack, 1986; Sauvola and Pietikäinen, 2000;

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Figure 5.19 The four texture features of marine radar images: (a) entropy; (b) angular second-order moment; (c) correlation; (d) contrast. Reuse from Liu et al. (2019a) under CC-BY 4.0.

Chapter 5 Oil spill detection based on marine radar

Figure 5.20 Oil spill texture index of marine radar image. Reuse from Liu et al. (2019a) under CC-BY 4.0.

Figure 5.21 Rough extraction of oil spill area.

White and Rohrer, 1983; Yanowitz and Bruckstein, 1989; Wall, 1974), which have been widely applied in image segmentation. For instance, Fuzzy c-means (FCM) segmentation algorithm can effectively deal with nonuniform illumination images. However, it has low noise resistance and is very sensitive to parameters. The adaptive threshold segmentation algorithm of multidirectional gray fluctuation transform constructs the multi-directional gray change matrix, and then reduces the dimension by principle component analysis method to reduce the interference of nonuniform illumination, and finally uses Otsu method for global segmentation. This gray transformation method is also very sensitive to parameters, especially the selection of gray fluctuation threshold. The Water Flow model reduces the interference of uneven illumination on the image by estimating the text in the image background and then carries out global segmentation. For this method, the image surface is regarded as a three-dimensional terrain. Assuming that the image is composed of dark target and bright background, the text will be in the valley in the terrain, and then the background can be extracted by accumulating water in the valley. Based on the Water Flow model, an adaptive minimum error threshold segmentation algorithm is proposed. The Water Flow model is

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Figure 5.22 Oil spill information extraction using adaptive threshold.

used to remove the nonuniform illumination information firstly and then the minimum error algorithm is applied for global segmentation. The preprocessed marine radar image is segmented based on the adaptive threshold method threshold, and the results are shown in Fig. 5.22. There are many errors introduced by the adaptive threshold which are characterized as the transitional area from bright to dark in the radar image. Therefore, it is still difficult to get reasonable oil spill information by using the adaptive threshold method only. Adaptive threshold can be combined with texture analysis. Based on the rough extraction of texture analysis, accurate extraction is carried out through adaptive threshold. The oil spill information extraction results in Fig. 5.21 are shown in Fig. 5.23. The extraction results are drawn in a polar coordinate system, as shown in Fig. 5.24.

5.3.2.4 Classification by machine learning algorithm After processing using the proposed texture index, the marine radar image can be divided into two categoriesdone is the area containing oil spill and the other is the area without oil spill. To separate the area containing oil spill, machine learning is introduced. In this process, we do not need to test multiple thresholds

Figure 5.23 Oil spill information extraction by combining texture analysis and adaptive threshold.

Chapter 5 Oil spill detection based on marine radar

Figure 5.24 Oil spill information extraction in polar coordinate system.

to pick the best one for oil spill segmentation. In machine learning, several methods can be used for classification, and the following methods will be discussed in this section: Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Linear Discriminant Analysis (LDA), and Ensemble Learning (EL). 5.3.2.4.1 SVM SVM has been successfully used for classification (David and Lerner, 2005; Wang et al. 2011, 2012) and pattern recognition (Burges, 1998). To separate two classes by SVM, the goal is to find the hyperplane that maximizes the minimum distance between any data point. Suppose there is a training dataset contain l ing l points xi ; yi i , with the input data xi ˛Rn and the corresponding data yi ˛f1; þ1g as two classes. In feature space, SVM models can be expressed as: yðxÞ ¼ uT 4ðxÞ þ b;

(5.14)

where u is a weight vector of the same dimension as the feature space, 4ð$Þ is the nonlinear mapping to map the input vector into a so-called higher dimensional feature space, and b is the bias.

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The expression used for classification using the kernel trick is  X ai yi K ðx; xi Þ þ b ; (5.15) yðxÞ ¼ sign     where ai are support vectors, and K xi ; xj ¼ 4ðxi ÞT 4 xj is the kernel trick. Several types of kernels, such as linear, polynomial, splines, radius-based function, and multilayer perceptions, can be used within the SVM. 5.3.2.4.2 k-NN The k-NN algorithm is one of the most commonly used classifiers (Samworth, 2012; Decaestecker et al., 1997). Suppose datasets ðX1 ; Y1 Þ, ðX2 ; Y2 Þ, ðX3 ; Y3 Þ,. are independent and identically distributed pairs taking values in Rd . A classifier C is a Borel measurable function from Rd to {1, 2}, with the interpretation that the point x˛Rd is classified as belonging to class CðxÞ. Let ðXð1Þ ; Yð1Þ Þ, ., ðXðnÞ ; YðnÞ Þ denote a permutation of the training sample ðX1 ; Y1 Þ, ., ðXn ; Yn Þ such that k Xð1Þ  x k . k XðnÞ  x k, where k $ k is an arbitrary norm on Rd . We define the k-NN classifier to be 8 k I fY ¼ 1g > P 1 > ðiÞ < 1; if  ; kNN 2 k C ðxÞ ¼ (5.16) i¼1 > > : 2; otherwise; where I is the group indicator vector. In the k-NN method, each sample belonging to the test dataset is classified according to the closest k samples belonging to the training dataset. Among the class numbers obtained from k samples, the class having the maximum number is determined as the class of the sample (Decaestecker et al., 1997). The distances between the test samples and the training samples can be calculated as Euclidean, Manhattan, and Hamming. 5.3.2.4.3 LDA LDA is a normally used method for classification (Ng et al., 2011; Peng and Luo, 2016; Ye et al., 2015). Suppose a set of N samples {x1 , x 2 , ., x N } are in an n-dimensional space, and the

Chapter 5 Oil spill detection based on marine radar

samples belong to m classes {C1 , C2 , ., Cm }. The between-class scatter Sb and within-class scatter Sw are defined as: Sb ¼

m X

Ni ðmi  mÞðmi  mÞT ;

(5.17)

i¼1

Sw ¼

m X X

ðx k  mi Þðx k  mi ÞT ;

(5.18)

i ¼ 1 x k ˛Ci

where m is the total sample mean vector, mi is the mean sample of class Ci , and Ni is the number of samples in class Ci . LDA finds a projection matrix A by maximizing the following objective function:   T A Sb A . ALDA ¼ arg max  T (5.19) A  A S w A According to Eq. (5.19), LDA seeks directions on which data points of different classes are far from each other while requiring data points of the same class to be close to each other. 5.3.2.4.4 Ensemble Learning (EL) EL is an effective method to enhance classification performance and accuracy in the remote sensing community (Samat et al., 2014; Wu et al., 2012; Merentitis et al., 2014). The EL method is a paradigm of machine learning, and it focuses on using multiple learners to solve a problem. In the EL method, instead of finding the best individual learner to solve the problem, better performance can be reached by combining multiple weak learners’ outputs. An EL system is constructed by two key componentsda specific strategy to build an ensemble using diverse classifiers, and a rule to combine multiple outputs. The diversity of multiple classes is the foundation for constructing an effective EL system. The normally used method supports different training sets produced by a resampling technique, such as Boosting and Bagging. 5.3.2.4.5 Oil spill detection on the texture analyzed image using machine learning algorithm and adaptive threshold method To carry out machine learning methods, training data are first needed to gauge the parameters in different machine learning methods. Then, classification is carried out by the trained model of machine learning methods. On the image of the proposed texture index, the selected data for training is shown in

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Figure 5.25 Selected training data for four machine learning methods, where green polygons are training data of water and blue polygons are training data of oil spills. Reuse from Liu et al. (2019a) under CC-BY 4.0. For interpretation of the references to color in this figure legend, please refer online version of this title.

Fig. 5.25. The data in the blue polygons are used as oil spills, and the data in the green polygons are taken as water. In the SVM method, the type of kernel is polynomial with a degree as 3. In the k-NN method, the value of k is 10 and the Euclidean distance is used. In the EL method, the Logit Boost method is adopted, after which a decision tree learner is used. Parameters used in the LDA method are deduced by training data. Based on training information and set parameters, the oil spill detection carried out by SVM, k-NN, LDA, and EL is shown in Fig. 5.26. In Fig. 5.26, white areas are oil spills and black areas are water. In machine learning, the image of the texture index is pooled by texture analysis. Therefore, the detected region of oil spills on the image of the texture index should be restored to the original size. The restored areas containing oil spills extracted by four machine learning methods are shown in Fig. 5.27. As shown in Fig. 5.27, oil spill areas detected by textural analysis and machine learning contain lots of water area. Hence, fine measurements of oil spills are required. To obtain accurate oil spill information based on selected regions by textural analysis and machine learning, the adaptive thresholding method was used. The spilled oil floating on the water appears as continuous zones. If detected oil spill areas were small and not continuous, they were determined as incorrect extraction and removed as noise. On the voyage, ship wake disturbs sea surface considerably, resulting in a dark area in radar images. Therefore, areas containing the ship wake cannot be distinguished from oil spills area on the marine radar image. In our research, the areas containing the ship wake were excluded from oil spill detection. This area was set from 30 degrees portside to 30 degrees starboard in the stern. Finally, the fine measurements of oil spills in the Cartesian coordinate are shown in Fig. 5.28, and the detected oil spills plotted on the original radar image are shown in Fig. 5.29.

Chapter 5 Oil spill detection based on marine radar

Figure 5.26 Oil spill segmentation based on the image of proposed texture index by four machine learning methods: (a) Support Vector Machine (SVM); (b) k-Nearest Neighbor (k-NN); (c) Linear Discriminant Analysis (LDA); and (d) Ensemble Learning (EL). Reuse from Liu et al. (2019a) under CC-BY 4.0.

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Figure 5.27 Selected areas containing oil spills and the surrounding water on the marine radar image in its original size by four machine learning methods: (a) SVM, (b) k-NN, (c) LDA, (d) EL. Reuse from Liu et al. (2019a) under CC-BY 4.0.

Chapter 5 Oil spill detection based on marine radar

Figure 5.28 Fine measurement results of oil spills (shown in white) based on coarse extracted areas by four machine learning methods in the Cartesian coordinate: (a) SVM, (b) k-NN, (c) LDA, (d) EL. Reuse from Liu et al. (2019a) under CC-BY 4.0.

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Figure 5.29 Fine measurements of oil spills (shown in red) marked on the marine radar image in the polar coordinate based on coarse extracted areas by four machine learning methods: (a) SVM, (b) k-NN, (c) LDA, (d) EL. Reuse from Liu et al. (2019a) under CC-BY 4.0. For interpretation of the references to color in this figure legend, please refer online version of this title.

Chapter 5 Oil spill detection based on marine radar

References Björn, L., Clarence, O., Collins, I., et al., 2016. Multi-directional wave spectra from marine X-band radar. Ocean Dynam. 66 (8), 973e988. Burges, C.J.C., 1998. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121e167. David, A., Lerner, B., 2005. Support vector machine-based image classification for genetic syndrome diagnosis. Pattern Recogn. Lett. 26, 1029e1038. Decaestecker, C., Salmon, I., Dewitte, O., Camby, I., Ham, V., Pasteels, J.L., Brotchi, J., Kiss, R., 1997. Nearest-neighbor classification for identification of aggressive versus nonaggressive low-grade astrocytic tumors by means of image cytometry-generated variables. J. Neurosurg. 86, 532e537. Liu, P., Li, Y., Liu, B., Chen, P., Xu, J., 2019a. Semi-automatic oil spill detection on X-band marine radar images using texture analysis, machine learning, and adaptive thresholding. Rem. Sens. 11 (7), 756. Liu, P., Li, Y., Xu, J., Wang, T., 2019b. Oil spill extraction by X-band marine radar using texture analysis and adaptive thresholding. Remote Sens. Lett. 10 (6), 583e589. Liu, R., Liu, X., 1991. Automatic preventing collision information processing system connected with ARPA. J. Dalian Maritime College 17 (1), 50e55 (in Chinese). Merentitis, A., Debes, C., Heremans, R., 2014. Ensemble learning in hyperspectral image classification: toward selecting a favorable bias-variance tradeoff. IEEE J-STARS 7, 1089e1102. Ng, M.K., Liao, L.Z., Zhang, L., 2011. On sparse linear discriminant analysis algorithm for high-dimensional data classification. Numer. Lin. Algebra Appl. 18, 223e235. Niblack, W., 1986. An Introduction to Digital Image Processing. Prentice Hall, Upper Saddle River, NJ. Otsu, N., 1979. Threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62e66. Peng, J., Luo, T., 2016. Sparse matrix transform-based linear discriminant analysis for hyperspectral image classification. Signal Image Video 10, 761e768. Pinel, N., Dechamps, N., Bourlier, C., 2008. Modeling of the bistatic electromagnetic scattering from sea surfaces covered in oil for microwave applications. IEEE Trans. Geosci. Rem. Sens. 46 (2), 385e392. Roth, S., Black, M.J., 2009. Fields of experts. Int. J. Comput. Vis. 82, 205e229. Samat, A., Du, P., Baig, M.H.A., Chakravarty, S., Cheng, L., 2014. Ensemble learning with multiple classifiers and polarimetric features for polarized SAR image classification. Photogramm. Eng. Rem. 80, 239e251. Samworth, R.J., 2012. Optimal weighted nearest neighbour classifiers. Ann. Stat. 40, 2733e2763. Sauvola, J., Pietikäinen, M., 2000. Adaptive document image binarization. Pattern Recogn. 33, 225e236. SeaDarQ, 2015. http://www.seadarq.com/seadarq/news/norbit-aptomaracquires-the-seadarq-radar-system-for-environmental-monitoring. Shen, J., Li, Y., Dai, Y., et al., 2011. Reaearch on co-channel interference suppression algorithm for X band radar images. Chin. J. Sci. Instrum. 32 (5), 1089e1094. Tang, Y., 2010. Research on key techniques of wave remote sensing based on marine radar, Ph.D Dissertation, Harbin Engineering University.

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Ulaby, F.T., Kouyate, F., Brisco, B., Williams, T.H.L., 1986. Textural infornation in SAR images. IEEE Trans. Geosci. Rem. Sens. 24, 235e245. Wall, R.J., 1974. The gray level histogram for threshold boundary determination in image processing to the scene segmentation problem in human chromosome analysis (Ph.D. Thesis). University of California at Los Angeles, Los Angeles, CA, USA. Wang, X.Y., Wang, Q.Y., Yang, H.Y., Bu, J., 2011. Color image segmentation using automatic pixel classification with support vector machine. Neurocomputing 74, 3898e3911. Wang, X.Y., Zhang, X.J., Yang, H.Y., Bu, J., 2012. A pixel-based color image segmentation using support vector machine and fuzzy C-means. Neural Network. 33, 148e159. Wei, B., 2008. Improved adaptive median filtering. Comput. Appl. 28 (7), 1732e1734. White, J.M., Rohrer, G.D., 1983. Image thresholding for optical character recognition and other applications requiring character image extraction. IBM J. Res. Dev. 27, 400e411. Wu, Q., Wang, L.W., Wu, J., 2012. Ensemble learning on hyperspectral remote sensing image classification. Adv. Mater. Res. 546e547, 508e513. Yanowitz, S.D., Bruckstein, A.M., 1989. A new method for image segmentation. Comput. Graph. Image Process. 46, 82e95. Ye, Q., Ye, N., Yin, T., 2015. Fast orthogonal linear discriminant analysis with application to image classification. Neurocomputing 158, 216e224. Zhang, X., Zhang, S., 2016. Diffusion scheme using mean filter and wavelet coefficient magnitude for image denoising. AEU- Int. J. Electron. Commun. 70I (7), 944e952. Zhang, W., Li, J., 2007. Analysis and study on radar shared frequency interference phenomenon. Fire Control Radar Technol. 6 (2), 50e53. Zhu, X., Li, Y., Feng, H., Liu, B., Xu, J., 2015. Oil spill detection method using Xband marine radar imagery. J. Appl. Remote Sens. 9, 095985.

6 Oil spill detection based on SAR 6.1 Remote sensors and sensing platforms As discussed in Chapter 5, the existence of oil film can reduce the backscattering coefficient received by the radar, so it appears as a low gray value area (black area) in the radar image. According to the mounting platform, the radars used for oil spill detection can be classified as spaceborne radar, airborne radar, marine radar, and ground-based radar. Synthetic aperture radar (SAR) is a high-resolution active microwave sensor, which is not limited by light and climate conditions. It has the characteristics of all-day and all-weather earth observation and has been widely applied to the fields of agriculture and forestry, water area, geology, natural disasters, and military. Since the United States launched the first spaceborne SAR (SeaSat) in 1978, E.S.A., Russia, Germany, Japan, and Canada have also successfully launched spaceborne SAR. The typical spaceborne SAR systems are shown in Table 6.1. Many researchers have done a lot of research in the field of sea surface oil spill monitoring using SAR and made fruitful achievements. Bern et al. (1993) used ERS-1 data for sea surface oil spill monitoring. Pavlakis et al. (1996) also used ESR data to monitor the oil spill accident in the Mediterranean Sea area. Margany used SAR images to analyze the major oil spill events in the Strait of Malacca and the Gulf of Mexico.

6.2 Principles of oil spill detection based on SAR 6.2.1 SAR imaging SAR is an active microwave imaging sensor, in which the radiation unit antenna is synthesized into a larger equivalent antenna. It obtains the image and analyzes the microwave scattering characteristics of the ground object targets based on the relative motion between the load satellite and the target. Compared with real aperture radar (RAR), which relies on Oil Spill Detection, Identification, and Tracing. https://doi.org/10.1016/B978-0-443-13778-5.00004-7 Copyright © 2024 Elsevier Inc. All rights reserved.

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Table 6.1 Spaceborne SAR systems.

Name

Ownership

Launching year

Bands

Spatial resolution

Seasat ERS-1 JERS-1 ERS-1 Almaz-1 RadarSat-1 ENVISAT(ASAR) ALOS(PALSAR) TerraSAR RadarSat-2 Cosmo-Skymedl RISAR HJ-1-C TerraSAR-X

U.S. E.S.A. Japan E.S.A. Russia Canada E.S.A. Japan Germany Canada Italy India China Germany

1978 1991 1992 1991 1991 1995 2002 2006 2007 2007 2007 2009 2009 2014

L C L C S C C L X C X X S X

25 m 25 m 20 m 25 m 10e15 m 9m 30 m 10 m 3 m/1 m 3m 3 m/1.5 m 1m 5m 0.5 m

increasing the antenna length to improve the azimuth resolution, SAR improves the range resolution and azimuth resolution and obtains high-resolution SAR image through the range compressed microwave pulse and the Doppler frequency shift of azimuth scattering signal, respectively. The azimuth resolution derived from Doppler effect is given as Eq. (6.1): Dx ¼

lR 2D sin j

(6.1)

where l is the wavelength of electromagnetic wave; R is the distance from the SAR sensor to the observation point; D is the moving disting of SAR sensor during sampling; j is the antenna beam azimuth. The range resolution determined by the pulse is given as Eq. (6.2): Dy ¼

Ds 2 sin q

Ds ¼ ct where Ds is the pules width, which is defined as Eq. (6.3):

(6.2) (6.3)

Chapter 6 Oil spill detection based on SAR

Ds ¼ ct

(6.4)

where t is the pules during; c is the speed of electromagnetic wave. SAR system is not only an active sensor working in microwave band but also a high-resolution imaging radar with pulse Doppler technology. Due to the unique detection advantages of microwave, it can operate normally both in day and night. In addition, the system is not affected by bad weather conditions such as cloud, rain, and fog (Lee and Pottier, 2017). The ground observation geometry of SAR system is shown in Fig. 6.1. The geometric variables involved in Earth observation are given as follows: H: height of SAR system; V: speed of SAR system; R: distance from SAR to the central point of the observation area; it is also called ray axis or radar line of sight; q: Incident angle of antenna beam, which is defined as the angle between radar antenna beam and vertical direction; j: antenna beam azimuth, which is defined as the included angle between radar antenna beam and satellite flight direction;

Figure 6.1 The ground observation geometry of SAR system.

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Dx: width of radar antenna beam irradiation area in x direction.

6.2.2 Polarized SAR imaging of oil spill A polarimetric SAR system records the target echo signals under different combinations (e.g., amplitude information and phase difference information) through the transmission and reception of pulse signals between horizontal polarization antenna array elements and vertical polarization antenna array elements, and can obtain the comprehensive polarization information of sea surface targets. In the process of transmitting and receiving signals, the vertical and horizontal polarization antenna array elements alternately transmit H polarization pulses and V polarization pulses at a time interval of half a pulse. The H and V polarization antenna array elements receive them at the same time. The radar processor performs synthetic aperture integration on the H and V echo pulses, respectively. Although there is a half-pulse time interval between the pulses transmitted in the two polarization directions, it is negligible relative to the pulse time order of the polarimetric SAR system. Therefore, it is still considered that the two groups of transmitted pulses of the polarimetric SAR system are transmitted at the same time. The polarimetric SAR system uses this working mode to record the echo information of oil film, oil-like film, and background seawater under different polarization combinations (Fig. 6.2). The polarization scattering matrix is usually calculated in order to achieve the mining and processing of oil film scattering information, which can effectively reduce the false alarm rate while improving the oil film detection ability.

6.2.3 Influence factors of oil spill detection based on SAR According to the principle of SAR imaging, the effect of sea surface oil spill detection will be affected by many factors including radar parameters, sea surface roughness, and scattering structure. These will be discussed in detail as follow. According to the order of wavelength from long to short, the radar satellite works in the microwave band can be divided into six bands: P, L, S, C, X, and K. The backscattering coefficient of oil film will be different in different wavebands. Alpers and € hnerfuss (1989) verified through experiments that the backHu scattering coefficients of background seawater and oil film on SAR images in L, C, and X bands are quite different and have

Chapter 6 Oil spill detection based on SAR

Horizontal receiver

Horizontal transmit

Transmitter

Vertical receiver

Vertical transmit Figure 6.2 Measurements of polarized SAR system.

obvious contrast. On one hand, when the wavelength is long, the penetration becomes stronger, and objects below the sea surface may be detected and affect the detection effect. Therefore, P band is not suitable for monitoring oil spills. On the other hand, when the wavelength is short, the penetration become weak, and often can not penetrate the atmosphere and clouds. Therefore, K band is not suitable for monitoring oil spills. As the result, the radar satellites in L, C and X bands are more suitable for oil spill monitoring. There are four main polarization modes of radar satellites: HH, HV, VH, and VV polarization. VV polarization is vertical emission with strong echo, which is suitable for areas with flat surface; HH polarization is horizontal emission with weak echo, which can be used in areas with uneven surface. Moreover, since VV polarization is more suitable for C band radar wave, especially when the wind field is strong, it is most suitable mode for monitoring sea surface oil spill (Zheng et al., 2017). Different polarization modes also have different imaging resolutions. The common resolutions are 150 m, 100 m, 50 m, 30 m, 25 m, 16 m, 10 m, 3 m, and 1 m. The higher the resolution, the

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better the monitoring effect but also higher cost. The latter three high resolutions are less used because they are expensive. Due to their large width, SAR images at the resolution of 150 m, 100 m, and 50 m are suitable for monitoring large-scale oil spill events; while those of 30 m, 25 m, and 16 m are suitable for monitoring small oil spill accidents and illegal sewage discharge from ships. Considering the resolution and cost, 30 m resolution is usually suitable for monitoring sea surface oil spill. The radar backscattering model is also different with different incident angles. Generally, when the incident angle is between 0 degrees and 20 degrees, the long-term incident electromagnetic wave is less than the sea surface roughness, which is suitable for the specular scattering model. When the incident angle range is between 20 degrees and 45 degrees, the electromagnetic wavelength is consistent with sinusoidal energy of the sea surface high power spectrum to produce resonance, which is suitable for the Bragg scattering model. Su (2013) analyzed the effects of incidence angle and wind speed on backscattering characteristics using the monitoring image of Envisat ASAR satellite in the oil spill accident in the Gulf of Mexico in 2010. The experimental results showed that the suitable incidence angle range was between 28 degrees and 36 degrees. If the wind speed is too low, a large area of low wind speed area is formed on the image, which is also a black area. It is difficult to distinguish whether there is oil film under low wind speed conditions. If the wind speed is too high, only the thick oil film is visible, and the thin oil film is scattered by the waves, which can not damp the sea Bragg wave and be extracted from the image. The optimum wind speed range on the sea surface is 3e10 m/s. The oil on the sea surface would usually drift and diffuse under the action of ocean advection, turbulence, shear flow, and its own gravity. At the same time, it would continue to undergo a series of physical and chemical changes, including evaporation, dissolution, dissipation, emulsification, sedimentation, biodegradation, and oxidation. Therefore, the oil with a short life cycle is not conducive to be detected. According to the SAR imaging principle, the targets that can reduce the roughness of the sea surface and the backscattered echo signal received by the radar sensor appear as dark black areas in the SAR image. Among them, the main interference factor to distinguish the oil spill information is the false target in the surrounding background environment. There are many kinds of SAR oil spill false targets with complex causes. Typical false targets include marine internal wave, low wind speed area, sea

Chapter 6 Oil spill detection based on SAR

ice, ship wake, biofilm, estuarine impact area, rainfall area, etc. Similar to as actual oil spill, the false target can reduce the roughness of the sea surface and the backscattering energy received by the radar sensor, which also appears black area on the radar image. The existence of false targets will affect the judgment and extraction of oil spill information on the sea and delay the emergency treatment of oil spill on the sea. Therefore, the effective distinction between oil spill and false target is the key and guarantee to achieve SAR oil spill automatic detection.

6.3 Oil spill detection process based on SAR 6.3.1 SAR image preprocessing The radar echo signal is affected by the coherence of background clutter and electromagnetic wave. As the result, the formation of SAR image usually can not truly reflect the original information of the target, which negatively affects the effect of recognition. Therefore, in order to effectively improve the image quality before using SAR image to identify an oil spill, it needs radiation correction, geometric correction, and filtering.

(1) Radiometric correction Radiation correction is to eliminate and weaken the radiation distortion caused by various factors and determine the mapping relationship between the gray level of the image pixel and the backscattering coefficient of target. Many scholars have studied the corresponding calibration model between the gray value of SAR image pixel and the backscattering coefficient of target unit for different radars. According to the working principle of SAR, Gao and Yao (2002) deduced the quantitative relationship between the gray value of SAR image pixel and the backscattering coefficient of its corresponding ground unit. Minchew et al. (2012) analyzed the unmanned airborne SAR data of the oil spill in the Gulf of Mexico in 2010 and studied the equation of backscattering coefficient. Radiometric correction of SAR image can be divided into internal calibration and external calibration. Internal calibration is to overcome the systematic error caused by sensor response characteristics, and external calibration is to solve the random error in the process of echo measurement. These two calibrations are usually determined by the satellite data-providing department through experiments and then provided to users together with SAR images.

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(2) Geometric correction SAR image is affected by the changes in sensor angle and position, atmospheric refraction, earth rotation, earth curvature, and terrain, which will produce certain geometric distortion. In order to truly reflect the geometric characteristics of the target, geometric correction is a necessary process. The geometric correction methods of SAR images mainly include polynomial correction based on ground control points and collinear equation correction.

(3) Filter processing SAR echo signal is formed by coherent processing of multiple scatterer echoes of multiple continuous radar pulses. Due to the relative motion between the radar sensor and the target, as well as the roughness of the target surface, the signal received by SAR is the combination and superposition of multiple scattered echoes. In this process, the scattered electromagnetic wave signal will interfere. If the echo phase is consistent, the electromagnetic echo signal will strengthen, and vice versa. It appears as a granular texture with black and white dots in SAR image, which is also called speckle noise. In order to improve the quality of SAR image and detection results, it is essential to suppress speckle noise effectively. Many scholars have carried out the studies on SAR image filtering and proposed some mature filtering algorithms, each of which has its own advantages and disadvantages. Some common filtering methods include mean filtering, median filtering, Lee filtering, sigma filtering, frost filtering, gamma map filtering, and wavelet filtering. In practical application, the most suitable and effective filtering method is selected according to the application purpose of the image.

6.3.2 SAR image segmentation Due to the differences in the backscattering coefficients of oil film and seawater, their gray values on SAR images are also different: the oil film area appears as a black area in SAR image. Before oil film recognition and analysis, high-quality SAR image segmentation is an important process. Common segmentation methods include threshold segmentation, edge segmentation, region segmentation, segmentation based on intelligent algorithm, etc.

Chapter 6 Oil spill detection based on SAR

6.3.2.1 Threshold segmentation Threshold segmentation is one of the most commonly used image segmentation methods. It achieves segmentation by comparing the gray values of each pixel in the image with a gray value threshold. The key to this method is to determine an appropriate threshold. Some common methods include the peak valley method of gray histogram, the minimum error method, the maximum interclass variance method, and the maximum entropy automatic threshold method, which can be classified as a global threshold, adaptive threshold, and optimal threshold. Threshold segmentation method has the advantage of efficiency, but also has poor universality and is not suitable for images with complex changes in gray value (Otsu, 1979).

6.3.2.2 Edge segmentation Edge segmentation is an important method of image segmentation. It achieves the segmentation by locating the discontinuous mutation at the edges within the image This discontinuity at the edges can be detected by calculating the derivative of the gray value of the pixels (e.g., the position of the step edge corresponds to the extreme point of the first derivative or the zero intersection of the second derivative). Differential operators are usually used as templates for convolution operations with images for edge detection. Some commonly used differential operators include Roberts operator, Prewitt operator, Canny operator, Sobel operator, Laplace operator, LOG operator, and Kirsh operator. Because the operators used in edge segmentation are usually sensitive to noise, it is only suitable for low noise and less complex images.

6.3.2.3 Region segmentation Region segmentation method mainly includes the region growth method and split-merge method. The specific process of region growth method is: first, find a pixel in the region to be segmented as the growth seed; then expand from the seed pixel to the surrounding neighborhood according to the predetermined growth criteria, and merge pixels with the same or similar properties according to the similarity principle; after that, these new pixels continue to grow and merge as new seeds; finally, when no new pixel satisfies the same or

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similar conditions, the loop exits and a segmented region is formed. The algorithm is slow and needs to initialize the seed manually, so it is suitable for more uniform and smaller connected regions. The specific process of split-merge method is: first, determine a criterion for splitting and merging; if the characteristics of the image are inconsistent, divide the image into multiple subregions, and continue to segment each subregion until it can no longer be segmented; after that, determine whether the adjacent subregions meet the similar features; if so, they are combined into a large region; when all subregions are judged, the segmentation effect is finally achieved. The algorithm is suitable for complex image segmentation, but the computational complexity of this algorithm is high, and the boundary of the region may be destroyed.

6.3.2.4 Segmentation based on intelligent algorithm The artificial intelligent algorithms have received fast development in that past few decades, and have been applied to image segmentation. For example, image segmentation based on morphology, clustering algorithm, wavelet transform, genetic algorithm, neural network, etc.

6.3.3 Oil spill detection based on polarized SAR How to effectively identify oil film targets and eliminate the interference of look-alikes and sea clutters information is the key to oil spill detection based on SAR. The traditional singlepolarized SAR system limits the polarization mode of the combination of transmitting and receiving antennas. It can only transmit information through a fixed polarization antenna and receive echo signal components consistent with its polarization direction. Oil spill targets and oil-like films show similar low backscattering areas on the single-polarized SAR image, resulting in misclassification. Due to this limitation, the current research and application of oil spill identification detection tend to be based on multi-polarization SAR images. The development of dual-polarized SAR system connects single-polarized system and fully polarized SAR system. Although it only obtains partial polarization information, it has been widely used in marine oil spill detection because it can obtain single

Chapter 6 Oil spill detection based on SAR

polarization amplitude information and partial polarization information on the premise of balancing system workload. Extracting regions of interest from wide images can effectively reduce the amount of computation. The traditional visual saliency detection is often more sensitive to the targets with high brightness and strong boundary differences in the image, such as sea land boundary, island, ship, and so on. However, for SAR image oil spill detection, the oil spill area with “dark spot” feature is the area that users pay more attention to, and the targets such as islands and ships with bright features are regarded as clutter information. In addition, many oil-like films caused by natural phenomena on the sea surface, such as low wind speed area on the sea surface, atmospheric front caused by local wind stress, leeward Cape, internal ocean wave, ship wake, rain mass, ebb tide beach, and upwelling, also show similar dark spot characteristics, which affect the detection, identification, and interpretation of oil spills. With the in-depth study of dual-polarized SAR oil spill detection, the marine oil spill detection based on a single time phase has gradually expanded to long time series, which can obtain the temporal and spatial changes of the oil spill, and better evaluate the continuous changes, frequency distribution, and regional risk level of oil spill. Combined with the characteristics and imaging mechanism of oil spill targets in SAR images, it is the key content of multi-temporal SAR oil spill detection to construct an oil spill region of interest extraction algorithm in SAR images, and comprehensively analyze the morphological changes of oil spill under spatiotemporal conditions according to the extraction results. Marine oil spills can be detected and extracted in fully polarization SAR image using polarization characteristic parameters, which is also the core link to identify and classify oil spills based on the differences of scattering characteristics between oil film, oil-like film, and sea water. Therefore, the construction and extraction of effective polarization characteristic parameters can enhance the contrast of oil film with seawater and oil-like film, expand the polarization characteristic space, and thus effectively identify the oil film. Different types of polarization characteristic parameters proposed in many studies have been successfully applied to oil spill detection research. Extracting high-quality polarization characteristic parameters, further suppressing false alarm signals, and highlighting oil film signals

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based on expanding polarization characteristic space is the focus issues in oil spill detection research based on fully polarized SAR.

6.3.3.1 Extracting polarization characteristic parameter Polarized SAR system can simultaneously transmit and receive target echo signal pulses of different polarization channels, and record the amplitude and phase difference information of combined echoes of different polarization channels in the form of a scattering matrix. This improves the ability of ground object target recognition. Oil spill can be distinguished based on the differences in scattering mechanisms and characteristics of the polarization feature space between the marine oil film area and the surrounding background seawater. Cloude and Pottier (1997) constructed H/a plane and used it to classify SAR images. This study was also the basis of subsequent parameter extraction and scattering characteristic analysis. With the wide application and expansion of polarization data in recent years, many studies have expanded and improved the traditional parameter extraction methods. Zhang et al. (2011) extended the consistency coefficient of soil moisture estimation to oil spill detection and found that the consistency coefficients of oil film and background seawater are positive and negative, respectively. Combined polarization parameters can analyze the characteristics of different composition parameters and enhance the separability between different targets through mathematical transformation. It has been effectively applied in oil spill detection. Liu (2012) proposed a novel characteristic parameter based on polarization coherence coefficient, entropy, anisotropy, and scattering angle, and combined it with Otsu threshold segmentation method to effectively extract oil spill. The combined parameters based on polarization scattering entropy H and anisotropy A have been widely used in oil spill detection and analysis to expand the polarization parameter system and improve the ability to distinguish different scattering types. Schuler and Lee (2006) built H_ A combination characteristics are used for oil spill detection analysis. The parameters that can increase the probability of oil spill detection are explored according to several mathematical combinations of parameters, including HA, H (1  A), A (1  H), (1  H) (1  A). Cai et al. (2016) analyzed and compared the oil spill characteristics based on the combined characteristic spectrum of polarization scattering entropy H and anisotropy A, respectively. Zou et al. (2016) further verified the effectiveness of combined H_ A features. In addition, some studies introduced improved characteristic parameters to expand the polarization characteristic space.

Chapter 6 Oil spill detection based on SAR

Skrunes et al. (2018) used the two maximum eigenvalues of the coherence matrix to improve the definition of traditional anisotropy and obtained the improved anisotropy parameter A12. This parameter that is defined by the two minimum eigenvalues is different from the traditional anisotropy parameter A, and its effectiveness and robustness were proved by different oil spill detection studies.

6.3.3.2 Oil spill detection and analysis based on combined characteristic parameter The research on oil spill detection based on polarization characteristic parameters is gradually increasing, and new parameters are constantly proposed and applied. The previous research has summarized and classified the parameters according to the definition of parameters and the principle of oil spill detection. This book further classifies them into the following four categories: characteristic parameters defined based on backscattering energy, the correlation between polarization channels, scattering mechanism, and parameter combination. Polarized characteristic parameters defined based on backscattering energy: Because the sea surface is mainly dominated by Bragg scattering mechanism, it has sufficient capillary wave and short gravity wave, which introduces strong backscattering energy. The existence of oil film, however, has a certain damping effect on the capillary wave and short gravity wave on the sea surface, resulting in the difference in backscattering intensity between them. Therefore, the polarization parameters developed by quantifying the differences in scattering energy between clean and oil polluted seawater can theoretically detect oil spill. Some parameters in this category including surface scattering fraction (Singha and Ressel, 2016), geometric intensity parameters (Skrunes et al., 2014), copolarization ratio (Minchew et al., 2012; Skrunes et al., 2014; Espeseth et al., 2017), etc. Polarized characteristic parameters are defined based on the correlation between polarization channels: The differences of ground objects are reflected in the differences of correlation between channels. For example, the correlation between the sea surface area covered by oil film and the polarization channel is low, while the correlation between the same polarization channel on the clean sea surface is high. Therefore, oil spill targets can be detected based on the correlation difference between channels. Some parameters in this category including order of copolarization correlation coefficient r_co (Wang et al., 2010; Skrunes et al., 2014, 2018), real part of the cross product of copolarization

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r_co (Skrunes et al., 2014), consistency coefficient (Skrunes et al., 2014; Zhang et al., 2011), etc. Polarized characteristic parameters defined based on scattering mechanism: The dominant scattering mechanism on the ocean surface is Bragg surface scattering, while the existence of oil film leads to a complex scattering mechanism on the ocean surface. The polarization characteristics parameters proposed based on the difference of scattering mechanism between oil film and sea surface mainly include polarization parameters obtained from Coude decomposition method H/A/a (Skrunes et al., 2014), the improved anisotropy parameter A12 (Migliaccio et al., 2009; Minchew et al., 2012), the angle of maximum eigenvalue a, Datum height calculated from eigenvalues PH (Cloude and Pottier, 1997; Nunziata et al., 2011; Skrunes et al., 2014), etc. Polarized characteristic parameters defined based on parameter combination: A variety of parameters are proposed in the recent studies by mathematically combining and transforming the commonly used parameters (Schuler and Lee, 2006). These parameters were derived according to the oil spill detection ability of several parameters and were expected to expand the contrast between oil film and seawater to improve the discrimination ability between targets. For example, Liu (2012) forms a new combination parameter of H_ A based on polarization entropy, scattering angle, improved anisotropy, and the order of copolarization correlation coefficient. It should be noted that the combined polarization parameters are based on the combination of several characteristic parameters, and its scattering mechanism may be closer to the characteristics of a component parameter. Some commonly used polarized characteristic parameters that follow the four categories are shown as Table 6.2:

6.3.4 False target recognition technology 6.3.4.1 Direct analysis Geometric feature is an effective method to distinguish oil spill from false target. The oil spill on the sea is affected by natural forces (weather, ocean current, etc.), and its shape is relatively smooth and changes continuously with time. The characteristics of false targets are not uniform and have various characteristics. They usually appear as fuzzy boundary in the image, accompanied by other features. For example, the oil spill area is likely to produce smooth bending under the action of wind or ocean current and diffuse into a sector or circle with the continuous action of wind. On the other hand, the natural film is usually spiral. For

Chapter 6 Oil spill detection based on SAR

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Table 6.2 Definition of polarized characteristic parameters.

Category

References

Backscattering energy

s (Singha and Ressel, 2016)

Correlation between polarization channels

Definition

. 2 s ¼ CjSHH þ SVV jD span . 2 2 PR: (Minchew et al., 2012; Skrunes et al., PR ¼ CjSHH jD CjSVV jD 2014; Espeseth et al., 2017)   .qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  D  CjS jD2 CjS jD2  r_co: (Wang et al., 2010; Skrunes et al., r co ¼ CSHH SVV  HH VV  2014, 2018)    r_co: (Skrunes et al., 2014) rN ¼ Re CSHH S D  VV

Scattering mechanism

Parameter combination

A12: (Skrunes et al., 2014) H: (Minchew et al., 2012; Migliaccio et al., 2009) F (Liu, 2012) H(1  A12) (Li et al., 2021)

A12 ¼ ðl1l2 Þ =ðl1þl2 Þ P H ¼ 3i¼1  Pi log3 Pi  Fwang ¼ ½ð1 HÞ þð1 aÞ þA12 þr co 4 Hð1 A12 Þ

the linear black strip without branches and sharp bends, if there are bright spots nearby, it may be a ship or an oil drilling platform. In addition, the false targets can be distinguished by analyzing the boundary contour characteristics of the dark region on the image. Because the spilled oil is viscous, the boundary contour in the SAR image is relatively obvious and gradually blurred with the increase of drift time. The boundary of false targets in SAR images is mostly fuzzy. However, substances with high viscosity may also be other man-made pollutants, and their viscosity will also be reduced as they diffuse and drift on the sea surface. Therefore, artificial pollutants or false targets are difficult to distinguish in practical research, and operators need to have rich experience in distinguishing and identifying them.

6.3.4.2 Correlation analysis In some complicated cases, direct analysis only by shape, texture, and other characteristics is not effective. It’s necessary to obtain information of comprehensive factors such as geographical location, weather conditions, wind field information, and platform distribution in surrounding sea areas. Some examples are discussed in detail as follows. Meteorological data: The recording of wind field information is very important for the detection of oil spill. Only under the

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appropriate wind speed can the oil spill information be visible in the SAR image. If the offshore wind speed is too low, the oil spill area information cannot be captured in the image; If the offshore wind speed is too high, the sea wave is large and the offshore oil spill is dispersed, thus the oil spill area cannot be effectively identified in the image. In addition, the shape of the oil spill area is closely related to the wind. Through the recorded data of wind speed and direction, we can better grasp the drifting status and shape of the oil spill. In addition, the seasonal and ocean current information in the accident area also plays an auxiliary role in distinguishing oil spills from false targets. For example, the existence of sea ice can be excluded in summer; The tidal current can transport the oil film thousands of meters away at a certain time. Geographical condition: Different geographical conditions can produce different SAR images. For example, dark black areas of hundreds of square meters or even larger will be formed near leeward areas such as nearshore waters, oil platforms, and underwater reefs. Generally, the shadow in the nearshore area is a ring around the land, the shadow width is consistence, and the boundary is smooth. The shadow formed by the offshore platform is triangular, and the part near the platform is wide; Therefore, the low wind area can be identified according to the geographical conditions and the characteristics of the dark black area.

References € hnerfuss, H., 1989. The damping of ocean waves by surface films: Alpers, W., Hu a new look at an old problem. J. Geophys. Res. 94 (94), 6251e6265. Bern, T.I., Wahl, T., Andersen, T., et al., 1993. Oil spill detection using satellitebased SAR: experience from a field experiment. Photogramm. Eng. Rem. Sens. 59 (3), 423e428. Cai, Y., Zou, Y., Liang, C., et al., 2016. Research on polarization of oil spill and detection. Acta Oceanol. Sin. 35 (3), 84e89. Cloude, S.R., Pottier, E., 1997. An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans. Geosci. Rem. Sens. 35 (1), 68e78. Espeseth, M.M., Skrunes, S., Jones, C.E., Brekke, C., Holt, B., Doulgeris, A.P., 2017. Analysis of evolving oil spills in full-polarimetric and hybrid-polarity SAR. IEEE Trans. Geosci. Rem. Sens. 55 (7), 4190e4210. Gao, J., Yao, K., 2002. Multifractal features of sea clutter. In: Paper Presented in IEEE Radar Conference, Long Beach, USA, pp. 500e505. Lee, J.-S., Pottier, E., 2017. Polarimetric Radar Imaging: From Basics to Applications. CRC press, Boca Raton.

Chapter 6 Oil spill detection based on SAR

Li, G., Li, Y., Hou, Y., Wang, X., Wang, L., 2021. Marine oil slick detection using improved polarimetric feature parameters based on polarimetric synthetic aperture radar data. Rem. Sens. 13 (9), 1607. Liu, P., 2012. Research on Ocean Oil Spill Detection and Recognition Using SAR Data (Doctoral Dissertation). Ocean University of China (in Chinese). Migliaccio, M., Gambardella, A., Nunziata, F., Shimada, M., Isoguchi, O., 2009. The PALSAR polarimetric mode for sea oil slick observation. IEEE Trans. Geosci. Rem. Sens. 47 (12), 4032e4041. https://doi.org/10.1109/ TGRS.2009.2028737. Minchew, B., Jones, C.E., Holt, B., 2012. Polarimetric analysis of backscatter from the Deepwater Horizon oil spill using L-band synthetic aperture radar. IEEE Trans. Geosci. Rem. Sens. 50 (10), 3812e3830. Nunziata, F., Migliaccio, M., Gambardella, A., 2011. Pedestal height for sea oil slick observation. IET Radar, Sonar Navig. 5 (2), 103e110. Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9 (1), 62e66. Pavlakis, P., Sieber, A.J., Alexandry, S., 1996. Monitoring oil-spill pollution in the Mediterranean with ERS SAR. Earth Obs. Q. 52, 8e11. Schuler, D., Lee, J.-S., 2006. Mapping ocean surface features using biogenic slickfields and SAR polarimetric decomposition techniques. IEE Proc. - Radar, Sonar Navig. 153 (3), 260e270. Singha, S., Ressel, R., 2016. Offshore platform sourced pollution monitoring using space-S fully polarimetric C and X band synthetic aperture radar. Mar. Pollut. Bull. 112 (1), 327e340. Skrunes, S., Brekke, C., Eltoft, T., 2014. Characterization of marine surface slicks by Radarsat-2 multipolarization features. IEEE Trans. Geosci. Rem. Sens. 52 (9), 5302e5319. Skrunes, S., Brekke, C., Jones, C.E., Espeseth, M.M., Holt, B., 2018. Effect of wind direction and incidence angle on polarimetric SAR observations of slicked and unslicked sea surfaces. Remote Sens. Environ. 213, 73e91. Su, T., 2013. Analysis on the influence factor of oil spill detection using EnviSat ASAR. Acta Oceanol. Sin. 32 (4), 467e473. Wang, W., Lu, F., Wu, P., Wang, J., 2010. Oil spill detection from polarimetric SAR image. In: Paper Presented in the 10th IEEE International Conference on Signal Processing Proceedings. Zhang, B., Perrie, W., Li, X., Pichel, W.G., 2011. Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image. Geophys. Res. Lett. 38 (10), 602. Zheng, H., Zhang, Y., Wang, Y., et al., 2017. The polarimetric features of oil spills in full polarimetric synthetic aperture radar images. Acta Oceanol. Sin. 5, 105e114. Zou, Y., Shi, L., Zhang, S., et al., 2016. Oil spill detection by a support vector machine based on polarization decomposition characteristics. Acta Oceanol. Sin. 35 (9), 86e90.

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7 Oil spill detection based on GNSS-R 7.1 Remote sensors and sensing platforms GNSS-R techniques use the reflected navigation satellite pseudo-random code signal or carrier signal, analyze the code delay and correlation function waveform, and extract the information of the target reflection surface carried in the reflected signal (e.g., the change of reflected signal waveform, the change of polarization characteristics, frequency, amplitude, phase, etc.) based on the scattering theory of electromagnetic waves on sea surface and wave. It can be applied to the real-time information extraction and monitoring of offshore targets (Li et al., 2015). As a passive remote sensing technology, GNSS-R can work with airborne, shipborne, satellite-based, or ground-based receiver and does not require a separate signal source transmitter. Compared with other remote sensing technologies, it has the following characteristics. (1) Thanks to the completeness of Galileo system and Beidou system, GNSS system has a lot of on-orbit satellites available. Therefore, there are abundant signal sources for GNSS-R receivers, and a separate signal source transmitter is not necessary. (2) The transmitter and receiver are separated for GNSS-R detection. The receiver can receive the reflected signals from multiple satellites at the same time and has high temporal and spatial resolution. (3) GNSS-R technology has high precision that SAR does not have. Its receiver also has the advantages of small volume, lightweight, low power consumption, and low detection cost (4) GNSS-R can work all day and in real time. It is generally not affected by clouds, rain, and other weather conditions.

Oil Spill Detection, Identification, and Tracing. https://doi.org/10.1016/B978-0-443-13778-5.00011-4 Copyright © 2024 Elsevier Inc. All rights reserved.

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7.1.1 Geometrical structural of GNSS-R GNSS satellite, sea surface, and receiver constitute a separated transceiver structure in the reflected signal measurement of GNSS system (Fig. 7.1). GNSS-R receiver can receive the direct and reflected signals of multiple navigation satellites within a certain range at the same time. In terms of the remote sensing detection mechanism, it adopts the bistatic radar observation mode, which greatly improves the spatial-temporal resolution. In order to receive GNSS-reflected signals with high elevation, the receiver generally needs to use two antennas: an upward low-gain right-handed circular polarization antenna for receiving direct signals, and a downward high-gain left-handed circular polarization antenna for receiving reflected signals. The signal received by GNSS-R receiver belongs to forward scattering. The remote sensing monitoring of sea state information can be achieved through the measurement of sea surface scattering.

GNSS Signal source

Signal transmitter Receiver platform Signal collection

Propagation channel

Remote sensors

Scattering signal

Incident signal

Propagation channel

Scattering on sea surface

Figure 7.1 Schematic graph of the GNSS-R technology.

Chapter 7 Oil spill detection based on GNSS-R

7.1.2 Fresnel reflection coefficient Fresnel reflection coefficient is used to describe the energy relationship between the incident and reflected electromagnetic wave at the interface of two mediums. In the case of the air-seawater interface, it depends on the dielectric constant of seawater, polarization mode, and satellite altitude angle (Zavorotny and Voronovich, 2000). The specific expression is given as Eqs. (7.1)e(7.4): pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi εcos2 q pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RVV ¼ ε sin q þ εcos2 q pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sin q  εcos2 q pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RHH ¼ sin q þ εcos2 q ε sin q 

(7.1)

(7.2)

RRR ¼ RLL ¼

1 ðRVV þ RHH Þ 2

(7.3)

RLR ¼ RRL ¼

1 ðRVV  RHH Þ 2

(7.4)

where the subscripts V, H, R, and L represent verticle polarization, horizontal polarization, dextral circular polarization, and left circular polarization; the first subscript indicates the polarization mode of the incident wave, while the latter on indicated that of the reflected wave; q is the elevation angle of the satellite; ε is the complex dielectric constant, which is determined by dielectric constant, electromagnetic wavelength and conductivity as given in Eq. (7.5): ε ¼ ε0  ε00 i

(7.5)

Taking GPS L1 band (1575.42 MHz) as an example, assuming the seawater temperature is 25 C and the salinity is 35, the complex dielectric constant ε ¼ 70.53 þ 65.68i. The relationship between normalized Fresnel reflection coefficient and GPS satellite elevation angle is shown as Fig. 7.2.

7.1.3 Mathematical representation of the direct GNSS signal Since the GNSS signal can be regarded as a quasi monochromatic phase modulated spherical wave signal, the strength of the direct signal at the point R of the receiver can be given as Eq. (7.6):     RD (7.6) ED ðR; tÞ ¼ ARF ðRD Þa t  exp jkRD  2pjfl t c

147

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Left-handed polarization component Right-handed polarization component

Normalized Fresnel’s reflection coefficient

148

Vertical polarization component

Horizontal polarization component

Height angle Figure 7.2 The relationship between normalized Fresnel reflection coefficient and GPS satellite elevation angle.

where a(t) is the baseband transmission signal of GNSS; c is the speed of light in vacuum; j2 ¼ 1;k is the wave number k ¼ 2p/ l; f1 is the band frequency of GPS L1 signal; RD is the distance between transmitter T to the receiver R; ARF is the amplitude level of the received direct signal, which is the square root of signal power that can be given as Eq. (7.7): PðRD Þ ¼

Gr l2 Pt Gt ð4pÞ2 Lf R2D

(7.7)

where PtGt is the transmitting power of GNSS satellite; Gr is the gain of receiver; Lf is the attenuation in the atmosphere (Yang and Zhang, 2012). Apply Eq. (7.7) to Eq. (7.6) and the signal strength can be given as Eq. (7.8):       1 RD ! a t (7.8) ED R ; t ¼ A exp jkRD  2pjfl t RD c

Chapter 7 Oil spill detection based on GNSS-R

where A is the amplitude factor that is defined as Eq. (7.9): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pt Gt Gr l2 A¼ (7.9) 2 Lf ð4pÞ

7.1.4 Mathematical representation of the reflected GNSS signal Ocean remote sensing using GNSS-R is based on the joint action of different scattering areas on the sea surface. For small scattering area, the influence of the Earth’s surface can be ignored and the sea surface is assumed to be plane. The geometric diagram of the reflected signal is shown in Fig. 7.3. Assuming that the coordinates of the sea surface scattering point P are (x,y,z), in which z is the random variable for the sea surface height, and the corresponding horizontal position vector is r¼(x,y). The unit vectors from the transmitting satellite to the scattering point and from the scattering point to the receiver are given as Eq. (7.10) and Eq. (7.11), respectively (Yang and Zhang, 2012): ! ! P T   b ¼ (7.10) m ! ! P  T    ! ! RP  b¼ n ! ! R  P   Define the reflection vector q as Eq. (7.12):    b  mÞ b ¼ qx ; qy ; qz ¼ qt ; qz q ¼ kðn

E

Ԑ

]

Figure 7.3 Geometric diagram of reflected signal.

(7.11)

(7.12)

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where qx, qy, qz are the x, y, z components of the reflection vector. q┴¼(qx,qy) is the horizontal components of the reflection vector. According to the Kichhoff approximate geometric optical model, the field strength of the reflected signal ES at the receiver R is given as Eq. (7.13):

     ZZ 1 v ! expðjkRR Þ 2 ! ES R ; t ¼ Dðr; tÞR E P d r 4p vN RR   ! 3 2     ZZ vE P vR 1 ! R 5 R expðjkRR Þd2 r þE P jk  ¼ Dðr; tÞ4 vN RR 4p vN RR

(7.13)

where D(r,t) is the directional graph function of the receiver; E(P) is the incident signal of scattering point P, which can be given as Eq. (7.14):       1 RT ! (7.14) E P;t ¼ A a t  exp jkRT  2pjfl t RT c Apply Eq. (7.14) to Eq. (7.13), ES is then given as Eq. (7.15):          ZZ RT þ RR j vRT j vRR ! kþ þ kþ  ES R ; t ¼ A Dðr; tÞa t  RT vN RR vN c     R exp½jkðRT þ RR Þ exp 2pjfl t d 2 r  4pj RT RR (7.15)

Considering the unit vector in the normal direction N: ! vRT RT ¼  VRT , N ¼  , N ¼  m,N (7.16) vN RT ! vRR RR ¼ VRR , N ¼ , N ¼ n,N vN RR then:

    j j kþ ðm , N Þ þ k þ ðn , N Þ z q,N RT RR

(7.17)

(7.18)

For rough sea surface: q,N z

q2 qz

(7.19)

Chapter 7 Oil spill detection based on GNSS-R

ES on rough sea surface is then given as Eq. (7.15):       ZZ   RT þ RR ! ! Dðr; tÞa t  ES R ; t ¼ A , exp 2pjfl t , g R ; t d2 r c (7.20)   R q2 ! g R;t ¼  exp½jkðRT þ RR Þ (7.21) 4pjRT RR qz where RT and RR are the distance from the scattering point on sea surface to transmitter T and receiver R.

7.1.5 Bistatic forward scattering model based on KA-GO GNSS-R signal is mainly composed of specular reflection components. At the specular reflection point, the incident angle of the signal is equal to the reflection angle. The scintillation area is centered on the specular reflection point (Fig. 7.4). The concentric ellipse in the scintillation area is a series of delay isolines, and the curve is Doppler frequency shift isoline. The points in the annular area between two adjacent delay isolines are

Satellite signal

Receiver

Delay contour Glistening zone

Specular reflection point Doppler contour

Figure 7.4 Schematic of the scattering area.

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considered to have the same code delay (the delay of the same chip as the specular reflection point), and the points in the Doppler area between two Doppler shift curves are considered to have the same Doppler shift. The scintillation area is divided into cross panels by equal delay and equal Doppler frequency shift isolines. The equal delay loop and equal Doppler line divide the sea surface scattering area from the perspectives of time and frequency respectively. For the completely calm sea surface, the scattered signal mainly comes from the specular reflection point. According to the Rayleigh criterion and the Fraunhofer criterion, the sea surface is generally considered to be moderately rough. The greater the roughness of the sea surface, the greater the scattering area. The area with the greatest contribution is the scintillation area. The scattering signal far from the scintillation area is too weak to have impact on the received signal power and inversion results and thus can be ignored. Based on the above physical scattering mechanism, Zavorotny and Voronovich (2000) proposed a bistatic GNSS-R sea surface scattering power distribution model based on Kirchoff approximation theory and geometric optical limit (KAGO), which was combined with Elfouhaily wave spectrum model to estimate the expected scattering process. The relevant power model of GNSS-R received signal is given as Eq. (7.22):   2 Z Z D2 ! r s0 ! r L2 ðDsÞjSðDf Þj 2 2   d2 r < jY ðDs; Df Þj > ¼ Ti 2 ! 2 ! A 4pRT r RR r (7.22) where r is the position vector of a point on sea surface; D2(r) is the antenna gain of the receiver. Ti is the coherent integration time; A is the scintillation area; Ds is the C/A code delay between the scattering point and the specular reflection point on the sea surface; s is the scattering coefficient of scattering point; Dfd is the Doppler shift between the scattering point and the specular reflection point on the sea surface; L(Ds) is the autocorrelation function of C/A code, which is defined as Eq. (7.23): ( 1  jDsj=sc ; jDsj < sc (7.23) LðDsÞ ¼ 0; jDsj  sc where sc ¼ 1 ms/1023, which is the length of a single C/A code it determined the equi-distance line and equi-Doppler line.

Chapter 7 Oil spill detection based on GNSS-R

It can be seen from Eq. (7.22) that on the sea surface of observations, the variables that contribute to the integration model are the overlaps between antenna coverage area and the glistening zone, equi-distance, equi-Doppler. The sea surface scattering signal correlation power model discussed above is a function of code delay and Doppler frequency shift, which reflects the distribution of reflected signals in different equi-distance in a specific equi-Doppler area on the scattered sea surface and can be used to retrieve sea surface information. However, the model is only valid to observe the scattering of signals whose spatial scale is several wavelengths larger than the incident wave. When scattering occurs in some special areas, such as low sweep angle, folded surface, or broken ice surface, it is necessary to include analytical models for predicting frequency and polarization characteristics of reflected signals and more complex scattering models for multiple scattering and diffraction effects (Scott and Demoz, 2009).

7.2 Oil spill information extraction based on GNSS-R The basic principle of extracting oil spill information by GNSS-R technology is similar as that of marine radar. From the point of view of physical chemistry and hydrodynamics, oil film will reduce the surface tension of sea water, reduce the ocean surface roughness, and damp the Bragg scattering of sea capillary wave and short gravity wave on incident electromagnetic wave. When GNSS-R is used to retrieve the sea surface oil spill information, the existence of an oil spill can be transformed into a change of surface mean square slope (MSS) by microwave imaging method. For a given wind speed, the oil spill area will have a lower MSS than the uncontaminated clean sea surface, which will increase the forward scattering of GNSS and reduce the backscattering coefficient. This fact can be indicated in the scattering coefficient distribution map of the observation area, which is in sharp contrast to the uncontaminated clean sea area. This difference can be distinguished from the waveform or the delay-Doppler maps (DDM). The damping effect of sea oil film on sea capillary wave and short gravity waves makes it appear as a low scattering area on Doppler map or DDM (Marchan-Hernandez et al., 2009; Li and Huang, 2013). The key to oil spill information inversion is the DDM, which can obtain the scattering energy of different points and the scattering coefficient distribution in the observation sea area. Some detection models are further discussed in the following sections.

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7.2.1 Normalized bistatic radar cross section of sea surface As shown in Fig. 7.5, normalized bistatic radar cross section of sea surface can be used to describe the roughness of oil film (You et al., 2004) based on Kirchhoff geometric optics as Eq. (7.24)  ! ! ! 4 ! q qt 2 0 s ¼ pjRj P  (7.24) qz qz where m and n are the unit vectors of the incident and scattered wave; P is the probability density function (PDF) of the sea slope; q is the scattering vector defined as Eq. (7.25)   ! b  mÞ b h! z (7.25) q t þ qz b  q  h kðn Due to the small variation of seawater salinity, the intensity of bistatic scattering signal is mainly affected by sea surface roughness. For gradual and linear surface gravitational waves, the PDF of sea surface slope approximately follows the anisotropic binary Gaussian distribution as given in Eq. (7.26):   13 0 2 qt;u qt;c !   ! 7 6 1B q 1 qz qz C C7 (7.26) P  t ¼ exp6  B þ @ A 5 4 2 2 2psu sc 2 qz su sc

Satellite

Receiver

Specular reflection point

Delay contour

Doppler contour

Figure 7.5 Sea surface scattering vector model.

Chapter 7 Oil spill detection based on GNSS-R

155

where -qu/qz and -qu/qz are the sea slope components in the wind direction and cross-wind direction; s2u and s2c are the MSS in the wind direction and cross-wind direction (Fig. 7.6).

7.2.2 MSS model of oil spill sea surface The sea surface MSS based on wave spectrum is an important parameter of sea surface statistical characteristics. The sea surface MSS of upwind and cross-wind is given as Eq. (7.27) and Eq. (7.28): Z NZ p 2 ðk cos jÞ Sðk; jÞdjdk (7.27) s2u ¼ p

0

Z s2c ¼

N

Z

p

p

0

2

ðk sin jÞ Sðk; jÞdjdk

(7.28)

where S(k,j) is the two-dimensional wave energy spectrum (e.g., Elfouhaily wave spectrum, etc.); k is the wave number; j is the wave direction. The existence of oil spill results in the change of surface MSS by microwave DDM. For a given sea surface wind speed, the oil spill area will have a lower mean square wave steepness than the uncontaminated clean sea surface. Therefore, there is a different linear fitting relationship between the MSS of the clean sea surface and that of the oil-polluted sea surface. The MSS of clean sea surface and that of oil-polluted sea surface can be calculated by the linear empirical formula proposed by Cox and Munk (1954) as Eqs. (7.29)e(7.32):

GNSS satellite The direct signal The receiver

θ The reflection signal

HR

θ

S

θ

MSS

Figure 7.6 Mean square slope of sea surface.

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s2c;c ¼ 0:003 þ 1:92  103 U10

(7.29)

s2u;c ¼ 3:16  103 U10

(7.30)

s2c;s ¼ 0:003 þ 0:84  103 U10

(7.31)

s2u;s ¼ 0:005 þ 0:78  103 U10

(7.32)

where the subscript c and s indicate the clean and oil-polluted sea surface; U10 is the actual wind speed at 10 m above the sea surface. Nevertheless, the model is mainly used for optical signal extraction. In order to apply the model to the L-band signal of GNSS-R remote sensing, the empirical formula can be improved (Katzberg et al., 2006) as Eqs. (7.33)e(7.36):   s2c;c ¼ 0:45  0:003 þ 1:92  103 fðU10 Þ (7.33) s2u;c ¼ 0:45  3:16  103 fðU10 Þ



s2c;s ¼ 0:45  0:003 þ 0:84  103 fðU10 Þ

(7.34) 

s2u;s ¼ 0:45  0:005 þ 0:78  103 fðU10 Þ



(7.35) (7.36)

where f is a segmented function of windspeed defined as Eqs. (7.37)e(7.39): fðU10 Þ ¼ U10 0:00 < U10  3:49

(7.37)

fðU10 Þ ¼ 6  lnðU10 Þ3:49< U10  46:00

(7.38)

fðU10 Þ ¼ 0:411  U10 46:00 < U10

(7.39)

As indicated from Eqs. (7.33)e(7.36), the MSS value of the sea surface to be measured directly depends on the sea surface wind speed and the existence of the oil spill. As a result, the sea surface oil spill can affect the distribution of thesea surface scattering coefficient and change the corresponding DDM. Therefore, under a certain windspeed, the sea surface scattering coefficient can be retrieved by the change of DDM, and the oil spill information on sea surface can be extracted according to the model.

7.2.3 DDM of oil spill on sea surface In order to test the ability of using GNSS-R DDM to retrieve sea surface oil spill, the X-band marine radar oil spill image is

Chapter 7 Oil spill detection based on GNSS-R

used to simulate the DDM of clean and oil-polluted sea surface. The generated scattering coefficient distribution images of oilfree and oil-polluted sea surfaces are shown in Fig. 7.7, and the points (x ¼ 0, y ¼ 0) are the specular reflection points. The generated DDM, which shows the power waveform under different Doppler frequency shifts (relative to the specular reflection point), is shown in Fig. 7.8. The shape of the power distribution can be used to describe the surface state of the ocean. The time-delay-Doppler correlation power of the scattered signal is concentrated near the specular reflection point (s0 ¼ 0, f0 ¼ 0), and decreases with the increase of time-delay; With the change of Doppler frequency shift, the whole DDM presents a typical “horseshoe” curve, indicating the distribution of scattered signal energy in different areas of the sea surface. Compared with Fig. 7.8a, the existence of oil spill caused abnormal area in the DDM in Fig. 7.8b. The power value of the abnormal area is larger than that of the surrounding area, showing an irregular highlight feature. This feature is more intuitively shown as the two peaks in Fig. 7.8c. Assuming that the receiver, satellite, and sea surface environment are consistent, the oil spill area that is likely to cause

Figure 7.7 Simulated scattering coefficient distribution images of: (a) Clean sea surface; (b) oil polluted sea surface.

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Figure 7.8 Generated GNSS-R DDM for: (a) Clean sea surface; (b) oil polluted sea surface; (c) clean sea surface in 3-D vision; (d) oil polluted sea surface in 3-D vision.

the increase of the sea surface scattering coefficient can be extracted and identified. Through the transformation from delayed Doppler domain to spatial domain, the oil spill area can be determined in the Cartesian coordinate system based on the specular reflection point established.

References Cox, C., Munk, W., 1954. Measurement of the roughness of the sea surface from photographs of the sun’s glitter. J. Opt. Soc. Am. A. 44 (11), 838e850. Katzberg, S.J., Torres, O., Ganoe, G., 2006. Calibration of reflected GPS for tropical storm wind speed retrievals. Geophys. Res. Lett. 331 (18), 122e140. Li, C., Huang, W., 2013. Sea surface oil slick detection from GNSS-R DelayDoppler Maps using the spatial integration approach. In: Paper Presented in 2013 IEEE Radar Conference. Li, Y., Zhu, X., Cao, Y., liu, B., Yang, Y., 2015. Review of GNSS-R ocean remote sensing monitoring technique. Mar. Sci. Bull. 33 (5), 121e129.

Chapter 7 Oil spill detection based on GNSS-R

Marchan-Hernandez, J.F., Camps, A., Rodriguez-Alvarez, N., Valencia, E., BoschLluis, X., Ramos-Perez, I., 2009. An efficient algorithm to the simulation of delayeDoppler maps of reflected global navigation satellite system signals. IEEE Trans. Geosci. Rem. Sens. 47 (8), 2733e2740. Scott, G., Demoz, G., 2009. GNSS Applications and Methods. Artech House, London. You, H., Garrison, J.L., Heckler, G., et al., 2004. Stochastic voltage model and experimental measurement of ocean-scattered GPS signal statistics. IEEE Trans. Geosci. Rem. Sens. 42 (10), 2160e2169. Yang, D., Zhang, J., 2012. GNSS Reflection Signal Processing: Fundamentals and Applications. Publishing House of Electronics Industry. Zavorotny, V.U., Voronovich, A.G., 2000. Scattering of GPS signals from the ocean with wind remote sensing application. IEEE Trans. Geosci. Rem. Sens. 38 (2), 951e964.

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8 Oil spill tracing technology 8.1 Ecological effect of oil spill As one of the main energy sources in the world, the demand for oil is increasing with the development of economy. In recent years, with the unprecedented development of offshore oil exploitation and offshore shipping industry, offshore oil spills occur frequently, causing serious pollution and harm to the marine ecological environment. As an important primary producer of the ocean, marine microalgae play a vital role in maintaining the stability of marine ecosystem, and oil spill could have longterm damage to marine microalgae (Gemmell et al., 2018). After an oil spill accident, an oil film will be formed on the surface of sea water, which hinders the normal photosynthesis of marine microalgae (Li et al., 2019), inhibits the metabolism of substances (fatty acids, amino acids, etc.) in the algae, and leads to the abnormal growth and reproduction of microalgae (Bretherton et al., 2019; Ben et al., 2018). In addition, the toxic components (polycyclic aromatic hydrocarbons, olefins, etc.) contained in oil will dissolve in sea water to form oil spill dispersion. The oil spill dispersion has strong lipophilicity and is easy to accumulate in marine microalgae with high-fat content and has toxic effects on them (Santander-Avancena et al., 2016). Fatty acids are the main components of cytoskeleton and cell membrane. When petroleum hydrocarbons enter algal cells, they will destroy the cell structure and membrane system, thus affecting the synthesis of fatty acids (Shishlyannikov et al., 2017). Mutations in proteins and antioxidants, and even interfere with the biosynthesis of hydrocarbons could be induced in cells. Therefore, the study of the impact of oil spill pollution on marine microalgae is of great significance to maintain the stability of the marine ecosystem. With the development and application of sequencing technologies such as genomics, transcriptomics, proteomics, and metabolomics, we have a certain understanding of the mechanism of fatty acid biosynthesis in microalgae. At present, the research on fatty acid synthesis of microalgae by means of molecular biology mainly focuses on improving the yield of algal fatty acids. Oil Spill Detection, Identification, and Tracing. https://doi.org/10.1016/B978-0-443-13778-5.00007-2 Copyright © 2024 Elsevier Inc. All rights reserved.

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Gene manipulation of microalgae with existing gene bank is mainly the manipulation of related enzyme genes, so as to change the content and composition of fatty acids in microalgae cells. Chen et al. (2019) cloned and highly expressed acetyl CoA carboxylase to increase the content of unsaturated fatty acids in Rheinland algae. Wang et al. (2018) optimized the lysophosphatidyltransferase gene and glycerol-3-phosphate dehydrogenase gene according to the codon preference of Chlamydomonas reinhardtii and inserted them into the genomic DNA of model microalgae respectively to improve the lipid content. Fukuda et al. (2018) promoted the production of triglycerides by overexpressing glycerol-3-phosphate acyltransferase in unicellular red algae. Wang et al. (2017) used artificial miRNA to inhibit phosphoenolpyruvate carboxylase, so as to improve the fatty acid production of Chlamydomonas reinhardtii. In addition, Jaeger et al. (2017) proposed a model of triacylglycerol and lipid accumulation in oily green algae based on molecular biological means such as transcriptome sequencing. Muhlroth et al. (2013) proposed the n-3 long-chain polyunsaturated fatty acid synthesis pathway of Phaeodactylum tricornutum based on the latest transcriptome data and combined with molecular biology and metabolism research in recent years. However, when using molecular biological methods to study microalgae fatty acid synthesis, the flux distribution between fatty acid synthesis precursors and different products can not be further clarified. In the fatty acid synthesis pathway, some enzymes act on two pathways respectively, such as D 5D and D 6D fatty acid desaturase (Pollak et al., 2012; Qiu, 2003). Due to the continuous production and consumption of fatty acid synthesis and metabolism, it is impossible to determine which pathway accounts for more carbon flux and which pathway accounts for less by means of molecular biology. This flux distribution is very important to accurately understand the change law of the fatty acid synthesis pathway in the process of microalgae metabolism under pollution stress. Stable isotope fractionation will occur in the process of biochemical reaction, that is, the stable isotope composition of the product will change relative to the stable isotope composition of the reactant (Sim et al., 2019; Wilkes and Pearson, 2019). This change in stable isotopic composition is related not only to the size of the isotopic effect of a specific biochemical process (Thomas et al., 2019), but also to the number of chemical reactions existing in the biochemical process (Close, 2019; Pearson et al., 2019). For example, if there are multiple products in a biochemical reaction, the stable isotopic composition of the reactant is affected by the isotopic effect of multiple biochemical

Chapter 8 Oil spill tracing technology

reactions. According to the law of conservation of matter, in the process of rapid metabolic reaction (i.e., steady state), the carbon stable isotopic composition of a metabolite is the same as that of a metabolite. It can be deduced that there is a linear correlation between the carbon-stable isotopic composition of the reactant and the flux distribution of the metabolic pathway (Hayes, 2011). When organisms are stressed by external environmental pollution, they will produce a series of defense measures to maintain their own cell survival. Its essence is that organisms adapt to environmental changes by regulating the changes in biological metabolic pathways (Batista-Silva et al., 2019; Lim et al., 2017). The carbon stable isotope composition of a single metabolite is analyzed by gas chromatography-tandem stable isotope ratio mass spectrometer, so as to calculate the flux distribution relationship of its metabolic pathway (Busch et al., 2018; Long and Antoniewicz, 2019). Inspired by this, we propose to use the combination of monomer-stable isotopes and key enzymes and genes to explore the flux distribution mechanism of different fatty acid synthesis pathways of marine microalgae under oil spill stress, which has not been reported.

8.1.1 Effects of pollution stress on metabolism of microalgae Microalgae, as the main primary producer in the marine ecosystem, affect the stability of the whole marine ecosystem. When pollutants enter the marine environment, microalgae are sensitive to their pollution stress and produce a series of toxic effects (Xiong et al., 2018; Dupraz et al., 2019). Ramadass et al. (2017) studied the changes in chlorophyll a content and oxidative stressrelated enzyme activity of three microalgae induced by diesel oil and used antioxidant enzymes as biomarkers to evaluate the intensity of oxidative stress response in microalgae for the first time. At present, most studies mainly focus on the determination of chlorophyll and intracellular antioxidant system of microalgae by toxic substances, which are often applied to the change of antioxidant enzyme activity, so as to judge the degree of algae under the stress of toxic substances. However, when pollutants stress microalgae, the synthesis of main metabolic substances (fatty acids and amino acids) in algal cells will also be affected and changed. The stress degree of microalgae can only be judged by antioxidant enzymes, but the change of metabolic substance content in cells is unclear. Microalgae, as a primary production in the ocean, contains a variety of beneficial unsaturated fatty acids,

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High trophic animals obtain essential fatty acids by feeding microalgae. When microalgae are under pollution stress, the content of fatty acids will be reduced, thus changing the structure of the food chain. Qian et al. (2018) studied that the hormone acetaminophen can change the metabolism of Chlorella in vivo, especially the metabolism of amino acids, carbohydrates, and lipids. Shishlyannikov et al. (2017) proved that petroleum hydrocarbons will accumulate in diatom cells and accumulate in lipids, affecting cell fat metabolism and lipid synthesis.

8.1.2 Effect of marine oil spill on fatty acid synthesis of microalgae Algae have important contributions to marine ecosystems and environment. They are the starting point of the marine biological chain. They play a vital role in material circulation and energy flow in marine ecosystem. Algae use carbon sources to synthesize metabolites inside cells through photosynthesis, and fatty acids are the main source of biological energy, which are composed of a large number of carbon elements and play an important role in the function and integrity of cell membrane (Zulu et al., 2018). When microalgae are exposed to hydrocarbon compounds, there are few studies on the metabolic changes of algal fatty acids. Wee et al. (2016) studied the effect of oil-soluble components (WAF) on Skeletonema costatum and found that the growth rate of Skeletonema costatum decreased significantly under oil pollution stress. The total cellular lipid content of algae exposed to uncontaminated and oil pollution culture is different. The lipid content of the control group is twice that of the oil spill stress group, The content of saturated and unsaturated fatty acids in the control group was higher than that in the oil pollution group. Olivares-Rubio et al. (2017) found that the content of fatty acids in diatom halamphora Oceanica decreased significantly under crude oil stress, and polycyclic aromatic hydrocarbons accumulated in fat, reducing the activities of fatty acid synthase and acetyl CoA oxidase (AOX); AOX plays an important role in the fatty acid synthesis and is responsible for the activation of long-chain fatty acids. If the activity of AOX is affected, it can inhibit the production of acetyl CoA and reduce the synthesis of fatty acids. At present, the mechanism of desaturation of algae is still in the preliminary stage, especially the effect of enzymes on the synthesis of fatty acids needs to be further studied.

Chapter 8 Oil spill tracing technology

8.1.3 Application of stable isotope analysis in marine ecology Stable isotopes can be used to trace and determine the sources of matter and energy and their transfer between species. This method has been widely used in the field of marine ecology in recent years. The analysis of stable carbon isotopes of algae can not only effectively indicate the utilization strategy of algae for carbon sources, but also determine the nutritional structure relationship and temporal and spatial variation law of marine food chain web. Li et al. (2018) established an experimental method to obtain the stable carbon isotope fractionation value of microalgae in the process of CO2 assimilation by using the two-way labeling method and found that carbonic anhydrase can improve the utilization ability of algae to CO2 in the atmosphere. Elliott et al. (2018) analyzed the stable carbon isotope of monomer amino acids of three kinds of large algae kelp, neopines, Ulva pertusa, and a variety of invertebrate consumers across different nutritional levels from the subtidal zone and intertidal zone in the South Central Sea area of Alaska. Based on the stable carbon isotope value of amino acids of these organisms, they analyzed the material transfer between species of various groups of food webs, The contribution of these algal amino acids to different consumers was quantified by isotope mixing model. Park et al. (2017) found by measuring the stable carbon homeostasis information characteristics of benthic algae and their consumers in two different Gulf regions of South Korea that the restriction of tides caused by embankment reduced the utilization rate of benthic algae by consumers, which would lead to the reorganization of the nutritional structure of local benthic community, indicating that stable isotopes can effectively indicate the changes of marine environmental information caused by human factors. It can be seen that the stable isotope analysis of algae can provide great help to understand the law of material circulation and energy flow in marine ecosystem, and it is also an important means to study marine ecological changes. However, at present, the application of this analysis method mainly focuses on the study of the nutritional relationship between marine environmental factors and organisms and the temporal and spatial changes of food web structure. It only reveals the transmission law of matter and energy in the food chain web, but the research on the law of anabolism in the process of material transmission has not been reported.

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8.2 Identification index of oil spill tracing Under a variety of geological conditions, organic matter has undergone long-term chemical changes to form thousands of organic compounds. Oil is composed of these complex organic compounds. The main components of petroleum are hydrocarbon compounds composed of carbon and hydrogen, including saturated hydrocarbons, aromatic hydrocarbons, colloids, and asphaltenes. Its main elements are carbon and hydrogen, as well as a small amount of sulfur, oxygen, and nitrogen and metal elements such as nickel, vanadium, and iron. The infinite variability of the composition of these elements leads to different chemical differences between different oils. Refined petroleum products are usually the fractions of crude oil at a certain stage. Due to the different characteristics of crude oil and the differences in raw materials and refining process, the chemical composition of finished oil is also different. Therefore, all crude oil and petroleum products have different chemical compositions from each other to a great extent. The difference in chemical composition leads to the unique chemical “fingerprint” of each oil, which provides an important basis for identifying the source of spilled oil (Wang and Fingas, 2003). At present, the most commonly used chemical fingerprint identification indicators of oil spill include saturated hydrocarbon index (including isoprene), aromatic compound index, and biomarker index. Generally, the content distribution or diagnostic ratio of a series of indicators is used to identify the source of oil spill.

8.2.1 Saturated hydrocarbon index The saturated hydrocarbon components in petroleum mainly include normal alkanes (straight-chain alkanes), isoalkanes (branched-chain alkanes), and cycloalkanes. The content of this component is more than half of the total content in petroleum and is the most important petroleum component. For oil spill identification, the original spectra or relevant diagnostic ratios of n-alkanes and isoprenoids in oil can be compared by gas chromatography (GC-FID) and gas chromatography-mass spectrometry (GC-MS), so as to trace the source of oil spill (Texeira et al., 2014). The most common identification parameters of saturated hydrocarbons include but are not limited to PR/pH, C17/PR, C18/pH, odd even dominance index (OEP), carbon dominance index (CPI), (C19 þ C20)/(C19 w C22), among which PR is pristane, pH is phytane, and the number after C represents carbon number. The above diagnostic ratio can reflect the source characteristics

Chapter 8 Oil spill tracing technology

and sedimentary environment of oil, and can be used to identify and distinguish oil sources (Hao, 2011). Bayona et al. (2015) explored the diagnostic ratios of various n-alkanes and analyzed the differences between various crude oils. They found that C17/PR, C18/pH, and PR/pH have clear identification effects for different crude oils. Similarly, Texeira et al. (2014) analyzed the petroleum hydrocarbon components of oil spill samples by GC-MS. the results show that PR/pH index is stable in the same oil sample, but there are significant differences between different samples, which can be used as a good oil spill identification index. Retnam et al. (2015) studied the fingerprints of crude oil. Among them, the indexes of normal alkanes and isoprenoid compounds (PR, pH) have an obvious distinguishing effect on different crude oils. Fernandez-Varela et al. (2008) obtained the distribution pattern of n-alkanes in fuel oil by GC-FID method and successfully matched the petroleum hydrocarbon samples collected on the beach with fuel oil by combining mathematical statistics.

8.2.2 Polycyclic aromatic hydrocarbons index Polycyclic aromatic hydrocarbons (PAHs) refer to hydrocarbon compounds containing two or more benzene rings. The content of these compounds in oil is very high, second only to the content of saturated hydrocarbons in oil. Polycyclic aromatic hydrocarbons in petroleum mainly come from precursors formed by microorganisms and terrestrial plants, which are formed through a geological synthesis process. In the field of organic geochemistry, the distribution of polycyclic aromatic hydrocarbons is used to indicate the source, thermal maturity, and sedimentary environment of crude oil and source rocks (Wilson and Jones, 1993). Therefore, oils with different sources and types have different PAH distribution patterns or different diagnostic ratios. In oil products, in addition to the parent of polycyclic aromatic hydrocarbons, more content is alkylated polycyclic aromatic hydrocarbons, which can more truly reflect the composition of polycyclic aromatic hydrocarbons in oil (Wang and Fingas, 1995). In addition, different crude oils can also be successfully distinguished by using the difference between sulfurcontaining polycyclic aromatic hydrocarbons (Hegazi et al., 2004). Polycyclic aromatic hydrocarbons carry the original information in the process of oil formation (Sinaei et al., 2012), and have stronger weathering resistance than saturated hydrocarbons (Boehm et al., 1997; Zhou et al., 2015; Ebrahimi et al., 2007; Fernandez-Varela et al., 2009). They are important indicators in the identification of weathered oil samples. Researchers have

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done a lot of research on the fingerprint of spilled oil and found some polycyclic aromatic hydrocarbon diagnostic ratios with good stability and strong discrimination, which are suitable for oil sample identification. These aromatic hydrocarbons mainly include naphthalene, phenanthrene, fluorene, dibenzothiophene, and their corresponding alkylation products. The composition, distribution, and content of aromatics in different oil samples are different. Many studies use GC-FID and GC-MS to obtain the composition data of aromatic compounds in oil and then analyze the information and source of oil samples (Yim et al., 2011). Previous studies have shown that it is effective to distinguish oil samples by using the distribution pattern of aromatic compounds and some specific aromatic diagnostic ratios (Zhang et al., 2016; Yunker et al., 2002). The characteristic ratio of Dibenzothiophene and phenanthrene series can be used to distinguish two fuel oils, while naphthalene series and phenanthrene series can be used to judge the weathering degree of oil samples (Douglas et al., 1996).

8.2.3 Biomarker index Biomarkers are one of the most important hydrocarbon compounds in petroleum for chemical fingerprint identification. They are complex molecules from previous organisms. Biomarkers found in crude oil, rocks, and sediments have little change in the structure of their biological precursors (such as hopane, sterols, and steroids). Compared with the concentration of biological precursors, the concentration of biomarkers in oil is low, usually in the range of hundreds to one millionth (PPM). Biomarkers are useful for petrochemical fingerprints because they retain the original carbon skeleton of all or most natural products, and this structural similarity can reveal more information about oil sources than other compounds in oil (Wang et al., 2007). Specific applications of biomarker fingerprints in the field of petroleum geochemistry: correlation between oil and oil source rock; The types of precursor organisms present in the source rock (such as bacteria, algae, algae, microorganisms and higher plants, as each type of organism may contain different biomarkers); Effective evaluation of the relative thermal maturity (i.e., immature, mature, mature) and oil thermal history of oil in the whole oil production window (i.e., early, peak or late); Based on the loss of n-alkanes, isoprenoids, aromatics, terpanes and steranes in the process of biodegradation, the migration of reservoir and the degree of biodegradation are evaluated; Determine sedimentary environmental conditions (e.g., marine, terrestrial, delta or

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high salinity environments and anoxic or hypoxic sedimentary environments, because different sedimentary environments can also lead to characteristic or even subtle differences in biomarker composition); Provide information on the age of oil source rocks. Compared with other hydrocarbons such as n-alkanes and most aromatic compounds, biomarkers have high antidegradation ability in the environment. In addition, due to the diversity of geological conditions and oil formation time, each crude oil can basically show a unique biomarker fingerprint. Therefore, the chemical analysis of biomarkers has very important reference value in determining the source of oil spill, distinguishing and correlating oil, and monitoring the degradation process (weathering state) of oil under various conditions. They can also be applied to the identification of petroleum pollutants in marine and aquatic environment (Boehm et al., 1997).

8.3 Stable isotope fingerprint of spilled oil 8.3.1 Effect of weathering stress on oil fingerprint After the spilled oil enters the marine environment, it will float on the water in the form of oil film, and only a small part will dissolve. Then, driven by the power of the marine environment such as wind, wave, and current, it will continue to diffuse and migrate, accompanied by weathering processes such as evaporation, dissolution, adsorption and sedimentation, emulsification, photooxidation, and biodegradation. These physical, chemical, and biological effects lead to significant changes in the concentration and chemical composition distribution of the spilled oil (De Oteyza and Grimalt, 2006; D’Auria et al., 2009). For example, the chromatogram of moderately biodegradable oil will show an indistinguishable complex mixture (UCM) bulge, the complete disappearance of short-chain alkanes (n < C15), and the moderate change in the distribution of alkyl naphthalene (Asif et al., 2009). Prince et al. (2002) found that 20 years after an Arctic oil spill, the loss of hydrocarbons caused by biodegradation was as high as 87%. In this process, tetracycline series compounds suffered a serious loss due to photooxidation, and the loss could reach 50%. The degradation degree and weathering rate of spilled oil are mainly controlled by the type of spilled oil and environmental conditions. Light, temperature, salinity, and biological activities will change the physical properties and chemical composition of spilled oil to varying degrees. Generally speaking, the impact of weathering on the chemical composition of oil spill is mainly

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divided into three types: mild weathering, moderate weathering, and severe weathering (Wang et al., 2013a). The loss of slightly weathered oil spills is mainly caused by the loss of low molecular weight normal alkanes, but the ratios of nc17/PR and nc18/pH basically remain unchanged. For slightly or moderately weathered oil spills, normal alkanes and isoprene alkanes such as PR and pH can still be used to identify the oil spill source. For severely weathered spilled oil, complete loss of n-alkanes and even isoprenoids may occur in some cases. In this case, conventional chromatographic analysis technology has little significance for oil spill identification. Volatile polycyclic aromatic compounds such as benzene and its series, naphthalene, and naphthalene series will lose rapidly during weathering. When the oil spill is weathered to a certain extent (the loss is about 20%e25%), benzene series and C3 benzene will be completely lost. Among the PAHs, naphthalene series compounds are most vulnerable to weathering compared with other series alkyl compounds, Qu series compounds are relatively stable, and the weathering loss rates of naphthalene, phenanthrene, dibenzothiophene, fluorene, and Qu series slow down in turn. The relative abundance of biomarkers increases gradually during weathering due to their stability and strong antibiodegradation ability. The diagnostic ratios of steroids and terpenoids, including TS/TM, C23/C24, C29/C30, c3122s/(22s þ 22r), c3222s/ (22s þ 22r), c3322s/(22s þ 22r), basically do not change during long-term weathering under natural conditions. Sufficient research has been carried out on the impact of weathering on petrochemical composition. Ni (2008) conducted weathering simulation experiments on heavy fuel oil. The results show that nc18/pH, nc17/PR, nc17/nc18, and PR/pH cannot be used as reliable indicators after the oil has experienced moderate weathering, but can be used to identify slightly weathered heavy fuel oil; It can be used as the effective parameter for the complete weathering of heavy phenanthrene (NCD) and terpene (c315). Wang (2005) and others studied the changes in oil fingerprints of crude oil samples under natural weathering, ultraviolet light, and biodegradation. Under the condition of short-term natural weathering, light components such as alkanes, naphthalene, fluorene, and phenanthrene series with low carbon numbers lost to varying degrees; Under the condition of ultraviolet light, the yield in aromatics will be degraded preferentially; During biodegradation, alkanes and low ring number aromatics in petroleum components will be preferentially decomposed by bacteria, and the diagnostic ratio parameters of alkanes and aromatics will change significantly. Cheng (2009) studied the moderate weathering characteristics of oil spills in Xiamen through GC-MS analysis

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technology. The simulation experiment found that the naphthalene series in the sand surface in Xiamen completely disappeared after 1 month, alkanes, Wat, dibenzothiophene, and phenanthrene series compounds were seriously affected, the abundance of characteristic ion spectra of terpanes and steranes also decreased, and the weathering rate of various petroleum hydrocarbons in the sand surface weathering simulation experiment was significantly higher than that in the water surface weathering experiment. Lv (2004) Studied the marine moderately and severely weathered oil spills. 15 kinds of oils such as Liaohe light crude oil were weathered under different experimental conditions such as seawater, fresh water, and sandy soil for 1 year. It was found that the ratio parameters of steroid and terpene biomarkers were less affected by weathering. Liu et al. (2017) showed that after 210 days of moderate and severe weathering, only oep2 characteristic ratio in the diagnostic indicators of normal alkanes maintained good stability, and the relative standard deviation was less than or equal to 5%. PR/pH, nc17/PR, and nc18/pH characteristic ratio parameters changed significantly, which was of little significance for oil identification; In the diagnostic indexes of polycyclic aromatic hydrocarbons, except that the relative standard deviation of MNR and MP/P characteristic ratio is greater than 5%, the relative standard deviation of mpi-1, mpi-2, MPDF, and MNR is less than 5%. Yang (2013) Studied crude oil and fuel oil weathered for up to 1 year. The results show that the ratios of nc17/PR, PR/ pH, and nc18/pH of severely weathered crude oil are less affected by weathering in the long-term weathering process, which can be used as characteristic parameters for identifying long-term weathered crude oil; In the unweathered and weathered marine fuel oil, these three ratios change obviously, so they should be carefully selected in the identification of marine fuel oil. In conclusion, the degradation degree and weathering rate of spilled oil are not only affected by environmental conditions but also related to the nature of oil products. The weathering degree of spilled oil should be determined according to the specific situation. However, weathering will have varying degrees of impact on the oil fingerprint and increase the difficulty of oil spill identification. It is limited and uncertain to confirm the oil spill source by comparing the oil fingerprint with the oil samples of the sea surface oil spill and the oil spill source.

8.3.2 Carbon stable isotopes of petroleum Part of the dispersed organic matter in sedimentary rocks is continuously pyrolyzed and evolved under geological conditions.

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The final complex mixture is called oil, and its main component is petroleum hydrocarbons. The carbon-stable isotopic composition of oil is mainly controlled by the sedimentary environment and the type of organic parent material from which oil is generated and is relatively less affected by other factors such as the thermal evolution of organic matter. There is a very significant similarity between the carbon-stable isotopic composition of oil components and the stable isotopic composition of biochemical components of organic matter precursors. This phenomenon is called the parent material inheritance effect of oil (Xu et al., 2001). For example, chloroform extracts from the same source have similar d13C values as saturated hydrocarbons in crude oil. Correspondingly, lignin is very close to the carbon-stable isotopic composition of aromatic hydrocarbons in crude oil. The isotopic composition of petroleum from different organic sources is very different. Therefore, the carbon-stable isotopic composition of oil can be used for the identification of oil and oil source. In addition, carbon stable isotopes can also be used to classify oil and gas types, judge oil and gas maturity, and study secondary changes and migration of oil and gas. It is generally believed that the d13C value of the marine crude oil is less than 30&, while that of the continental crude oil is between 28& to 24&, and that of the lake crude oil is between 29.5& to 28&. Foreign researchers have measured the carbon stable isotopic composition of saturated hydrocarbon and aromatic hydrocarbon components in more than 300 kinds of crude oil and successfully distinguished the oil samples from marine and nonmarine sources through the combination of the two parts of data (Maslen et al., 2011). The carbon stable isotopic composition of petroleum with different properties is also different. The study of nearly 400 different carbon-stable isotopes of petroleum in China shows that the d13C value of condensate (light oil) is significantly higher than that of normal crude oil. As one of the components of polycyclic aromatic hydrocarbons, the stable carbon isotopic composition of alkyl naphthalene can be used as a reference index for oil-oil and oil source correlation. From the above research, we can see that the carbon-stable isotope composition of oil can provide or supplement more abundant and unique oil sample information. Because the stable carbon isotope composition of petroleum can reflect the characteristic information such as the source of organic matter, sedimentary environment, and age and has stability, this index has incomparable advantages. Stable isotope analysis technology is also used in environmental forensic science and oil spill identification. The carbon stable isotopic

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composition of oil samples is mainly divided into two categories: one is the carbon stable isotopic composition data of the whole oil sample, and the other is the carbon stable isotopic composition of a single hydrocarbon of petroleum hydrocarbon. In the initial stage of the study, the researchers mainly studied the carbon-stable isotope composition of the whole oil sample. Calder and Parker (1968) used stable isotope technology to determine that the source of petroleum pollutants in the ship’s channel came from the petrochemical plant in the industrial zone in eastern Texas. Hartman and Hammond (1981) measured the carbon-stable isotopic composition of the asphaltene fraction of beach tar near Los Angeles. The results show that the carbon isotopic composition of the asphaltene fraction is similar to that of the local oil and gas (d13C is 22.5& w 23.2&). The author believes that the isotopic composition can be used to identify the source of beach tar even after weathering for 2e4 weeks. The Exxon Valdez tank oil spill in 1989 caused a large amount of oil to leak in Alaska’s Prince William Bay. Kvenvolden et al. (1993) analyzed the whole oil carbon stable isotope composition and molecular distribution of Exxon valdezoil spill residue, and the carbon stable isotope composition and biomarker distribution determined the source of heavy oil residue. Wang et al. (2015) Analyzed tar balls on the west coast of North America by GC-MS and carbon stable isotope composition. The d13C values of tar balls from Northern California and Oregon have a very narrow isotopic range (26.8&  0.1&), and the source of these tar balls is fuel oil from tank cars. Mansuy et al. (1997) extracted the oil spill residue from the feathers of birds affected by the oil spill accident and successfully found its source by measuring the carbon stable isotopic composition of n-alkanes. Li and Xiong (2009) Studied the weathered mixed oil source by measuring the carbon stable isotope composition of long-chain n-alkane monomer hydrocarbons. The results show that the d13C value of n-alkane monomer is a powerful tool to identify the source of oil spill. Wang et al. (2013b) Analyzed the chemical fingerprint and carbon stable isotopic composition of normal alkanes of oil samples in the oil spill accident in Dalian port. The data show that the d13C value of monomer normal alkanes changes little after weathering of the oil spill, and the composition of monomer normal alkanes of oil samples can be used for traceability. Harvey et al. (2012) Studied the chemical characteristics of several diesel fuels from different sources. On the premise of similar chromatograms of several diesel fuels, they successfully distinguished all oil products through the difference of n-alkane d13C data of diesel.

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Lim, G.H., Singhal, R., Kachroo, A., Kachroo, P., 2017. Fatty acide and lipidmediated signaling in plant defense. Annu. Rev. Phytopathol. 55, 505e536. Liu, Y., Xu, J., Chen, W., Li, Y., 2017. Effects of short-term weathering on the stable carbon isotope compositions of crude oils and fuel oils. Mar. Pollut. Bull. 119 (1), 238e244. Long, C.P., Antoniewicz, M.R., 2019. High-resolution 13C metabolic flux analysis. Nat. Protoc. 14, 2856e2877. Lv, X., 2004. Study on fingerprints distinction serious-weathering spilled oil in the sea. In: Master Thesis. Dalian Maritime University, Chinese. Mansuy, L., Philp, R.P., Allen, J., 1997. Source identification of oil spills based on the isotopic composition of individual components in weathered oil samples. Environ. Sci. Technol. 31 (12), 3417e3425. Maslen, E., Grice, K., Le Metayer, P., Dawson, D., Edwards, D., 2011. Stable carbon isotopic compositions of individual aromatic hydrocarbons as source and age indicators in oils from western Australian basins. Org. Geochem. 42 (4), 387e398. Muhlroth, A., Li, K., Rokke, G., Winge, P., Olsen, Y., Hohmann-Marriott, M.F., Vadstein, O., Bones, A.M., 2013. Pathways of lipid metabolism in marine algae, co-expression network, bottlenecks and candidate genes for enhanced production of EPA and DHA in species of chromista. Mar. Drugs 11, 4662e4697. Ni, Z., 2008. Study on the oil weathering and identification on the sea. In: Master Thesis. Ocean University of China, Chinese. jera-Martínez, M., Godínez-Ortega, J.L., Olivares-Rubio, H.F., Salazar-Coria, L., Na Vega-López, A., 2017. Lipid metabolism and pro-oxidant/antioxidant balance of Halamphora oceanica from the Gulf of Mexico exposed to water accommodated fraction of Maya crude oil. Ecotoxicol. Environ. Saf. 147, 840e851. Park, H.J., Kang, H.Y., Park, T.H., Kang, C.K., 2017. Comparative trophic structures of macrobenthic food web in two macrotidal wetlands with and without a dike on the temperate coast of Korea as revealed by stable isotopes. Mar. Environ. Res. 131, 134e145. Pearson, A., Hurley, S.J., Elling, F.J., Wilkes, E.B., 2019. CO2-dependent carbon isotope fractionation in Archaea, Part I: modeling the 3HP/4HB pathway. Geochem. Cosmochim. Acta 261, 368e382. Pollak, D.W., Bostick, M.W., Yoon, H., Wang, J., Hollerbach, D.H., He, H., Damude, H.G., Zhang, H., Yadav, N.S., Hong, S.P., Sharpe, P., Xue, Z., Zhu, Q., 2012. Isolation of a D5 desaturase gene from Euglena gracilis and functional dissection of its HPGG and HDASH motifs. Lipids 47, 913e926. Prince, R.C., Owens, E.H., Sergy, G.A., 2002. Weathering of an Arctic oil spill over 20 years:the BIOS experiment revisited. Mar. Pollut. Bull. 44 (11), 1236e1242. Qian, L., Qi, S., Cao, F., Zhang, J., Zhao, F., Li, C., Wang, C., 2018. Toxic effects of boscalid on the growth, photosynthesis, antioxidant system and metabolism of Chlorella vulgaris. Environ. Pollut. 242, 171e181. Qiu, X., 2003. Biosynthesis of docosahexaenoic acid (DHA, 22:6-4, 7, 10, 13, 16,19): two distinct pathways. Prostag. Leukotr. Ess. 68, 181e186. Ramadass, K., Megharaj, M., Venkateswarlu, K., Naidu, R., 2017. Toxicity of diesel water accommodated fraction toward microalgae, Pseudokirchneriella subcapitata and Chlorella sp. MM3. Ecotoxicol. Environ. Safe. 142, 538e543. Retnam, A., Kassim, A.M., Ahmad, W.K.W., 2015. Fingerprinting of light fuel oil: A Malaysia case study. Proc. Environ. Sci. 30, 190e194. https://doi.org/10.1016/ j.proenv.2015.10.034.

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Santander-Avancena, S.S., Sadaba, R.B., Taberna, H.S., Tayo, G.T., Koyama, J., 2016. Acute toxicity of water-accommodated fraction and chemically enhanced WAF of bunker C oil and dispersant to a microalga Tetraselmis tetrathele. Bull. Environ. Contam. Toxicol. 96, 31e35. Shishlyannikov, S.M., Nikonova, A.A., Klimenkov, I.V., Gorshkov, A.G., 2017. Accumulation of petroleum hydrocarbons in intracellular lipid bodies of the freshwater diatom Synedra acus subsp. radians. Environ. Sci. Pollut. Res. 24, 275e283. Sim, M.S., Ogata, H., Lubitz, W., Adkins, J.F., Sessions, A.L., Orphan, V.J., McGlynn, S.E., 2019. Role of APS reductase in biogeochemical sulfur isotope fractionation. Nat. Commun. 10, 44e52. Sinaei, M., Eghtesadi araghi, P., Mashinchian, A., Fatemi, M., Riazi, G., 2012. Application of biomarkers in mudskipper (Boleophthalmus dussumieri) to assess polycyclic aromatic hydrocarbons (PAHs) pollution in coastal areas of the Persian Gulf. Ecotoxico. Environ. Safety 84, 311e318. Texeira, C.C., Santos Siqueira, C.Y., Aquino Neto, F.R., Miranda, F.P., Cerqueira, J.R., Vasconcelos, A.O., Landau, L., Herrera, M., Bannermaman, K., 2014. Source identification of sea surface oil with geochemical data in Cantarell, Mexico. Microchem. J. 117, 202e213. Thomas, P.J., Boller, A.J., Satagopan, S., Tabita, F.R., Cavanaugh, C.M., Scott, K.M., 2019. Isotope discrimination by form IC RubisCO from Ralstonia eutropha and Rhodobacter sphaeroides, metabolically versatile members of ‘Proteobacteria’ from aquatic and soil habitats. Environ. Microbiol. 21, 72e80. Wang, C., Chen, X., Li, H., Wang, J.X., Hu, Z., 2017. Artificial miRNA inhibition of phosphoenolpyruvate carboxylase increases fatty acid production in a green microalga Chlamydomonas reinhardtii. Biotechnol. Biofuels 10, 91e101. Wang, C., Gao, X., Sun, Z., Qin, Z., Yin, X., He, S., 2013a. Evaluation of the diagnostic ratios for the identification of spilled oils after biodegradation. Environ. Earth Sci. 68 (4), 917e926. Wang, C., Chen, B., Zhang, B., He, S., Zhao, M., 2013b. Fingerprint and weathering characteristics of crude oils after Dalian oil spill, China. Mar. Pollut. Bull. 71 (1e2), 64e68. Wang, C., Li, Y., Lu, J., Deng, X., Li, H., Hu, Z., 2018. Effect of overexpression of LPAAT and FPD1 on lipid synthesis and composition in green microalga Chlamydomonas reinhardtii. J. Appl. Phycol. 30, 1711e1719. Wang, L., 2005. Study on identification methods of marine oil pollutants and their fate and source. In: Master Thesis. Xiamen University, Chinesee. Wang, Z., Fingas, M., 1995. Differentiation of the source of spilled oil and monitoring of the oil weathering process using gas chromatography-mass spectrometry. J. Chromatogr. A 712 (2), 321e343. Wang, Z., Fingas, M., 2003. Development of oil hydrocarbon fingerprinting and identification techniques. Mar. Pollut. Bull. 47 (9e12), 423e452. Wang, Z., Fingas, M., Landriault, M., Sigouin, L., Castle, B., Hostetter, D., Zhang, D., Spencer, B., 2015. Identification and linkage of tarballs from the coasts of Vancouver island and northern California using GC/MS and isotopic techniques. J. High Resolut. Chromatogr. 21 (7), 383e395. Wang, Z., Yang, C., Fingas, M., Hollebone, B., Yim, U.H., Oh, J.R., 2007. Petroleum biomarker fingerprinting for oil spill characterization and source identification. In: Wang, Z., Stout, S.A. (Eds.), Oil Spill Environmental Forensics: Fingerprinting and Source Identification. Academic Press, pp. 73e146.

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Wee, J.L., Millie, D.F., Nguyen, N.K., Patterson, J., Cattolico, R.A., John, D.E., Paul, J.H., 2016. Growth and biochemical responses of Skeletonema costatum to petroleum contamination. J. Appl. Phycol. 28, 3317e3329. Wilkes, E.B., Pearson, A., 2019. A general model for carbon isotopes in redlineage phytoplankton: interplay between unidirectional processes and fractionation by RubisCO. Geochem. Cosmochim. Acta 265, 163e181. Wilson, S.C., Jones, K.C., 1993. Bioremediation of soil contaminated with polynuclear aromatic hydrocarbons (PAHs): a review. Environ. Pollut. 81 (3), 229e249. Xiong, J., Govindwar, S., Kurade, M.B., Paeng, K.J., Roh, H.S., Khan, M.A., Jeon, B.H., 2018. Toxicity of sulfamethazine and sulfamethoxazole and their removal by a green microalga, Scenedesmus obliquus. Chemosphere 218, 551e558. Xu, Y., Shen, P., Liu, W., Guan, P., Huang, D., 2001. Isotopic composition characteristics and identification of immature and low-mature oils. Chin. Sci. Bull. 22, 1923e1929. Yang, S., 2013. The research of longterm weathering characteristics of crude oil and fuel oil at sea. In: Master Thesis. Dalian Maritime University, Chinese. Yim, U.H., Ha, S.Y., An, J.G., Won, J.H., Han, G.M., Hong, S.H., Kim, M., Jung, J.H., Shim, W.J., 2011. Fingerprint and weathering characteristics of stranded oils after the Hebei Spirit oil spill. J. Hazard Mater. 197, 60e69. Yunker, M.B., Macdonald, R.W., Vingarzan, R., Mitcheal, R.H., Goyette, D., Sylvestre, S., 2002. PAHs in the Fraser River basin: a critical appraisal of PAH ratios as indicators of PAH source and composition. Org. Geochem. 33 (4), 489e515. Zhang, H., Wang, C., Zhao, R., Yin, X., Zhou, H., Tan, L., Wang, J., 2016. New diagnostic ratios based on phenanthrenes and anthracenes for effective distinguishing heavy fuel oils from crude oils. Mar. Pollut. Bull. 106 (1e2), 58e61. Zhou, P., Chen, C., Ye, J., Shen, W., Xiong, X., Hu, P., Fang, H., Huang, C., Sun, Y., 2015. Combining molecular fingerprints with multidimensional scaling analyses to identify the source of spilled oil from highly similar suspected oils. Mar. Pollut. Bull. 93 (1e2), 121e129. Zulu, N.N., Zienkiewicz, K., Vollheyde, K., Feussner, I., 2018. Current trends to comprehend lipid metabolism in diatoms. Prog. Lipid Res. 70, 1e16.

9 Case study: routine surveillance of the oil spills in coastal environment 9.1 UV-induced fluorescence device for oil spills detection Oil production platforms and oil storage tanks in coastal areas are major sources of oil spills, which can affect various aspects of daily human activities (Yang, 2014; Hildur et al., 2015). As an important hub of maritime transportation, the port is the assembly point of land and water transportation and an important material distribution center. More and more attention has been paid to the environmental problems in the port. There is a high risk of oil spill in the oil terminal and coastal oil storage and transportation base. Therefore, routine surveillance in major ports is important for the early warning, prevention, and control of oil spills. The accurate detection and daily monitoring of offshore oil film in ports and large oil terminals are of great significance to the early warning and prevention and control of oil spill accidents. At present, large-scale oil spill detection (OSD) was generally carried out in marine oil spill accidents. There is a lack of OSD means and equipment for the port environment, which can not achieve the accurate detection of small scale and thin oil film. In addition, the traditional remote sensing technology and methods were usually carried on satellites and aircraft, so the time cycle of oil spill monitoring is long, which can not meet the needs of daily monitoring and long-term continuous operation. For such applications, techniques and sensors are required that permit high-frequency monitoring in the field, at a low cost per analysis. UV-induced fluorescence technology has the advantages of high sensitivity and rapid response, and the active detection method was not affected by bad weather. It can carry out noncontact detection all day. It is an active remote sensing technology with many advantages in offshore oil film detection

Oil Spill Detection, Identification, and Tracing. https://doi.org/10.1016/B978-0-443-13778-5.00003-5 Copyright © 2024 Elsevier Inc. All rights reserved.

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in a port environment. For example, Chase et al. (2005) have developed an OSD and alarm system that can detect trace oil and micron-thin oil film on the surface of seawater, in real time. It is difficult to achieve stable and low-cost measurements when continuous monitoring is desired. Further, producing sensor with anticorrosion properties and a low explosive risk is challenging, given the hazardous nature of old oil storage tanks. Optical measurement techniques are well suited to this purpose since they permit continuous surveillance (Tebeau et al., 2007). Excitation-emission matrix spectroscopy (EEMS) is an effective tool for the detection and quantification of oils on the surface of seawater (Tucker and Acree Jr., 1992; Baszanowska et al., 2013; Li et al., 2022). In EEMS, ultraviolet (UV) lasers are used as the excitation source in the spectral region between 240 and 355 nm and emission spectra are collected in the wavelength region between 300 and 500 nm (Zhou et al., 2013). Currently, EEMS require large, specialized laboratory equipment to acquire the fluorescence spectra, which requires a dedicated power source (Xie et al., 2022). Thus, further technical development is required so that EEMS could be practical for many field applications (Loh et al., 2021). Among the various optical principles available for sensing, UV-induced fluorescence methods have an advantage in that almost all oil types have characteristic fluorescence properties and fluorescence can be used to obtain valuable information required to detect oil on various backgrounds, including seawater, soil, ice, and snow (Christensen et al., 2005; Fingas and Brown, 2013). For example, fluorescence spectra and light absorption properties have been obtained by UV-induced fluorescence methods to detect trace oil in seawater. These advantages have led to the development of a variety of laboratory and field in€ nting et al., 2000; Baszanowska and Otremba, 2019). struments (Bu Fiber optical sensors can be used with UV lasers for the fluorescence detection of organic pollutants in water, especially oil products (Hillrichs and Neu, 1994; Karlitschek et al., 1998; Saito et al., 2014). A multichannel receiver, fluorescence LiDAR, or photomultiplier tube (PMT) can be used to detect and record the emitted fluorescence spectrum. When combined with fiber optics, in situ applications and long-distance surveillance systems are feasible (Bublitz et al., 1995). However, for short wavelengths in the UV spectral range, laser transmission is limited by increased fiber attenuation in the fiber material. The transmission quality will decrease when intense UV light propagates through the commonly used fused silica fibers (Hillrichs et al., 1994). In addition, fiber optics need more maintenance for day and night

Chapter 9 Case study

surveillance. These factors set limitations on the long-distance detection and monitoring of oil spills.

9.2 Design of UV-induced fluorescence device for oil spills detection The OSD device introduced in this book was designed and fabricated by Hou et al. (2017), who considered the UV-induced fluorescence of both clean and oil-bearing seawater to examine the efficacy of applying the coastal-mounted fluorescence filter system-based (FFS-based) method for the detection and monitoring of oil spills on the surface of seawater. The goal was to develop a compact, sensor-based instrument with day-long operability that could be mounted at a coastal site such as a harbor or port. This sensor-based instrument complements “late” spill modeling, once the oil spill has reached the shore or is very close to it. Fig. 9.1 presents schematic diagrams of the coastal-mounted sensor for monitoring oil spills. The continuous wave source is a xenon lamp (200e300 nm, L4634-01 synthetic silica glass) with a dedicated power source. For this study, a PMT was choosen as the real-time detection unit. The sensor was also required to meet a number of constraints, including a compact size, low power consumption, and independence from outside water and power requirements. These subassemblies are compact and integrated within a stainless-steel case (roughly 45 by 40 by 20 cm) which has anticorrosion and antiexplosion properties. The coastal-mounted sensor can detect micron-thin oil films in the laboratory and can operate at a range in excess of 5-m above the seawater surface. The result of experiments in the initial design and upgrade of sensor indicated that the xenon lamp and PMT can be proved to be highly effective for the detection of micron-thin oil film (the minimum oil film thickness is 1.0 mm) from a distance of 5-m above the target surface area. The key limitation for detection range was that the excitation intensity of xenon lamp is whether enough to enable oil film to be detected by the PMT. Besides, this 5-m limit is the approximate upper bound for reliable detection. The coastal-mounted sensor can achieve reliable detection once per second (maximum frequency) determined by the sensor’s control unit. Wireless transmission module is required for the coastal-mounted sensor, which has been designed to use a basic RS232/RS485 protocol for integration with industrial process

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Figure 9.1 Schematic diagram of the coastal-mounted sensor for monitoring oil spills: (a) The internal structure; (b) installation drawing; and (c) structural sketch of the coastal-mounted sensor. Reuse from Hou et al. (2017) under CC-BY 4.0.

control systems. The main parameters of the coastal-mounted sensor are shown in Table 9.1. Previous studies on oil-on-seawater monitoring showed that fluorescence signals were affected by solar radiation and the choice of continuous wave source. These fluorescence signals were unable to detect trace oil and micron-thin oil films, unless the signal was filtered and processed (Neil et al., 1980; Wang et al., 2014). In this study, an FFS was designed to improve the

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Table 9.1 The main parameters of the coastal-mounted sensor. The detection subassemblies

The case of the sensor

Excitation wavelength Detection wavelength Detection range Detection frequency Temperature range Size Material Protection level

200e300 nm 300e400 nm 0e5 m 0.1e1 Hz
20 to þ70 C Roughly 45 by 40 by 20 cm Stainless-steel IP66

Reuse from Hou et al. (2017) under CC-BY 4.0.

accuracy and reduce noise effects in the coastal-mounted sensor (Fig. 9.1, No. 3). The FFS is composed of two-stage band-pass optical filters (300e400 nm) and a convex lens, as shown in Fig. 9.2. Selection of the 300e400 nm band-pass filter was made considering the impacts of solar radiation, as well as the fact that most oils have a fluorescence peak within this wavelength band. Further, the reflection coefficient of the ocean surface to the light in this band is less than that for the 400e600 nm band

Figure 9.2 Schematic diagram of the FFS: (a) The optical schematic and (b) structural sketch of the FFS combined with the continuous wave source. Reuse from Hou et al. (2017) under CC-BY 4.0.

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(Miller et al., 1984; Accetta and Shumaker, 1993). The first-stage optical filter is larger in size to expand the viewing angle. The convex lens is used to concentrate the fluorescence signals and thus converge on a single point in the center of the second optical filter. For detection, a spectrograph’s (laboratory) or PMT’s (field) fiber optics are connected to the center of the second optical filter (Fig. 9.2a). To ensure the FFS receives fluorescence that is not affected by the continuous wave source, it is necessary to install a band-stop filter (300e400 nm) at the continuous wave source (Fig. 9.2b).

9.3 Long-term experiment of oil spill monitoring using UV-induced fluorescence device Since September 2017, the continuous daily monitoring and testing experimental research of sensors has been carried out in Lingshui wharf affiliated with Dalian Maritime University. The sensor is installed on the side of the wharf berth, 1 m above the historical highest water level. The sensor site location and experimental site diagram are shown in Fig. 9.3. Firstly, simulate the oil spill experiment at Lingshui wharf to verify the ability of the sensor to detect the thin oil film at sea. Take a small amount of 0# diesel oil sample and pour it into the iron support box. After forming a certain thickness of oil film, put it into the detection area of the sensor. The monitoring value and location information of monitoring points are displayed through the remote monitoring software, and the alarm threshold is set, as shown in Fig. 9.4. The remote

Figure 9.3 The experiments and sensors located at Lingshui port. Reuse from Hou et al. (2017) under CC-BY 4.0.

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Figure 9.4 Sensor monitoring software in simulation experiment.

monitoring software receives the monitoring data of the sensor through the wireless communication network, stores the PMT output results as the monitoring data, and the PMT output value was processed by the data change rate, and the 3.0 output is set as the oil spill warning threshold of the sensor. In the simulation oil spill monitoring experiment, when the simulated offshore oil film was placed in the sensor monitoring area, the monitoring data of the monitoring software was significantly higher than the alarm threshold, and the offshore oil spill early warning signal will be sent. The non-contact oil spill monitoring and alarm system equipment, which is excited by high-intensity UV and receives and analyzes the oil spill fluorescence, was installed at different monitoring points of the port and wharf and forms a distributed detection network to monitor the oil spill in real time all day. In case of an accident, the on-site audible and visual alarm is automatically carried out, and the oil spill photos, time, location, and other information are sent to the monitoring center through wireless transmission. The system parameters (sensitivity and threshold) can be adjusted to meet the monitoring and alarm requirements in different environments. Research, test, and select the most suitable UV light source and optical detector with a high multiplication effect and high response rate that can be

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used for fluorescence detection. Study the algorithm to reduce the false alarm rate and further improve the detection accuracy and sensitivity.

9.4 Routine surveillance of oil spills using UVinduced fluorescence device There are 15 oil berths in Dalian that are associated with the three major PetroChina ports in the Northern Yellow Sea. Three OSD sensors (one at each port) were installed, each on the periphery of an oil berth 2 m above the highest water line, to evaluate their ability to provide monitoring and routine surveillance of oil spills. During the period from December 1, 2018 to May 31, 2019, long-term continuous operation trials and routine surveillance demonstrations were conducted using the proposed OSD sensor. Because a considerable amount of monitoring data were generated during the 180 days of long-term continuous operation trials and routine surveillance, the data acquired in this study over a period of 8 days in each of the 6 months of the trial period were selected for analysis (Wang et al., 2020a, 2020b). The data obtained over these periods of 8 days were selected based on weather conditions and covered different weather conditions. Table 9.2. The weather condition selected 8 days in each of the 6 months. To analyze the long-term monitoring data more intuitively, these data were averaged. The monthly averaged data from the three monitoring points are shown in Fig. 9.5. The daily monitoring data indicated that, from 6:00 to 18:00, the sampling points exhibited increased fluorescence intensities (Fig. 9.5). Although the selection of the 300e400 nm bandpass filter was made considering the impacts of solar radiation, as well as the fact that most oils have a fluorescence peak within this wavelength band, the solar radiation possesses radiation power within this band, which could inevitably mix with the induced fluorescence of oil spills. Moreover, with the intensifying of solar radiation, the excitation effects of its UV band to oil slicks and other fluorescent substances contained in seawater could be further enhanced, resulting in an enhancement of the signal intensities received in the 300e400 nm wavelength. During the trial period, the maximum value of the data collected by the OSD sensor during each month, from December to May, gradually increased because the observation sites were located in the Northern hemisphere, where the daily solar radiation duration and intensity are relatively low during winter but increase during the spring and

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Table 9.2 The weather condition of selected 8 days in each of the 6 months.

Weather condition

Date

January 12, 2018 Sunny/Cloudy May 12, 2018 Cloudy/Snowy June 12, 2018 Snowy December 15, 2018 December 16, 2018 December 19, 2018 December 23, 2018 December 27, 2018 January 1, 2019 September 1, 2019 October 1, 2019

Temperature Date 11 C/4 C

Weather condition

Temperature

Rainy Sunny Cloudy

9 C/0 C 8 C/1 C 9 C/2 C

Sunny/cloudy

10 C/2 C

Cloudy

6 C/2 C

May 3, 2019 July 3, 2019 September 3, 2019 March 14, 2019

Sunny

7 C/0 C

March 18, 2019

Sunny

14 C/8 C

Sunny

8 C/1 C

March 20, 2019

Rainy/cloudy

9 C/3 C

Sunny


1 C/
4 C

March 26, 2019

Sunny

11 C/4 C

Cloudy


8 C/
13 C

March 29, 2019

Rainy/cloudy

9 C/2 C

Sunny/Cloudy Sunny


3 C/
8 C 1 C/
6 C

Sunny Rainy/cloudy

17 C/7 C 11 C/6 C

Sunny

4 C/
2 C

Sunny/cloudy

16 C/7 C

Sunny

18 C/11 C

3 C/
1 C
1 C/
9 C

November 1, 2019 January 16, 2019 January 19, 2019 January 26, 2019 January 30, 2019 May 2, 2019 August 2, 2019 November 2, 2019 February 14, 2019

Cloudy

5 C/
1 C

April 4, 2019 September 4, 2019 December 4, 2019 April 15, 2019

Sunny Cloudy Sunny Cloudy Cloudy Sunny Cloudy

0 C/
4 C 2 C/
6 C 1 C/
5 C 4 C/
7 C 1 C/
4 C
5 C/
11 C 0 C/
7 C

April 17, 2019 April 23, 2019 April 28, 2019 April 29, 2019 March 5, 2019 June 5, 2019 August 5, 2019

Sunny/cloudy Cloudy Sunny/cloudy Sunny Sunny Sunny Cloudy

18 C/11 C 16 C/9 C 15 C/8 C 16 C/11 C 26 C/16 C 19 C/13 C 24 C/15 C

Cloudy/Snowy


1 C/
7 C

Cloudy/rainy

21 C/12 C

February February February February

Sunny Sunny/Cloudy Sunny/Cloudy Sunny

9 C/1 C 6 C/0 C 7 C/0 C 10 C/2 C

December 5, 2019 May 20, 2019 May 25, 2019 May 26, 2019 May 30, 2019

Sunny Sunny/cloudy Rainy Cloudy

22 C/14 C 25 C/17 C 21 C/12 C 25 C/16 C

23, 2019 25, 2019 26, 2019 28, 2019

Reuse from Hou et al. (2022) under CC-BY 4.0.

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Figure 9.5 Monthly averaged monitoring data of no. 1, 2, and 3 (from left to right) oil spill detection sensors. Reuse from Hou et al. (2022) under CC-BY 4.0.

into the summer. In addition, the voltage data obtained via the OSD sensor were always lower than 2.0 when there was no oil spill contamination within the detecting area (in the laboratorysimulated experiments were consistently >2.5 V). Therefore, the output of PMT can be regarded as reliable parameters for the detection of the different oil samples’ fluorescence signals. Meanwhile, the monitoring data exhibited stronger fluctuations and greater noise interference at night than during the day. The strong fluctuations and interference noise at night were due to the filtering of the background environmental fluorescence during

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the OSD sensor signal processing. The sensor control unit controls the PMT sampling frequency at a level greater than the UV excitation source flash frequency. The solar radiation is strong during the day, which could inevitably mix with the induced fluorescence of environment and oil slicks on seawater. Consequently, the fluctuations and interference noise were weaker during daytime and false alarms can be avoided when environmental background values are high. Moreover, there were different ships operations at the three berths accompanied by other lighting sources at night, which caused the variation pattern of data obtained iat night from the sensor installed on the three adjacent berths were significantly different. In addition, this was attributed to the fact that microorganisms in seawater generate a certain amount of fluorescence that contributes to a low-level background fluorescence signal at night. It should be noted that the results above are similar to the modeling patterns reported in previous studies that examined solar radiation reflectance from oil spills (Turney et al., 2009; Wan et al., 2013). Although the effects of solar radiation on the sensor gradually increased during the day and decreased at night, the increase and decrease were slow processes that did not involve sudden changes. Thus, as shown in Fig. 9.6a, the data change rate (DCR) value fluctuates between 0.7 and 1.4, which provides a quantitative indication of the DCR distribution during routine

Figure 9.6 Data change rate (DCR) of the oil spill detection sensor for simulated oil spill experiments: (a) Control (no oil slick in the detection area) and (b) four oil spill simulations at 04:00, 10:00, 16:00, and 21:00 h. Reuse from Hou et al. (2022) under CC-BY 4.0.

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surveillance when there are no oil spills and reduces ambient interference. Meanwhile, when four oil spill simulations were performed at the observation site at 04:00, 10:00, 16:00, and 21:00 h, the DCR value increased abruptly, generating a peak for each simulated spill with an intensity >4.0, as shown in Fig. 9.6b. After repeated experiments, a sensor DCR value of 4.0 was set as the OSD threshold. When this threshold is exceeded, the OSD sensor is designed to issue an oil spill alarm. In this manner, false alarms resulting from slow increases in the environmental background fluorescence can be avoided. The results verified that the DCR parameter more intuitively reflected the intensity of the fluorescence emitted by oil in the monitoring area of the sensor. Moreover, the results of the onsite oil spill experiments indicated that the occurrence of oil spills can be effectively monitored using the OSD sensor. This finding demonstrates that the sensor can be effectively employed for the routine monitoring of surface oil spills along coasts and in harbors by enabling continuous monitoring and timely alerts. The UV excitation source and monitoring method employed in this study is not adequate for quantitative analysis, although they can be applied extensively to achieve timely oil spill alerts (regardless of oil species or the exact thickness of the film). Thus, the compact, low-cost sensor can be also employed for prompt alarm signaling of an oil spill in other aquatic environments where oil products may leak.

References Accetta, J.S., Shumaker, D.L., 1993. The Infrared and Electro-Optical Systems Handbook. Infrared Information Analys; SPIE Optical Engineering Press, Bellingham, WA, USA. Baszanowska, E., Otremba, Z., 2019. Detecting the presence of different types of oil in seawater using a fluorometric index. Sensors 19 (17), 3774. Baszanowska, E., Otremba, Z., Toczek, H., 2013. Fluorescence spectra of oil after it contacts with aquatic environment. J. Kones 20, 29e34. Bublitz, J., Dickenhausen, M., Grätz, M., 1995. Fiber-optic laser-induced fluorescence probe for the detection of environmental pollutants. Appl. Opt. 34, 3223. € nting, U., Karlitschek, P., Lewitzka, F., 2000. Analysis of fluorescence tracers in Bu water by a mobile fiber-optical laser-fluorimeter. In: Proceedings of the Conference on Lasers and Electro-Optics, San Francisco, CA, USA, 7e11 May 2000. Chase, C.R., Van Bibber, S., Muniz, T.P., 2005. Development of a non-contact oil spill detection system. Proc. Oceans 2, 1352e1357. Christensen, J.H., Hansen, A.B., Mortensen, J., Andersen, O., 2005. Characterization and matching of oil samples using fluorescence spectroscopy and parallel factor analysis. Anal. Chem. 77 (7), 2210e2217.

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Fingas, M., Brown, C.E., 2013. Detection of oil in ice and snow. J. Mar. Sci. Eng. 1, 10. Hildur, K., Templado, C., Zock, J.P., 2015. Follow-Up genotoxic study: chromosome damage two and six years after exposure to the prestige oil spill. PLoS One 10, 837e845. Hillrichs, G., Neu, W., 1994. UV laser induced fluorescence to determine organic pollution in water. Laser Remote Sens. 4, 109e112. Hillrichs, G., Karlitschek, P., Neu, W., 1994. Fiber optic aspects of UV laser spectroscopic in situ detection of water pollutants. Int. Soc. Opt. Eng. 2293, 178e185. Hou, Y., Li, Y., Liu, B., Liu, Y., Tong, W., 2017. Design and implementation of a coastal-mounted sensor for oil film detection on seawater. Sensors 18 (1), 70. Hou, Y., Li, Y., Li, G., Tong, X., Wang, Y., 2022. Oil-spill detection sensor using ultraviolet-induced fluorescence for routine surveillance in coastal environments. Appl. Phys. B 128, 41. € nting, U., 1998. Detection of aromatic pollutants Karlitschek, P., Lewitzka, F., Bu in the environment by using UV-laser-induced fluorescence. Appl. Phys. B 67, 497e504. Li, Y., Jia, Y., Cai, X., Xie, M., Zhang, Z., 2022. Oil pollutant identification based on excitation-emission matrix of UV-induced fluorescence and deep convolutional neural network. Environ. Sci. Pollut. Res. 29, 68152e68160. Loh, A., Ha, S.Y., Kim, D., Lee, J., Baek, K., Yim, U.H., 2021. Development of a portable oil type classifier using laser-induced fluorescence spectrometer coupled with chemometrics. J. Hazard Mater. 416, 125723. Miller, A.R., Brown, R.M., Vegh, E., 1984. New derivation for the rough-surface reflection coefficient and for the distribution of sea-wave elevations. IEE Proc. H Microw. Opt. Antennas 131, 114e116. Neil, R.A., Bujabijunas, L., Rayner, D.M., 1980. Field performance of a laser fluorosensor for the detection of oil spills. Appl. Opt. 19, 863e870. Saito, Y., Takano, K., Kobayashi, F., 2014. Development of a UV laser-induced fluorescence lidar for monitoring blue-green algae in Lake Suwa. Appl. Opt. 53, 7030e7036. Tebeau, P.A., Hansen, K.A., Fant, J.W., 2007. Assessing the long-term implementation costs versus benefits associated with laser fluorosensor spill response technology. In: Proceedings of the Arctic and Marine Oilspill Program (AMOP) Technical Seminar, Edmonton, AB, Canada, 5e7 June 2007. Tucker, S.A., Acree Jr., W.E., 1992. Excitation versus emission spectra as a means to examine selective fluorescence quenching agents. Appl. Spectrosc. 46 (9), 1388e1392. Turney, D.E., Anderer, A., Banerjee, S., 2009. A method for three-dimensional interfacial particle image velocimetry (3D-IPIV) of an airewater interface. Meas. Sci. Technol. 20 (4), 045403. Wan, W., Hua, D., Le, J., Liu, M., 2013. Laser induced chlorophyll fluorescence lifetime measurement and characteristic analysis for plant drought-stress. In: IEEE 11th International Conference on Electronic Measurement and Instruments, pp. 574e578. Wang, H., Liu, Z., Kim, S., 2014. Microfluidic acoustophoretic force based lowconcentration oil separation and detection from the environment. Lab Chip 14, 947e956. Wang, Y., Gao, Y., Li, Y., Tong, X., 2020a. A worker-selection incentive mechanism for optimizing platform-centric mobile crowdsourcing systems. Comput. Network. 171, 107144.

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Wang, Y., Cai, Z., Zhan, Z.-H., Zhao, B., Tong, X., Qi, L., 2020b. Walrasian equilibrium-based multiobjective optimization for task allocation in mobile crowdsourcing. IEEE Trans. Comput. Soc. Sys. 7 (4), 1033e1046. Xie, M., Jia, Y., Li, Y., Cai, X., Cao, K., 2022. Experimental analysis on the optimal excitation wavelength for fine-grained identification of refined oil pollutants on water surface based on laser-induced fluorescence. J. Fluoresc. 32, 257e265. Yang, T., 2014. Dynamic assessment of environmental damage based on the optimal clustering criteriondtaking oil spill damage to marine ecological environment as an example. Ecol. Indicat. 51, 53e58. Zhou, Z., Guo, L., Shiller, A.M., Lohrenz, S.E., Asper, V.L., Osburn, C.L., 2013. Characterization of oil components from the Deepwater Horizon oil spill in the Gulf of Mexico using fluorescence EEM and PARAFAC techniques. Mar. Chem. 148, 10e21.

10 Case study: Oil spill extraction in spaceborne dual-polarization SAR image Polarization SAR systems include two basic modes, i.e., a fully polarization system and a dual-polarization system. Due to different combinations of polarization channels, dualpolarization systems are further divided into HH-VV mode, VVVH mode, and HH-VH mode. This chapter focuses on the scattering mechanism and characteristics of marine oil films under multimode dual-polarization SAR. The term multimode polarimetric SAR here refers to the SAR data of multiple polarimetric modes, including full polarization and three kinds of dual-polarization, rather than the SAR data of different sensors or imaging modes.

10.1 Scattering mechanism of oil film on the sea surface 10.1.1 Signal-to-noise ratio in SAR system The radar backscattered signals of marine oil film targets and background seawater are only a part of the total incident power, and the noise equivalent sigma zero (NESZ) d is the background noise of the SAR system, representing the signal level of the radar backscatter cross-section (Minchew et al., 2012; Skrunes et al., 2014). There are two types of noise in the resolution unit, namely additive noise and multiplicative noise, whose magnitude is mainly affected by factors such as radar antenna power, antenna gain, system energy loss, and system ambient temperature. Additive noise is mainly caused by thermal noise generated by the background environment and operating system and does not depend on the strength of the target surface echo signal. Multiplicative noise is considered to be caused by time-varying or nonlinear SAR operating systems, which depend on the reflected signal and have a certain impact on ranging accuracy and image Oil Spill Detection, Identification, and Tracing. https://doi.org/10.1016/B978-0-443-13778-5.00013-8 Copyright © 2024 Elsevier Inc. All rights reserved.

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quality. The oil spill detection capability of SAR images is affected by the NESZ to a certain extent, and the normalized radar crosssection (NRCS) data below the NESZ baseline is considered to be damaged by noise. Currently, some SAR sensors, such as UAVSAR (Liu et al., 2011; Hassani et al., 2020), can achieve lower background noise, while spaceborne SAR systems have relatively higher background noise. For example, the NESZ range of Radarsat-2 is
27.5 to
43 dB (Skrunes et al., 2014). Typically, these are the types of data used for oil spill detection. Therefore, it is important and necessary to use NESZ as a benchmark to measure the detection limits of backscattered signals from the targets on the sea surface. This research uses three scenes from Radarsat2 fully polarization SAR image to analyze the signal noise level. On the one hand, it analyzes and compares the signal-to-noise level differences of images with the same sensor in the same mode under different incident angles. On the other hand, it compares the signal level differences of different oil film targets in the same image in different polarization channels. Fig. 10.1 shows the relative signal levels of NRCS and NESZ for each polarization channel in three sets of experiments. In the VV channel, most of the seawater sample data is higher than the NESZ baseline, while a small portion of the data spans and is lower than the NESZ baseline. The thick oil film sample data all span the NESZ baseline, and more than 50% of the samples are lower than the baseline value. The data range is from 7.4 dB below the baseline to 6.6 dB above the baseline, mainly distributed in the backscattering data range lower than the seawater sample. The thin oil sample data is between the seawater and the thick oil film, with about 50%e70% of the data located above the NESZ baseline. The samples with high incidence angles are

Figure 10.1 SNR analysis result of different polarization channels (x-axis indicates the incident angle and y-axis indicates NESZ value): (a) VV channel; (b) HH channel; (c) VH channel. Reuse from (Li et al., 2018) under CC-BY 4.0.

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

relatively high because the samples with high incidence angles are selected at a smaller oil belt boundary and the oil-water mixture is relatively sufficient. Compared to the VV polarization channel, the backscattering of all samples in the HH polarization channel decreases faster, and all samples decrease by about 5 dB compared to the corresponding samples in the VV channel. Therefore, the data in the HH channel is closer to the background noise. In cross-polarization channels, the vast majority of various sample data are located below NESZ, all contaminated by noise, and the overlapping data distribution ranges are clearly indistinguishable. In addition, the difference between oil film and seawater with different thicknesses in VV polarization channels is the largest, followed by HH channels and VH channels.

10.1.2 Scattering mechanism of polarization SAR system 10.1.2.1 Scattering mechanism of dual-polarization SAR system The scattering matrix and scattering vector of the HH-VV dualpolarization mode can be expressed as Eqs. (10.1.1) and (10.1.2): " # S HH 0 S HH
VV ¼ (10.1.1) 0 SVV pffiffiffi T S HH
S VV  = 2

k ¼ ½ S HH þ S VV

(10.1.2)

The acquisition of a dual-polarization coherence matrix is similar to that of the fully polarization covariance matrix, and the corresponding relationship between its elements and the fully polarization covariance matrix can be shown as Eq. (10.1.3): L 1X kk H L i¼1 # C11
C13 þ C31
C33

CT HH
VV D ¼

" 1 C11 þ C13 þ C31 þ C33 ¼ 2 C11 þ C13
C31
C33

C11
C13
C31 þ C33

(10.1.3)

where Cij is the fully polarization covariance matrix element in row i and column j. The scattering matrix and scattering vector of the VV-VH dualpolarization mode can be expressed as Eqs. (10.1.4) and (10.1.5):

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" SVV
VH ¼ k ¼ ½ S VV

0 S VH 0

# (10.1.4)

S VV

pffiffiffi T ðS VH þ i  S VH Þ= 2 

(10.1.5)

The correspondence between the coherence matrix and its elements and the fully polarization covariance matrix can be expressed as Eq. (10.1.6): " # L 2C33 ð1
iÞC32 1X 1 H kk ¼ CT VV
VH D ¼ (10.1.6) L i¼1 2 ð1 þ iÞC23 C22 The scattering matrix and scattering vector of the HH-VH dual-polarization mode can be expressed as Eqs. (10.1.7) and (10.1.8): " # S HH SVH S HH
VH ¼ (10.1.7) 0 0 k ¼ ½ S HH

pffiffiffi T ðS VH þ i  S VH Þ= 2 

(10.1.8)

The correspondence between the coherence matrix and its elements and the fully polarization covariance matrix can be expressed as Eq. (10.1.9): " # L 2C11 ð1
iÞC12 1X 1 H kk ¼ CT HH
VH D ¼ (10.1.9) L i¼1 2 ð1 þ iÞC21 C22 Since the dual-polarization SAR system only contains partial polarization information, its polarization decomposition method is based on a two-dimensional matrix for feature parameter extraction, and the implementation process is similar to the fully polarization mode: T dual ¼

2 X i¼1

 ui ¼ ej4i cos ai

li ui uH i

sin ai cos bi e jdi

(10.1.10) T

(10.1.11)

where ai is the polarization target scattering angle; bi is the radar line of sight direction angles; 4i and di are the phase angle of polarization scattering from the target; li is the two nonnegative eigenvalues corresponding to dual-polarization (satisfying l1 > l). Therefore, for dual-polarization SAR systems, considering the extreme distribution problem in the H/a plane, the H/a boundary of the plane is corrected, and the effective boundary is symmetric

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

to about a ¼ 45 plane, which can be expressed as Eqs. (10.1.12) and (10.1.13): " # 1 0 T1 ¼ ;0m  1 (10.1.12) 0 m " # m 0 ;0m  1 (10.1.13) T2 ¼ 0 1 The HH-VV dual-polarized H/a plane is modified based on the corresponding region of full polarization to form eight effective regions (Fig. 10.2). Its meaning is similar to that of fully polarization, corresponding to different types of scattering mechanisms. The plane divides the scattering entropy into three levels: low entropy (H ˛ [0,0.6]), medium entropy (H ˛ (0.6,0.95]), and high entropy (H ˛ (0.95,1)); For each level of entropy, the scattering angle is further adjusted. There are three levels corresponding to surface scattering, dipole scattering, and multiple scattering. Specifically. In the low entropy region, surface scattering and dipole scattering are characterized by the a ¼ 40 boundary, and dipole

Figure 10.2 Two-dimensional H/a feature space and division dual-polarization SAR.

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scattering and multiple scattering are divided based on the a ¼ 46 boundary. Therefore, regions 6, 7, and 8 correspond to low-entropy multiple scattering, low-entropy dipole scattering, and low-entropy surface scattering, respectively. In the middle entropy region, surface scattering and dipole scattering are characterized by the a ¼ 34 boundary, and dipole scattering and multiple scattering are divided based on the a ¼ 46 boundary. Therefore, regions 3, 4, and 5 correspond to low entropy multiple scattering, low entropy dipole scattering, and low entropy surface scattering, respectively. In the middle entropy region, surface scattering and dipole scattering are characterized by the a ¼ 46 boundary, and dipole scattering and multiple scattering are divided based on the a ¼ 33.2 boundary. Therefore, regions 1 and 2 correspond to low entropy multiple scattering, low entropy dipole scattering, and low entropy surface scattering, respectively.

10.1.2.2 Scattering mechanism of fully polarization SAR system The recognition and extraction of marine oil films depend on the different scattering mechanisms between the marine oil film and the background seawater. Polarization SAR systems transmit signals and receive echo signals that interact with the sea surface to capture the polarization information of the target. Through the interpretation and analysis of the polarization information, important indicators in the sea surface scattering signals can be effectively extracted. Many research efforts have been devoted to the analysis and research of different scattering mechanisms between oil films and seawater using fully polarization SAR systems in order to provide effective information and theoretical assistance for oil spill detection and extraction in fully polarization SAR systems. The polarization target decomposition method interprets and studies the physical mechanisms of targets based on the polarization scattering matrix. After continuous research and expansion, it has been widely used in the extraction of target scattering characteristics. In this study, the Cloude target decomposition method is selected to obtain the feature vectors and extended parameters of the target. The processing process can be expressed as follows (Liu, 2012): CT 3 D ¼ U 3 ½SU 3
1 ¼

i¼3 X i¼1

li T i ¼

i¼3 X i¼1

li ui uT i

(10.1.14)

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

2 U 3 ¼ ½ u1

u2

cosðai Þe j4i

3

7 6 jðdi þ4i Þ 7 u3  ¼ 6 5 4 sinðai Þcosðbi Þe sinðai Þcosðbi Þe jðgi þ4i Þ

(10.1.15)

where a is the polarization target scattering angle; b is the radar line of sight direction angle; d and g are the phase angles of target polarization scattering, with an angle range of [0e90 degrees]; Ti represents the scattering mechanism; li is the corresponding nonnegative eigenvalue (l1 > l2 > l3) represents the proportion of Ti in all scattering mechanisms. The three eigenvalues of the polarization coherence matrix can be further expanded into polarization characteristic parameters through mathematical operations to quantitatively describe the scattering mechanism and characteristics of the target, namely, the classical characteristic parameter H/a, along with the continuous development and improvement of new parameters, which are constructed based on the following polarization parameters. In this section, the scattering characteristics of the oil film and surrounding background seawater are analyzed on the H/a plane (Liu, 2012; Minchew et al., 2012; Skrunes et al., 2014). The polarization scattering entropy H represents the randomness of the scattering mechanism in the dominant target region, with a range of [0,1]. The two endpoint values represent the extreme cases of the two polarization states, respectively. When H is 0, the matrix has only one nonzero eigenvalue, which is a fully polarization state with a unique and deterministic scattering process. When H is 1, the three eigenvalues of the matrix are equal, and the target scattering presents completely random noise, which corresponds to a completely nonpolarization state. If the entropy value is very low, the system may be seen as having weak depolarization, and the dominant scattering mechanism may be seen as specifically identifiable equivalent point scattering, i.e., selecting the eigenvector corresponding to the largest eigenvalue, so the other two secondary vector components may be ignored. The definition is given as Eqs. (10.1.16) and (10.1.17) (Liu, 2012; Minchew et al., 2012): H ¼


3 X

Pi log3 Pi

(10.1.16)

i¼1

Pi ¼

li l1 þ l2 þ l3

(10.1.17)

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Average scattering angle a is used to describe the potential scattering mechanism of a target and can provide its isotropy during the scattering process. It is usually analyzed in combination with polarization entropy H, which can be defined as Eq. (10.1.18): a ¼ p1 a1 þ p2 a2 þ p3 a3

(10.1.18)

The H/a plane is widely used in the analysis and research of target scattering characteristics. The plane divides the scattering entropy into three levels: low entropy (H ˛ [0,0.5]), medium entropy (H ˛ (0.5,0.9]), and high entropy (H ˛ (0.9,1]); for each level of entropy, the scattering angle is further adjusted a. There are three levels corresponding to surface scattering, dipole scattering, and even scattering. The H/a plane is divided into effective interior regions by two boundary curves, the curve boundaries being expressed as Eqs. (10.1.19) and (10.1.20): 2 3 1 0 0 6 7 7 T1 ¼ 6 (10.1.19) 4 0 m 0 5; 0  m  1 0

T2 ¼

8 > > > > > > > > > > > > > > > >
> > 2m
1 > > >6 > 6 > > 6 > > > 6 0 > > 6 > > :4 0

0 0

m

3

7 7 7 0 7; 7 5 2m 0 0

0  m  0:5

3

(10.1.20)

7 7 7 1 0 7; 0:5  m  1 7 5 0 1

The plane is divided into eight effective scattering regions based on the above parameter levels and boundary conditions, which correspond to different physical scattering mechanisms. As shown in Fig. 10.3, the details are as follows (Liu, 2012; Ozigis et al., 2018). The effective region 1 corresponds to high-entropy multiple scattering. Even scattering mechanisms can also be distinguished in high entropy regions (H ˛ (0.9,1)), which are commonly used in forestry and exist in well-developed branches and vegetation canopies. Effective region 2 corresponds to high-entropy vegetation scattering. In the region of high entropy range (H ˛ (0.9,1)),

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

Figure 10.3 Two-dimensional H/a feature space and division fully polarization SAR.

when the scattering angle is 45 degrees, this may be caused by single scattering generated by a large number of anisotropic needle-shaped particles or multiple scattering generated by lowloss symmetric particles, usually reflecting forest canopy scattering and some vegetation surface scattering with randomly high anisotropic scattering elements. The effective region 3 corresponds to medium entropy multiple scattering. Typically, it comes from targets with a medium-entropy (H ˛ (0.5,0.9]) dihedral angular scattering as the dominant mechanism. For example, in forestry applications, canopy interaction increases the entropy of the scattering process. Effective region 4 corresponds to medium entropy vegetation scattering. Typically, targets of the dipole type with a dominant mechanism of medium entropy include vegetation surface scattering with anisotropic scatters, and the central statistical distribution of the orientation angle increases the entropy value. The effective region 5 corresponds to medium entropy surface scattering. This usually occurs in medium entropy (H ˛ (0.5,0.9]) scattering mechanism targets, reflecting an increase in the entropy due to the changes in the surface roughness and canopy transport effects. Therefore, as the surface roughness and related length change, or the surface is

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composed of oblate ellipsoidal scatters, it will increase the entropy value. The effective region 6 corresponds to low-entropy multiple scattering. Typically reflecting low-entropy double and even scattering, such as scattering provided by isolated dielectric and metal dihedral angles. The effective region 7 corresponds to low-entropy dipole scattering. Generally, there is a strong correlation mechanism inside, and there is a significant imbalance in the amplitude between the copolarization channels, which leads to the occurrence of isolated dipole scattering. The effective region 8 corresponds to low-entropy surface scattering. It usually occurs on low-entropy scattering mechanism targets with entropy values below 0.5 and scattering angles below 42.5 . In addition, it includes geometric and physical optical surfaces, Bragg scattering, and mirror scattering phenomena that cannot undergo 180 degrees phase inversion between copolarization channels.

10.1.3 Comparison results of the multimode polarization SAR scattering mechanism of ocean oil films 10.1.3.1 Comparison results on H/a of relative oil film thickness under multimodal polarization SAR H/a method describes the randomness of the target and its corresponding scattering mechanism. These polarization parameters have rotational invariance. Fig. 10.4 shows the H/a of oil films with different thicknesses and background seawater. In a fully polarization system, the overall entropy value of seawater is lower than that of oil film, about 0.4e0.7, and the average scattering angle is less than 30 degrees, which mainly distributes in H/ a regions 5 and 8. This indicates that the ocean surface is mainly characterized by medium entropy surface scattering, accompanied by low-entropy Bragg surface scattering. This is partly due to the high roughness of the ocean surface and partly due to the high NESZ baseline in the study area, which increases the randomness of the signal affected by noise. In addition, when approaching a high incidence angle (45 degrees), especially at grazing angles, the sea surface will show the characteristics of a multipath dihedral angle, which increases the randomness of the ocean surface scattering mechanism (Li et al., 2014). When oil leaks from the seabed at a relatively slow and constant rate and is released to the sea surface, a certain thickness of oil layer is formed after long-term accumulation. When obtaining the image data, the leeward oil film is relatively thick with clear boundaries, while the windward oil film is relatively thin and fully mixed

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

Figure 10.4 H/a plane distribution of multimode polarized SAR with different thicknesses of oil film.

with seawater (Wismann et al., 1998). Therefore, the entropy value of the thick oil film significantly increases in H/a plane, which is mainly distributed in regions 1 and 3, corresponding to the high entropy multiple scattering mechanism and the medium entropy multiple scattering mechanism, respectively. This indicates that the presence of oil films has changed the scattering mechanism of the sea surface. Usually, an oil film forms a surface structure with a certain thickness between the ocean and the atmosphere.

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The two rough interfaces between seawater oil film and oil film atmosphere divide the medium into three dielectric layers: atmosphere, oil film, and seawater. Multiple scattering mechanisms may occur in the middle layer of the oil film, which contains multiple scattering mechanisms and exhibits high randomness (Li et al., 2014). The entropy value of a thin oil film is between seawater and a thick oil film, ranging from 0.8 to 0.9, with an average scattering angle range of 45e55 degrees. At the H/a plane, it is mainly distributed in regions 4 and 5, corresponding to medium entropy dipole scattering and medium entropy surface scattering, respectively. To explore the scattering mechanism recognition performance of different dual-polarization modes, oil films of different thicknesses in the same H/a plane divided area are analyzed. For HH-VV mode dual-polarization, seawater samples are mainly distributed in the low-entropy surface scattering region, while a small number of samples are distributed in the mediumentropy surface scattering region. The thick oil film is subjected to high-entropy multiple scattering and dipole scattering, with a small number of samples distributed in medium-entropy multiple scattering. The thin oil film is located between the thick oil film and seawater mainly distributed in mid-entropy surface scattering and mid-entropy dipole scattering. In the HH-VV mode, various targets can basically maintain effective regions and distribution results similar to fully polarization information, but the mixing between oil film and seawater targets is to some extent higher than fully polarization, and the distribution of target samples is relatively divergent. The distribution of target sample data for dual-polarization in VV-VH mode and HH-VH mode is similar. The distribution of seawater samples with low entropy surface scattering mechanisms in VV-VH mode is relatively concentrated, but the oil film samples distributed in medium entropy surface scattering and dipole scattering are more dispersed. In the HHVH mode, the mixing between thick oil film, thin oil film, and seawater is relatively severe, making it difficult to distinguish the effective boundaries between different targets. The distribution range of the two modes on the H parameter is relatively large, but the three scattering mechanisms of low, medium, and high entropy cannot be distinguished in a parameter distribution. Polarization entropy can characterize the degree of randomness in the target area. In order to further explore the degree of difference between the oil spill detection information and the fully polarization information in the different dual-polarization modes, polarization entropy-corresponding scatter distribution comparison results were constructed for the different dual-

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

205

Figure 10.5 Comparison of scattering entropy scatter distribution between different dual-polarization modes and fully polarization of different oil thicknesses: (a) Fully polarization versus HH-VV mode; (b) fully polarization versus HV-VV mode; (c) fully polarization versus HV-HH mode.

polarization modes and the fully polarization data. As shown in Fig. 10.5, there are certain differences between the three dualpolarization modes and the fully polarization information. Among them, the HH-VV mode is the closest to the fully polarization mode, but the clustering distribution between various targets is slightly different from the fully polarization mode. Some data distributions are slightly lower than the corresponding data in the fully polarization mode. The scatter results of VV-VH mode data are significantly lower than those of fully polarization data, and the clustering effects of various targets are similar to those of HH-VV mode, but oil film targets in high entropy regions are relatively divergent. Compared with the fully polarization data, the distribution of various target samples in HH-VH mode is more divergent, and the aliasing is more severe. In summary, in the fully polarization mode, the main scattering mechanisms of the oil film targets with different thicknesses are clearly distinguished. The HH-VV polarization mode is closer to fully polarization information and can better retain similar information, followed by the VV-VH mode, and slightly worse for the HH-VH mode.

10.1.3.2 Comparison results on H/a of different types of oil films under multimodal polarization SAR In a fully polarization system, the scattering characteristics of different types of oil films exhibit certain differences. The entropy value of seawater is about 0.15e0.3, and the average scattering angle is less than 20 degrees. It is concentrated in Region 8, corresponding to low entropy surface scattering in the H/a plane. The distribution of vegetable oil is similar to that of seawater

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but slightly higher, with an entropy value of about 0.3e0.5 and an average scattering angle range of about 10e30degrees. It is mainly distributed in region 8, with a small portion distributed in Region 5 in the H/a plane, corresponding to the low entropy surface scattering mechanism and the medium entropy surface reflection mechanism, respectively. This indicates that the main mechanism of vegetable oil is surface scattering, which is relatively different from the surface of seawater. The main range of entropy values of emulsified oil is between 0.5 and 0.8, and the scattering angle is mainly distributed between 20 and 40 degrees. It is concentrated in Region 5, with a small portion distributed in Region 8 in the H/a plane, corresponding to medium entropy surface scattering and low entropy surface scattering, respectively. The main range of crude oil entropy value is between 0.75 and 0.95, and the scattering angle is mainly distributed between 30 and 50 degrees, which is higher than seawater, vegetable oil, and emulsified oil. It is concentrated in Region 4, with a small portion distributed in Region 2 and region 5 in the H/a plane, corresponding to medium entropy vegetation scattering, high entropy vegetation scattering, and medium entropy surface scattering, respectively. This indicates that the scattering mechanism of crude oil and emulsified oil is relatively complex, while the scattering mechanism of vegetable oil film and seawater is relatively simple, and the difference between crude oil and vegetable oil is greater than that between emulsified oil and vegetable oil. For different dual-polarization modes, the overall results presented are similar to those of the aforementioned study area. In the HH-VV mode dual-polarization system, the seawater sample is distributed as a whole on the low-entropy surface scattering. Vegetable oil is mainly distributed on low-entropy surface scattering, while a small number of samples are distributed on medium-entropy surface scattering. In addition, the scattering angles of vegetable oil film and seawater are basically equal, so they exhibit the same scattering angle level within the same scattering mechanism area. Emulsified oil is distributed on mediumentropy surface scattering and low entropy surface scattering. Crude oil is mainly distributed in medium-entropy surface scattering, with a small amount distributed in medium-entropy dipole scattering. The distribution results of different types of oil film and seawater targets in the HH-VV mode are similar to those of fully polarization information, but the recognition ability of the scattering mechanism is slightly lower than that of fully polarization information, and there is a certain overlap phenomenon between targets. The distribution and shape of target sample data in VV-VH mode and HH-VH mode are similar,

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

with different oil film targets distributed on the surface scattering. The sample distribution in VV-VH mode is slightly better than that in HH-VH mode, and the clustering effect of sample distribution is obvious, as shown in Fig. 10.6. The comparison results of polarization entropy corresponding to scattering distribution based on different dual-polarization modes and fully polarization data are shown in Fig. 10.7. The HH-VV mode is closest to the fully polarization mode, with an

Figure 10.6 H/a plane distribution of multimode polarized SAR with different types of oils.

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Figure 10.7 Comparison of scattering entropy scatter distribution between different dual-polarization modes and fully polarization of different oil types: (a) Fully polarization versus HH-VV mode; (b) fully polarization versus HVVV mode; (c) fully polarization versus HV-HH mode.

overall distribution close to a linear result. However, various targets exhibit significant dispersion as entropy increases, indicating that the fit with fully polarization information decreases as randomness increases. The scattering entropy data of various targets in the VV-VH mode is significantly lower than that in fully polarization data, and the clustering effect of various targets is similar to that in the HH-VV mode. The target distribution in high-entropy areas is relatively divergent. Compared with fully

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

polarization data, the distribution of various target samples in the HH-VH mode data is more divergent and the clustering effect is poor. Therefore, the data distribution results of different types of oil films in this study area show that the HH-VV polarization mode is closer to the fully polarization information, but the distribution of various targets is more dispersed. The VV-VH mode is similar to the HH-VH mode, but the clustering of various targets in the VV-VH mode is more concentrated, and the aliasing phenomenon is lighter, which is better than the HH-VH mode. The classification targets in all three modes are more dispersed with the increase of polarization entropy.

10.1.3.3 Comparison results on H/a of oil films and oil-like films In the fully polarization SAR system, the entropy value of seawater is relatively low at about 0.2e0.4, and the average scattering angle is less than 20 degrees. It is concentrated in Region 8 in the H/a plane, indicating that Bragg surface scattering is the dominant scattering mechanism on the ocean surface. On the ocean surface near the fishing ground, the biodiversity activities in the aquaculture area are frequent, and the concentration of surface organic matter is relatively high. The gradually accumulated organic matter eventually forms a biological oil film with a certain viscosity. The presence of the oil film increases the randomness of polarization scattering in the area, resulting in a high entropy value of about 0.8e0.95 and an average scattering angle range of about 45e55 degrees. It is mainly distributed in regions 1 and 2 in the H/a plane, corresponding to the high entropy multiple scattering mechanism and the high entropy dipole scattering mechanism, respectively. The sea surface caused by the atmospheric front exhibits a dark characteristic area similar to an oil film, with entropy values lower than that of a biological oil film but slightly higher than the seawater background. The entropy values are closer to the rough sea area in the lower left area of the image, mainly distributed in region 5, corresponding to the medium entropy surface scattering mechanism, and a small part distributed in region 8, corresponding to the low entropy surface scattering mechanism. This indicates that the dark regions caused by natural phenomena do not change the dielectric constant of seawater, and the scattering mechanism is the same as seawater, but the randomness of the surface increases. The information in the polarized space helps to distinguish it from marine oil films. For the HH-VV dual-polarization SAR system, the overall distribution of seawater samples is scattered by low entropy surfaces. The oil film is mainly distributed in the high entropy

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dipole scattering region and the medium entropy dipole scattering region, with a small number of samples distributed in the medium entropy multiple scattering and high entropy multiple scattering regions. The dark region samples caused by the atmospheric front are distributed in low entropy surface scattering and medium entropy surface scattering. In the HH-VV mode, the distribution of oil film, natural phenomenon dark areas, and seawater targets is similar to that of fully polarized information, but the recognition ability of the scattering mechanism is slightly lower than that of fully polarized information. The distribution of various target samples is relatively divergent, showing stronger randomness, and there is a certain degree of aliasing phenomenon between targets. The target sample data distribution and shape of VV-VH mode dual-polarization SAR and HH-VH mode dual-polarization SAR are similar. The sample distribution in VV-VH mode is relatively more concentrated than that in HHVH mode, and the targets in HH-VH mode are relatively dispersed. Two modes in the three scattering mechanisms corresponding to low, medium, and high entropy cannot be distinguished in the a parameter distribution, as shown in Fig. 10.8. The comparison results of polarization entropy corresponding to scatter distribution based on different dual-polarization modes and fully polarization data are shown in Fig. 10.9. The HH-VV mode is closest to the fully polarization mode, with a linear distribution. The clustering effect between various targets is good, but the local data of oil film targets is slightly higher than the data corresponding to the fully polarization mode, indicating that the randomness is higher than that of the fully polarization mode; The scattering entropy data of various targets in VV-VH mode is significantly lower than that in fully polarization data, and the clustering effect of various targets is similar to that in HH-VV mode. However, the oil film in the middle and high entropy regions is relatively divergent from the oil film-like targets. Compared with fully polarization data, the distribution of various target samples in HH-VH mode data is more divergent, and the clustering effect is poor. Therefore, the data distribution results of dark areas caused by oil film and natural phenomena in this study area also show that the HH-VV polarization mode is closer to fully polarization information and can better retain similar information, followed by the VV-VH mode, which is superior to the HH-VH mode, as shown in Fig. 10.9.

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

Figure 10.8 H/a plane distribution of multimode polarized SAR with oil films and oil-like films.

10.2 Oil spill detection algorithm based on the edge advantage characteristics of multitemporal region of interest Traditional visual saliency detection is often more sensitive to high brightness and strong boundary differences in the image, such as sea-land boundaries, islands, ships, etc. However, for oil spill detection in SAR images, the oil spill area with “dark

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Figure 10.9 Comparison of scattering entropy scatter distribution between different dual-polarization modes and fully polarization of oil films and oil-like films: (a) Fully polarization versus HH-VV mode; (b) fully polarization versus HV-VV mode; (c) fully polarization versus HV-HH mode.

spots” characteristics is the area that users pay more attention to, while targets such as islands and ships with bright features are considered clutter information. In addition, many oil-like films caused by natural phenomena on the sea surface, such as low wind speed areas on the sea surface, atmospheric fronts caused by local wind stress, leeward headlands, ocean internal waves, ship wakes, rain clusters, ebb tide beaches, upwelling, etc. (Liu, 2012), also show similar dark spot characteristics, which affect

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the interpretation, identification and detection of oil spills. Therefore, it is crucial to construct an SAR image oil spill region of interest (ROI) extraction algorithm based on the characteristics and imaging mechanism of oil spill targets in SAR images. The presence of different targets can affect the spatial distribution of SAR image pixels, as shown in Fig. 10.10. Different symbol symbols represent pixels with different intensities in the SAR image: Blue represents seawater pixels, gray represents inherent speckle noise, and black represents dark feature pixels such as oil film and oil-like film. The ideal pure sea surface is uniformly distributed (Fig. 10.10a), with only seawater and noise pixels present. When only oil spills occur on an ideal pure sea surface, the spatial distribution of image pixels exhibits locally significant low-intensity clustering (Fig. 10.10b). In reality, images may exhibit complex spatial distribution due to the inclusion of multiple targets (Fig. 10.10c). Both oil spills and oil-like phenomena exhibit low backscatter intensity, and there are several lowintensity clusters in the image (Shu et al., 2010). When conducting oil spill detection and analysis research based on SAR images, this chapter uses Sentinel-1A images covering the Oil Rock area of the Caspian Sea as the data source to propose a simple and fast ROI algorithm for locating continuous oil spills in multitemporal dual-polarization SAR images. Based on the frequency results of potential dark regions, lowprobability oil-film-like areas are removed, and the entire largescale image is reduced to a local range, reducing the computational complexity of image processing.

Figure 10.10 Comparison of visualization results between uniform and nonuniform areas on the sea surface: (a) Clean sea surface; (b) oil polluted sea surface; (c) sea surface with multiple targets.

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10.2.1 Wind field inversion Sea surface wind field retrieval using C-band spaceborne SAR images is usually inversed based on semiempirical geophysical model functions. It is a common method of modeling using the basic principles of radar sea exploration, mainly including CMOD4, CMOD-IFR2, CMOD5, and other series of models. It is a basic model widely used in C-band wind speed retrieval (Hersbach et al., 2007). The CMOD4 and CMOD-IFR2 models are developed based on ERS scatterometer and numerical simulation wind data from the European Center for Medium Range Weather Forecasting, as well as ERS, ECMWF numerical simulation wind data, and NOAA buoy data, respectively. However, they may exhibit underestimation at high wind speeds (Stoffelen and Anderson, 1997; Quilfen et al., 1998). Subsequently, Hersbach et al. (2007) further improved and developed the CMOD5 model to address the existing issues, overcoming the limitations of CMOD4 at high wind speeds. With the deployment by the European Space Agency, it gradually became widely applied in various fields (Xie et al., 2020, 2022). Some studies compared the accuracy of different models and measured data based on a large number of SAR image results, effectively proving that the accuracy of CMOD5 is superior to CMOD4 and CMOD-IFR2 (Hersbach et al., 2007; Nezhad et al., 2019), defined as Eq. (10.3.1): s0 ¼ b0 ½1 þ b1 cos 4 þ b2 cos 2 4

n

(10.3.1)

where b0, b1, and b2 are the functions of incident angle and wind speed. Assuming x¼(q
45)/25, b0 can be defined as Eq. (10.3.2): b0 ¼ 10a0 þa1 v f ða2 v; s0 Þ where:

( f ðs; s0 Þ ¼

g

ðs=s0 Þa gðs0 Þ; s < s0 gðsÞ; s  s0

(10.3.2)

(10.3.3)

1 1 þ expð
sÞ

(10.3.4)

a ¼ S0 ð1
gðS0 ÞÞ

(10.3.5)

gðsÞ ¼

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

8 > > a0 ¼ c 1 þ c 2 x þ c 3 x 2 þ c 4 x 3 > > > < a 1 ¼ c5 þ c6 x > g ¼ c9 þ c10 x þ c11 x2 > > > > : a 2 ¼ c7 þ c8 x s0 ¼ c12 þ c13 x

(10.3.6)

(10.3.7)

Thus, b1, and b2 can be defined as Eqs. (10.3.8)e(10.3.13): 8 > < b1 ¼ c14 ð1 þ xÞ
c15 vð0:5 þ x
tanhð4ðx þ c16 þ c17 vÞÞÞ 1 þ expð0:34ðv
c18 ÞÞ > : b2 ¼ ð
d1 v2 Þexpð
v2 Þ (10.3.8) ( n a þ bðy
1Þ ; y < y0 v2 ¼ (10.3.9) y; y  y0 v þ v0 v0 (   a ¼ y0
y0
1 =n n
1    b ¼ 1= n y0
1 y¼

v0 ¼ c21 þ c22 x þ c23 x2 ( d1 ¼ c24 þ c25 x þ c26 x2 d2 ¼ c27 þ c28 x

(10.3.10)

(10.3.11) (10.3.12) (10.3.13)

where s0 is the backscattering coefficient; v is the windspeed at the 10 m height from the sea surface; q is the radar incident angle; 4 is the angle of wind direction.

10.2.2 ROI extraction method based on potential dark regions In the research of SAR oil spill detection, the dark spot feature area often receives more attention, and other target information is equivalent to false alarm information. For multitemporal SAR images, the large amount of computation caused by the expansion of the time dimension has put enormous pressure on the efficiency of oil spill detection. Therefore, extracting ROI can effectively reduce the computational complexity of

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spatiotemporal dimensions and eliminate false alarm interference to lock in specific ground objects of interest. It has certain research significance and value for subsequent processing, calculation, and oil spill detection.

10.2.2.1 Potential dark area extraction The locking of ROI in wideband images by humans depends on the saliency of the target’s visual representation. In practical applications, SAR images often contain more targets and exhibit multilevel intensity structures. Land, ships, and other targets have higher backscatter intensity, which is more attractive to human vision and autonomous selection compared to seawater and oil films (Shu et al., 2010). Therefore, this chapter proposes an ROI extraction method based on the frequency characteristics of oil spills and oil-like spills in time series, which is based on the potential dark region frequency. Focus on dark areas with low backscatter intensity and use bright areas with high backscatter intensity as background areas. Based on the Otsu threshold segmentation algorithm (Otsu, 1979) to find the optimal threshold, the image is divided into two categories: potential dark area pixels and background pixels, represented by 1 and 0, respectively. In this study, different quarterly images were randomly selected for statistical analysis, and a threshold was uniformly selected as the backscatter intensity threshold. The aim was to extract multiple scene images in batches and to effectively avoid the failure of oil spill recognition caused by imaging conditions, such as high wind speeds, as shown in Fig. 10.11.

10.2.2.2 Potential dark area frequency result extraction The extracted potential dark areas not only include the offshore oil spill targets but also the dark spot areas caused by natural phenomena such as low wind areas, leeward capes, ocean internal waves, etc. Based on the characteristics of potential dark areas, it can be concluded that: (1) The dark spot area caused by oil film is the continuous overflow of leaked oil in a time series, and time repeatability is its most discriminative feature, so it occurs more frequently in local areas; (2) the dark spots caused by natural phenomena are generated due to the influence of natural factors such as wind and atmosphere. This phenomenon usually has strong randomness, so the frequency of appearing in the same area in time series images is much lower than the frequency of oil spills. After that, the potential dark region frequency results are constructed by synthesizing the results of multitemporal SAR images using Eq. (10.3.14):

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

Figure 10.11 Schematic diagram of potential dark area extraction. N P

Pði; jÞ ¼

n¼1

pn ði; jÞ N

(10.3.14)

where N represents the number of multitemporal image data; Pn(i,j) indicates whether the pixel (i,j) in the nth image is a potential dark area using 0 or 1. Finally, based on the frequency results of potential dark regions, high-frequency regions are extracted as the final ROI. In this chapter, we choose 0.5 as the threshold for extracting the ROI, which is considered a random dark region caused by natural phenomena with a low probability of occurrence below 0.5. The maximum distance between the horizontal and vertical directions is selected as the range critical.

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10.2.3 Analysis and comparison of different boundary features The ROI extracted based on the probability results of potential dark regions serves as the final calculation area, reducing the computational complexity of subsequent processing and analysis in the spatial dimension. When extracting and classifying features of targets in the ROI, relying solely on the backscatter intensity can only preliminarily classify ground objects and is easily affected by a small portion of residual interference targets. In terms of obtaining polarization information, the pulse repetition frequency of the dual-polarization system is lower than that of the fully polarization system, and only partial polarization information can be obtained (Li et al., 2014; Cheng et al., 2022). Therefore, in order to expand information abundance, this study comprehensively utilizes the polarization information and texture information of images for information mining, feature extraction, and target classification of oil spill targets.

10.2.3.1 Feature parameter extraction Similar to fully polarization data, polarization decomposition and polarization feature extraction for dual-polarization SAR are based on calculations using a two-dimensional covariance matrix or coherence matrix. Therefore, this article extracts feature parameters based on the two-dimensional covariance matrix generated from Sentinel-1A satellite VV-VH dual polarization data. Based on the dual-polarization feature parameter extraction, the fully polarization feature parameters are extended to the dual-polarization system, and the texture information of the image is integrated. The polarization covariance matrix constructed for a dualpolarization SAR system is defined as Eq. (10.3.15): " # i¼2 X C 11 C 12 li ui uT (10.3.15) CC 2 D ¼ ¼ i C 21 C 22 i¼1 where Cij,i,j˛(1,2) is the element in the scattering matrix, of which the eigenvalue is l1. This chapter is based on the polarization decomposition method to extract the backscatter intensity of diagonal elements, H/A parameters, eigenvalues, H/A combination parameters, self-similarity parameters, and geometric strength parameters extended from fully polarization to dual-polarization data. Among them, the backscatter intensity information of different polarization combinations is calculated to obtain

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

dimensional output due to the nondimensional and small order of magnitude of the backscatter coefficient output during calibration. Texture is also an important attribute used for detecting objects of interest in images. Its concept and definition originate from the expression of the surface properties of textiles and are used to describe the arrangement of textile components. It is a regional feature associated with the size and shape of the region, such as geological rock stripes obtained from aerospace images, tissue textures in medical applications, etc. (Misra and Balaji, 2017). The texture analysis of an image is a description of the spatial distribution pattern of the grayscale levels of image pixels, so different targets can be determined by whether the texture pattern has changed. The gray level cooccurrence matrix is a widely used texture statistical analysis method, represented as the joint probability distribution of two pixels with a distance of d appearing simultaneously in an image (Eq. 10.3.16). pði; j; d; qÞ Pij ¼ PP pði; j; d; qÞ i

(10.3.16)

j

where p represents the probability of the occurrence of paired pixels (i, j) with grayscale values of i and j, respectively, at the distance between d pixels in the direction.

10.2.3.2 Random forest classifier Proposed by Breiman (2001), random forest is a classifier combining bagging integration, classification, regression decision trees, and the concept of randomly selected features. It is a widely recognized supervised learning method integrating multiple classification and regression trees (Breiman, 2001). The content of a random forest mainly includes two parts: The growth of trees and the voting part (Breiman, 2001). Among them, for the growth part of the tree, random forest is composed of many metatree classifiers, which are combined and grown. It follows two randomness rules: randomly selecting training subsets and feature subsets. For the training set data with a sample size of n, n training subsets are randomly extracted from the original sample data using the bootstrap method, and a decision tree is generated accordingly. Unextracted samples are used for internal testing to evaluate the performance of the model, which is called out-of-bag (OBB) data. Its accuracy is called the OBB error rate. The smaller the error rate, the stronger the recognition ability of the model, and vice versa (Liaw and Wiener, 2002; Belgiu and

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Dragut, 2016). The split nodes of each tree are selected according to the minimum Gini coefficient of the randomly selected feature subset and finally constitute the entire random forest. For the voting part, the random forest performs the mode voting method on the prediction results of each tree in the framework, counts the votes obtained by all tags, and takes the category with the highest vote as the output tag, so as to obtain the final classification results of the entire image to be classified. The random forest model evaluates and ranks the importance of the input feature parameters based on the analysis of the OBB error. If the feature has a good ability to distinguish different types of training sample data, it has a high importance score and is considered to be a high-quality parameter with a high contribution to the classification results. Otherwise, it is believed that secondary parameters with low contributions to the classification results can be removed appropriately to improve the classification accuracy and the operational efficiency of the algorithm. Random forest usually screens the input characteristic parameters based on two types of selection methods and quantitatively evaluates their importance: Mean decrease impurity (MDI) and mean decrease accuracy (MDA) (Breiman, 2001). MDI aims to sort the impureness of each feature when training each tree and calculate the amount of weighted impureness of each feature that can reduce the tree. The weighted average of the impure reduction of each feature of the forest is used to get the average decreased impure and sort it. The impure usually includes Gini impure, information entropy, or information gain in the classification. MDA aims to measure the impact of each feature value on the accuracy of the model, using the average difference in accuracy of the variable as the original importance indicator. Therefore, if the feature is replaced with a feature that adds noise and the accuracy of the out-of-pocket data changes significantly, it indicates that the feature presents a highly important contribution to the model and classification, and vice versa (Breiman, 2001). This study uses MDA indicators to evaluate and rank the importance of input variables, ultimately obtaining a combination of advantageous features. The effectiveness of classification algorithms lies in their accuracy and efficiency. With the deep mining of remote sensing data and the interactive use of different types of features, more and more feature parameters are proposed and input for use. However, in practical applications, a large number of input features will lead to multiple increases in random forest computations. Moreover, due to the different contributions of features to different classification problems, the introduction of too many secondary

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

variables with low contributions will weaken the effectiveness of the classification algorithm. Therefore, the importance of scientifically and reasonably analyzing and quantitatively evaluating feature parameters is of great significance for feature screening, constructing effective feature sets, and improving the accuracy of classification algorithms. The classification results of images and the importance ranking of features are based on the overall output results of the image. In order to further explore the contribution of features in images with different oil-water boundary complexities, extract a set of universally advantageous features, and analyze the impact of feature changes on classification accuracy, this study selects universally advantageous feature parameters based on the important parameter results of images with different oil-water boundary complexities. The process is shown in Fig. 10.12. The random forest algorithm and feature importance assessment are completed based on the EnMAP-BOX platform, which is an open source platform for remote sensing data processing

Input characteristic parameters

Medium boundary

Strong boundary

Weak boundary

Optimal parameters Figure 10.12 Schematic diagram of random forest algorithm.

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and analysis developed by Humboldt University in Berlin and the Hermann von Helmholtz Potsdam GFZ based on the IDL platform contract. It integrates the support vector machine, random forest, and other classification or regression processing modules (Van Der Linden et al., 2015; Guanter et al., 2015). This research is based on the random forest module of the EnMAP-BOX platform to realize the classification research on Sentinel-1A time series data in the middle of the Caspian Sea (Fig. 10.13). The MDA method is used to evaluate the importance of random forest input polarization texture feature variables and explore the impact of

Figure 10.13 EnMAP Box platform random forest module implementation process.

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

different feature combination inputs on the classification results according to the feature importance results.

10.3 Experimental area and data source The Caspian Sea is located at the intersection of the Eurasian continent and is the world’s largest inland water body. The Caspian Sea is rich in oil and mineral resources, with a total storage capacity of over 250 billion barrels, second only to the Middle East region (Bayramov and Buchroithner, 2015; Bayramov et al., 2018a, 2018b; Mityagina and Lavrova, 2015; Marina and Olga, 2016). The Caspian Sea developed its main shelf oil field, Oil Rocks. In the late 1940s, it became one of the areas with the most severe oil pollution (Marina and Olga, 2016). Sentinel-1A satellite is an important component of the European Space Agency’s Copernicus Program. It was launched on April 3, 2014, and operates in the C band. It adopts the rightside earth observation mode and has four imaging modes. Users can query and download the data distribution center of ESA based on sensor time range, product type, polarization method, sensor mode, relative orbital number, and ROI range. According to the research background, purpose, and data source acquisition of this study, 29 scenes of Sentinel-1A images covering the Caspian Sea area were selected from February 2017 to January 2018, with the revisit cycle (12 days) of Sentinel-1A data as the step size. The data are standard Level-1 Single Look Complex data in IW mode with a VV-VH polarization composite pattern to ensure that the data images meet the same sensor, detection area range, incident angle, and orbit information conditions.

10.4 Multitemporal dual-polarization oil spill detection results 10.4.1 Radar signal characteristics of oil film under different sea surface wind speed conditions The offshore surface wind speed is generally considered to be the dominant factor affecting the effectiveness of oil spill detection in SAR images. Radar signals have penetrability to the atmosphere, but atmospheric phenomena indirectly affect the signal characteristics in radar images by modulating the distribution of capillary wave and short gravity wave components by changing

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the sea surface wind field, resulting in an uneven distribution of sea surface echo signals in radar images (Marina and Olga, 2016). Therefore, under different wind speed conditions, the radar signal characteristics and oil spill recognition ability of the oil film will exhibit different performances, as shown in Fig. 10.14. When the wind speed is too high, due to the strong action of wind and waves, the roughness of the sea surface is high, and the broken oil film on the sea surface will greatly reduce the damping ability of capillary waves and short gravity waves on the sea surface. As a result, the oil spill area cannot be effectively identified on the radar image because the short gravity waves and capillary waves generated on the sea surface cannot be suppressed (Bayramov et al., 2018a, 2018b; Mityagina and Lavrova, 2015; Marina and Olga, 2016). As shown in Fig. 10.14a, the wind speed is

Figure 10.14 Radar signal performance under different wind speed conditions: (a) High windspeed condition; (b) medium windspeed condition; (c) low windspeed condition.

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

12.9 m/s. Under the boundary layer convection process, the ocean surface presents a complex phenomenon on the SAR image, and the NRCS fluctuation range of the sea surface is large (
13 dB w
20 dB). From the obtained radar images of the central Caspian Sea, it can be seen that there is only a small and fine low backscatter area near the central oil rock, which underestimates the oil spill area. When the sea surface is under low wind speed conditions, the ocean surface in the low wind speed area cannot generate enough capillary waves and short gravity waves. The backscattered echo energy generated under the action of microwave signals is low, presenting a large area of dark feature areas unrelated to oil film on SAR images, increasing the probability of “false alarm” for oil spill detection. As shown in Fig. 10.14b, the wind speed in the low wind area is about 1.3 m/s, and the NRCS in the oil spill area is reduced by about 2e6 dB compared to the background seawater. The dark feature area formed in the low wind area is larger, and the normalized radar scattering cross-section value is similar to the oil spill area, but the boundary is fuzzy and the NRCS is slightly higher than the oil spill area. Therefore, under low wind speed conditions, the area of oil spill pollution based on SAR image detection will be overestimated due to the presence of false alarms. When the sea surface is under moderate wind speed conditions, sufficient capillary waves and short gravity waves are formed, presenting a stable Bragg scattering mechanism. The presence of oil film effectively suppresses the roughness of the sea surface, presenting clear and complete backscatter signal features on SAR images, which is an effective condition for oil spill detection. As shown in Fig. 10.14c, the boundary of the oil film is clear and has a strong contrast with the background seawater. The attenuation range of the oil film on the sea surface (NRCS) is 2e12 dB.

10.4.2 Results and analysis of ROI extraction This study selects 29 Sentinel-1A revisit period images covering the oil rocks area of the Caspian Sea for potential dark region extraction. The data has the same observation conditions, such as orbit, incidence angle, and NESZ. The frequency results of the synthesized dark region based on the 29 scenes data extraction are shown in Fig. 10.15. From the frequency results, it can be seen that the frequency value of the dark region near the Oil Rocks is the highest. In addition, both the nearshore sea area and the boundary area on the right side of the image have dark areas, but the density is relatively low. The density in the lower right corner area is slightly higher than that in the nearshore

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Figure 10.15 Multitemporal dark region frequency results.

leeward headland area. On the one hand, the frequency of low wind areas in this area is higher than that in other areas, resulting in a slightly higher density in the lower right corner area than in the nearshore dark area. On the other hand, the backscattered energy in the far end area at high incidence angles is relatively low, resulting in a dark area characteristic. To further evaluate the effectiveness of extracting oil film regions from ROI, the results of oil film regions extracted from each image were compared with human-machine interaction interpretation results. Among them, oil spill regions were detected in 22 images, while they were only partially detected in seven images. The accuracy and missed detection rate of the extracted oil film areas are shown in Table 10.1. The proposed method achieves detection rates higher than 85% in six of the images. The image taken on September 21st has the lowest detection rate at 74.3% due to its relatively low proportion of the largest area within the ROI. In summary, the oil spill ROI extraction method based on multitemporal latent dark regions can effectively extract the oil spill region. The vast majority of images within the year were extracted, with only a few images showing missed detections.

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

Table 10.1 Accuracies and missing detection rates of ROI in different SAR images.

Date of the images

Accuracy

Missing detection rate

April, 30 June, 29 July, 23 August, 16 September, 9 September, 21 January, 19

93.4% 89.6% 86.8% 87% 88.9% 74.3% 85.7%

6.6% 10.4% 13.2% 13% 11.1% 25.7% 14.3%

10.4.3 Comparison results of dominant features of different boundaries 10.4.3.1 Feature parameter screening and importance analysis This article evaluates the importance of features in three different boundary conditions of oil spill images and qualitatively divides them into three types of oil-water boundaries: (1) Areas with obvious oil-water boundaries and relatively low seawater background noise; (2) areas with obvious oil and water areas but slightly blurry boundaries for medium boundaries images, such as the end of oil spots; (3) the area with fuzzy oil-water boundary and many small oil zones, such as the edge of a small deep-fried dough stick belt. Based on the ranking of feature importance for three types of boundaries, the aim is to find advantageous feature parameters that always have high importance, stability, and universality under different conditions. The results are shown in Fig. 10.16. Overall, for the three types of oil-water boundaries, the first six feature parameters are the same, which are l1, variance, mean, l2, C22, and U. The top six features have a stable contribution in image classification results with different complexities and are advantageous features that can balance the complexity of different oil-water boundaries. The feature parameters in the three types of boundaries all present a hierarchical distribution. The top 10 feature parameters in strong boundary images can be divided into three echelons, with the first echelon consisting of the first three features,

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Figure 10.16 Feature importance sorting of images with different boundary complexities (x-axis indicates the characteristic parameter and y-axis indicate the score of importance): (a) Strong boundary; (b) medium boundary; (c) weak boundary.

including l1, variance, and mean, which are significantly more important than other features. The fourth to seventh ranking is the second tier, and the subsequent features belong to the third-tier team with low contributions. The top 10 feature parameters in medium boundary images can be divided into four tiers, with the first tier being the features with high significance and important scores in the top two, including l1 and variance. The second tier consists of the third and fourth features, which are significantly lower than the first tier, including C22 and mean. The third tier is composed of features from the fifth to seventh positions, and the subsequent features with lower contributions are in the fourth tier. The feature parameters in weak boundary images can be divided into three echelons, and the importance of the first four features is significantly higher than that of

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

subsequent features, including mean, l1, variance, l2. The fifth to seventh positions are in the second tier. The subsequent feature is the third tier, with a lower contribution. To explore the impact of different numbers of feature variable inputs on classification accuracy, low contribution features are removed and the best feature combination. This study conducts classification experiments on 23 different feature sets with a step size of 1. The criteria for selecting each set of feature sets are: (1) When there are duplicate features in the same sorting position, select the duplicate features under three types of boundary conditions as the final features of the sorting position, and the features that are not selected will be selected in the next order; (2) When there are no duplicate features in the same sorting position, the features with weak boundaries in that sorting are selected as the final features to take into account the most complex oil-water boundary situation. The classification accuracy results of images with different input quantities are shown in Fig. 10.17. This study focuses on the extraction of oil film, and therefore combines the overall accuracy and oil film accuracy results to analyze the impact of input feature quantity on classification accuracy. From the results, it

Figure 10.17 Feature importance sorting of images with different boundary complexities.

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can be seen that classification accuracy varies with the number of input features. The classification accuracy shows an upward trend with the increase of the number of features. When the number of input features is 6, the feature parameters have stable contributions at different boundaries, and the accuracy begins to show a gentle trend. When the number of input features is 12, the accuracy of the oil film reaches its maximum, and then shows unstable changes as the number increases. This is because the added features contribute more to the recognition of other targets than the oil film. Therefore, based on the overall accuracy and oil film accuracy, 12 features were ultimately selected as the final feature set for subsequent research.

10.4.3.2 Precision evaluation and analysis of oil spill detection based on random forest In order to verify the accuracy of the proposed method for detecting oil spills in the Caspian Sea under time series, this study, based on human-computer interaction and visual interpretation, drew on data and conclusions from previous literature within the same research scope of the Caspian Sea, including individual cases within the same research area and time range (Bayramov et al., 2018b). Four quarters of the images were selected for classification, namely February 5th, May 24th, September 9th, and December 26th, 2017. The accuracy of classification results is shown in Table 10.2. The overall accuracies of the four scene images are 89.74%, 87.8%, 93.36%, and 92.07%, respectively, with Kappa coefficients of 0.8225, 0.8052, 0.89, and 0.881. Among them, producer accuracy (PA) corresponds to the missed detection rate of the associated target. The higher the PA value, the lower the missed detection rate. The user’s accuracy (UA)

Table 10.2 Classification accuracy results of images in different seasons and phases.

Feb. 5th

May 24th

Sept. 9th

Dec. 26th

Target

PA/%

UA/%

PA/%

UA/%

PA/%

UA/%

PA/%

UA/%

Oil film Oil rock Seawater Kappa OA/%

86.79 85.82 92.33

93.87 84.70 89.93

77.53 96.7 90.23

98.04 88.28 80.39

94.64 89.45 94.53

94.4 92.84 87.14

83.97 95.28 97.11

98.83 97.51 82.44

0.8225 89.74

0.8052 87.8

0.89 93.36

0.881 92.07

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corresponds to the false alarm level of the associated target, and the higher the UA value, the lower the false alarm rate. The results show that the classification results of this method are good, with only the PA of the oil film and UA of the seawater in the image on May 24th being lower. This is because the sea surface conditions were complex when the image was obtained, and the seawater in the high wind speed area showed an abnormally bright phenomenon, causing confusion and misclassification between some Oil Rock areas and seawater areas. This study focuses on the extraction of offshore oil films, and the overall classification accuracy of oil films is good. The average PA of oil films is about 84.4%, and the average UA is about 96.2%. The PA results are only slightly lower in complex sea conditions, with the largest oil spill near the Oil Rocks. The classification results of the oil spill detection method based on random forest and taking into account the dominant features under different complex boundaries are good, which can be used for subsequent related research.

10.4.4 Spatial distribution and temporal change of the oil spills in multi-temporal dualpolarization SAR images In order to further quantify the spatial distribution and annual temporal changes of oil slicks in the central Caspian Sea region, oil spill extraction was performed on Sentinel-1A’s annual temporal images based on the method proposed in this paper. The images were obtained under imaging geometric conditions such as the same region, incidence angle, and orbit information. Fig. 10.18 depicts the spatial distribution of oil slicks in the time series of the sea surface. The results indicate that the two most severely polluted areas in the central Caspian Sea are the oilrock producing areas and the Chilov Islands, with the Oil Rocks area having the largest oil spill. This is because natural hydrocarbon leakage from the seabed in the Oil Rocks-producing areas of the Caspian Sea is the main source of oil spills. The maximum oil spill distance from north to south in the overall oil spill area is 64.7 km, and the maximum oil spill distance from east to west is 51.5 km. Fig. 10.19 shows the monthly average oil spill area in the central Caspian Sea during the year. Overall, the oil spill area is mostly distributed in the range of 200e500 km2. The oil patch area from April to September is relatively large, exceeding 300 km2. Among them, the average oil spill area in June reached the highest, exceeding 800 km2, while May was significantly lower than other

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Figure 10.18 Spatial distribution of multi temporal oil spill detection results.

months. This is because the data obtained were obtained under strong atmospheric conditions, with average sea surface wind speeds of 17.5 m/s and 12.5 m/s, respectively, during image acquisition. The average monthly oil spill area during the cold season is less than 200 km2, and the average wind speed is about 7.5 m/s. In addition, based on the time scale of annual changes, this article analyzes the temporal changes of oil spills in the central Caspian Sea based on the annual and monthly average results. The results show that the oil spill area in the central Caspian Sea showed seasonal changes during the study period, with an overall trend of “increasing in spring and summer and decreasing in autumn and winter.” In order to further explore the effectiveness of the results of this article, a comparison was made between the historical data obtained from previous studies based on

Chapter 10 Oil spill extraction in spaceborne dual-polarization SAR image

Figure 10.19 Annual and monthly changes in the oil spill area in the central Caspian sea.

multisource remote sensing images of oil spill distribution information in the same research area (Marina and Olga, 2016) and the extracted results of this article. The results showed that the overall trend of oil spill area changes within the year showed good consistency, with the monthly average values of oil spill area in spring and summer being higher than those in autumn and winter. Due to the relatively low average wind speed during the spring and summer seasons, which is conducive to the detection and extraction of marine oil films, the monthly average value of oil film area during the spring and summer seasons is relatively high, with only a few data points showing differences due to atmospheric influence during acquisition. During the cold months, the amount of oil spills is relatively low due to the relatively complex atmospheric processes and high average wind speeds, resulting in a relatively low monthly average of oil film area.

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References Bayramov, E., Buchroithner, M., 2015. Detection of oil spill frequency and leak sources around the Oil Rocks Settlement, Chilov and Pirallahi Islands in the Caspian Sea using multi-temporal envisat radar satellite images 2009e2010. Environ. Earth Sci. 73 (7), 3611e3621. Bayramov, E., Knee, K., Kada, M., Buchroithner, M., 2018a. Using multiple satellite observations to quantitatively assess and model oil pollution and predict risks and consequences to shoreline from oil platforms in the Caspian Sea. Hum. Ecol. Risk Assess. 24 (6), 1501e1514. Bayramov, E., Kada, M., Buchroithner, M.F., 2018b. Monitoring oil spill hotspots, contamination probability modelling and assessment of coastal impacts in the Caspian Sea using SENTINEL-1, LANDSAT-8, RADARSAT, ENVISAT and ERS satellite sensors. J. Oper. Ocean. 11 (1), 27e43. Belgiu, M., Dragut, L., 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS J. Photogrammetry Remote Sens. 114 (114), 24e31. Breiman, L., 2001. Random forests. Mach. Learn. 45 (1), 5e32. Cheng, L., Li, Y., Zhang, X., Xie, M., 2022. An analysis of the optimal features for Sentinel-1 oil spill datasets based on an improved JeM/K-Means algorithm. Rem. Sens. 14 (17), 4290. Guanter, L., Kaufmann, H., Segl, K., Foerster, S., Sang, B., 2015. The EnMAP spaceborne imaging spectroscopy mission for earth observation. Rem. Sens. 7 (7), 8830e8857. Hassani, B., Sahebi, M.R., Asiyab, R.M., 2020. Oil spill four-class classification using UAVSAR polarimetric data. Ocean Sci. J. 55, 433e443. Hersbach, H., Stoffelen, A., De Haan, S., 2007. An improved C-band scatterometer ocean geophysical model function: CMOD5. J. Geophys. Res. 112 (3), 006. Li, G., Li, Y., Liu, B., Hou, Y., Fan, J., 2018. Analysis of scattering properties of continuous slow-release slicks on the sea surface based on polarimetric synthetic aperture radar. ISPRS Int. J. Geo-Inf. 7 (7), 237. Li, H., Perrie, W., He, Y., Wu, J., Luo, X., 2014. Analysis of the polarimetric SAR scattering properties of oil-covered waters. IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens. 8 (8), 3751e3759. Liaw, A., Wiener, M., 2002. Classification and regression by randomForest. R. News 2 (3), 18e22. Liu, P., Li, X., Qu, J.J., Wang, W., Zhao, C., Pichel, W., 2011. Oil spill detection with fully polarimetric UAVSAR data. Mar. Pollut. Bull. 62, 2611e2618. Liu, P., 2012. Research on ocean oil spill detection and recognition using SAR data. In: Doctoral Dissertation. Ocean University of China (in Chinese). Marina, M., Olga, L., 2016. Satellite survey of inner seas: oil pollution in the black and Caspian seas. Rem. Sens. 8 (10), 875. Minchew, B., Jones, C.E., Holt, B., 2012. Polarimetric analysis of backscatter from the Deepwater Horizon oil spill using L-band synthetic aperture radar. IEEE Trans. Geosci. Rem. Sens. 50 (10), 3812e3830. 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. J. Indian Soc. Remote Sens. 45 (6), 1065e1075. Mityagina, M.I., Lavrova, O.Y., 2015. Multi-sensor satellite survey of surface oil pollution in the Caspian Sea. In: Paper Presented in the Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions, p. 96380Q. Toulouse, France.

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Nezhad, M.M., Groppi, D., Marzialetti, P., Fusilli, L., Laneve, G., Cumo, F., Garcia, D.A., 2019. Wind energy potential analysis using Sentinel-1 satellite: a review and a case study on Mediterranean islands. Renew. Sustain. Eng. Rev. 109, 499e513. Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE Trans. on Syst. Man Cybern. 9 (1), 62e66. Ozigis, M.S., Kaduk, J., Jarvis, C., 2018. Synergistic application of Sentinel 1 and Sentinel 2 derivatives for terrestrial oil spill impact mapping. In: Paper Presented in the Proceedings of the Active and Passive Microwave Remote Sensing for Environmental Monitoring II. Berlin, Germany. Quilfen, Y., Chapron, B., Elfouhaily, T., Katsaros, K., Tournadre, J., 1998. Observation of tropical cyclones by high-resolution scatterometry. J. Geophys. Res. 103 (4), 7767e7786. Shu, Y., Li, J., Yousif, H., Gomes, G., 2010. Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring. Remote Sens. Environ. 114 (9), 2026e2035. Skrunes, S., Brekke, C., Eltoft, T., 2014. Characterization of marine surface slicks by Radarsat-2 multipolarization features. IEEE Trans. Geosci. Rem. Sens. 52 (9), 5302e5319. Stoffelen, A., Anderson, D., 1997. Scatterometer data interpretation: estimation and validation of the transfer function CMOD4. J. Geophys. Res. 102 (3), 5767e5780. Van Der Linden, S., Rabe, A., Held, M., Jakimow, B., Leitão, P.J., Okujeni, A., Schwieder, M., Suess, S., Hostert, P., 2015. The EnMAP-Box-A toolbox and application programming interface for EnMAP data processing. Rem. Sens. 7 (9), 11249e11266. Wismann, V., Gade, M., Alpers, W., Huhnerfuss, H., 1998. Radar signatures of marine mineral oil spills measured by an airborne multi-frequency radar. Int. J. Rem. Sens. 19 (18), 3607e3623. Xie, M., Li, Y., Cao, K., 2020. Global cyclone and anticyclone detection model based on remotely sensed wind field and deep learning. Rem. Sens. 12 (19), 3111. Xie, M., Dong, S., Li, Y., 2022. A deep-learning-based fusion approach for global cyclone detection using multiple remote sensing data. IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens. 15, 9613e9622.

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11 Case study: tracing illegal oil discharge from ships 11.1 Illegal oil discharge from ships Since the 1970s in the MARPOL 73/78 Annex I, the International Maritime Organization (IMO) has been deploying prevention and control measures for oil spills, one of the important forms of marine pollution (Martínez and Moreno, 1996). The sources of marine oil spills mainly include natural seepage, platforms or ship accidents, and artificial discharges (Su et al., 2019). Natural seepage is easier to spot because of its frequent occurrence in a specific sea area. Oil spills resulting from a platform or ship accidents can also be monitored and handled because of the accident alarms and the specific location of occurrence. Although oil-containing wastewater from vessels can be received and treated at port reception facilities, some vessels choose to discharge illegally in waters far from ports because of the cost of wastewater treatment. Studies have shown that the risk of being fined for illegal discharges is lower than the cost of operating in accordance with regulations, and the likelihood of being detected, especially at night, is almost nil (Busler et al., 2015). The amount of waste oil discharged by ships exceeds the number of oil spills caused by platform or ship accidents and has become the largest source of marine oil spill pollution (Ferraro et al., 2009). Although there are many international and domestic regulations, the illegal discharges of waste oil from ships still exist, which is one of the important threats to the marine environment (Ivanov and Kucheiko, 2014). Remote sensing technology is one of the most effective methods for detecting oil pollution on the sea surface. Although the use of aircraft and vessels for inspecting illegal discharge is relatively flexible, covering the whole area is time-consuming because of the small observation range. Furthermore, the inspection and monitoring via aircraft and vessels are mainly based on visible light or infrared sensors, which are incapable of detecting illegal discharge at night or under foggy conditions due to Oil Spill Detection, Identification, and Tracing. https://doi.org/10.1016/B978-0-443-13778-5.00012-6 Copyright © 2024 Elsevier Inc. All rights reserved.

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limitations by weather conditions and distance (Gauthier et al., 2007). Spaceborne synthetic aperture radar (SAR) remote sensing has a broad observation range and the ability to overcome the influence of adverse conditions such as rainy weather and is, thus, capable of performing all-weather round-the-clock illegal discharge inspection, making it the most effective monitoring technique for marine oil spills at all times (Fingas and Brown, 2014, 2017). In view of the spatial distribution and frequency of oil spills, as well as the pollution probability, multisource sensors covering optical and SAR systems are used to classify the coverage of the oil spill in the middle of the Caspian Sea. Based on the classification result, Bayramov et al. build a risk model of oil spills on water quality and coastline ecosystems (Bayramov et al., 2018a). Researchers (Bayramov et al., 2018b) used Sentinel-1A, Radarsat, ENVISAT, and other multisource SAR images to detect continuous oil spills near Azerbaijan from 1996 to 2017. They combined SAR images with auxiliary data such as wind and current and used visual interpretation to distinguish oil spills and lookalikes, and finally output pollution probability heat maps of sea areas and shorelines. Shao et al. (2008) used multitemporal SAR images and microwave scatterometer (QuikSCAT and ASCAT) to detect and analyze the changes and trends of the “Hebei Spirit” oil spill, further proving the capability and effectiveness of microwave remote sensing data in oil spill monitoring and forecasting. Based on two sets of Sentinel-1A/B images of natural leaking oil films from 2017 to 2018, Ivanov and Morovic (2020) comprehensively analyzed the temporal changes of oil slicks on different sea surfaces. However, there are still relatively few studies on the detection of small-scale oil pollution such as waste oil discharged from ships on the sea (Busler et al., 2015; Ivanov and Kucheiko, 2014). The most difficult issue with illegal discharge detection and tracing is the association of the oil spill with the vessel involved in the incident. Depending on the nature of the oil, the discharged oil slick from vessels can stay on the sea surface for 2e12 h (Busler et al., 2015). The complexity of ship traffic and the drifting and diffusion of the oil spill makes it extremely challenging to trace the vessel using spaceborne SAR alone. It is difficult to obtain information on the nature of a spill from SAR satellite images but spills from vessels often appear as long, linear dark lines (indicating a substance discharging as the vessel is moving), with a bright spot (the vessel) at the tip. The automatic identification system (AIS) of vessels can provide information about the navigation time, location, and vessel information, thereby facilitating the tracing of the vessel involved in the oil spill (Wright et al., 2019). The vessel detected in a satellite image could be determined by

Chapter 11 Case study: tracing illegal oil discharge from ships

correlating the SAR data with vessel position reports. To hold the vessel in question accountable, in addition to AIS and remotesensing monitoring, it is necessary to obtain more concrete evidence (e.g., tampering tools, navigation records, and other evidence obtained from the on-site investigation) to finally determine the vessel involved in the illegal waste oil discharge (Busler et al., 2015). By integrating the SAR, AIS, and other data, some agencies have developed operational systems. In European waters, EMSA (European Maritime Safety Agency) is operating the SafeSeaNet and CleanSeaNet systems to provide the current positions of all ships and oil spill monitoring information in and around EU waters (Yang et al., 2013). The CleanSeaNet system has been running since 2007. In Japan, IHI Corporation and IJS (IHI Jet Service) developed a semiautomatic operational system, iOMS (IHI Ocean Monitoring Service), for illegal discharge. The SAR and AIS are utilized for ship detection and identification in some recent studies (Mizukoshi et al., 2020; Liu et al., 2021). The geographic factors related to oil pollution are the sea areas where major shipping routes pass through and the ones by nearshore facilities (Solberg et al., 1999, 2003, 2007). The oil spill risk assessment of China’s Bohai Sea shows that the most significant risk source in this sea area lies in the ports and vessels along the coast (Liu et al., 2015). The remote sensing monitoring data obtained from this sea area for the past 10 years also show that the distribution of oil spills in the Bohai Sea and the shipping routes largely overlap, while the relationship between the platform locations and oil spill distribution is relatively weak (Ding et al., 2016; Bing et al., 2019). In National Oil Spill Emergency Response Scheme, the Chinese government specified the need to build a remote-sensing (satellites and shore-based/shipborne platforms) oil spill monitoring system. The focus is to establish maritime surveillance, and enhance manned and unmanned aerial vehicle remote-sensing systems. The aim is to achieve full coverage of the waters by monitoring four oil spill high-risk areas (the Bohai SeaeNorth Yellow Sea, the Yangtze estuaryeNingbo Zhoushan Port, the Taiwan StraitePearl River Estuary, and the Qiongzhou StraiteBeibu Gulf) (Xiong et al., 2015). However, the current monitoring technique of vessel pollution in these sea areas is obsolete. An all-weather, round-the-clock threedimensional monitoring system for vessel pollution for the sea areas under the administration of Liaoning Province has not yet been established. This leads to the inability to discover vessel pollution accidents on time and affects the timeliness of emergency actions. In this chapter, the technical ideas and an example of using AIS and SAR to carry out sea surface oil spill surveillance

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are illustrated. We used a case of illegal discharge of oily sewage from a ship in the Bohai Sea, China, and give some suggestions for the construction of the system in the future.

11.2 Oil spill detection and look-a-like elimination from SAR images Liaodong Bay is located in the north of Bohai Sea, China. It is the location of the third largest oil and gas field in China and is rich in oil and gas resources. There are many oil drilling platforms, oil pipelines and other facilities in this sea area. The main ports along the coast of Liaodong Bay include Panjin Port, Yingkou port, and Huludao port. This sea area is one of the areas with the densest routes for oil tankers and other ships in the world. In recent years, with the increase in oil exploitation, the risk of oil spill caused by platforms, oil pipelines, and ships has gradually increased. The area studied in this paper is southeast of Liaodong Bay, on the route between Yingkou port and Huludao port from Dalian, Yantai, and other ports (Fig. 11.1).

Figure 11.1 Study area: Liaodong Bay. Reuse from Liu et al. (2021) with permission.

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Using AIS information, remote sensing monitoring results, simulation results of oil spill drift and diffusion, ship navigation records, and other information, an illegal discharge in Bohai Bay, China was tracked. The remote sensing data used for oil pollution detection came from Radarsat-2, a high-resolution commercial radar satellite equipped with C-band sensors, which was launched on December 14, 2007. Radarsat-2 data is a SAR satellite data widely used in oil spill monitoring. It has the ability of all-weather and all-time earth observation. The polarization mode of the data used is VV, the incident angle is 42.1205 degrees, and the spatial range covers most of the sea area of Liaodong Bay. The data of wind field and flow fields in the oil spill discovery area were obtained from the European Center for medium range weather forecasts (ECMWF) Website (https://www. ecmwf.int/), and the website of Japan Meteorological Administration (http://www.jma.go.jp/), and used for oil spill trajectory and oil spill diffusion simulation. The oil pollution on the sea surface was shown as a dark area in SAR image. According to the different sources, oil pollution may be shown as a strip or block shape. Whatever the shape is, its boundary will be clearer. On the SAR image used in this study, totally five dark areas are found (Fig. 11.2). By analyzing the spatial position, time phase, and geometric features of region (a)e(e), it is found that the dark features of a region also appear many times on Sentinel-1 data, and its

Figure 11.2 Look-alikes (a)e(e) in the study region. The gray images are the raw SAR images; the color images are image of significants that indicates the differences between the targets and the backgrounds. Reuse from Liu et al. (2021) with permission. For interpretation of the references to color in this figure legend, please refer online version of this title.

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geometric texture presents the characteristics of interval strips. Regions (b) and (d) have the characteristics of large areas and fuzzy boundary. The two areas are close to the land, and the average wind direction in this time period is northeast, that is, from the land to the sea. The existence of land makes the wind speed in this area low, forming a low wind speed area. Region (c) is a long strip away from land with a clear boundary. Compared with other temporal remote sensing images, it is found that such targets do not have the characteristics of multiple occurrences, which is in line with the characteristics of sea oil pollution. Region (e) is located near the estuary of the river and has an irregular shape. It is found from multiple remote sensing images that the dark area exists for a long time, which may be caused by estuarine alluvium. After a comprehensive evaluation, region (c) may be the sea surface oil spill strip caused by ships or platforms. The evaluation results were reported to Liaoning Maritime Safety Administration, which is in charge of oil spill supervision in the sea area. According to the query, the oil platform closest to the oil belt is located northwest of the oil belt, with a distance of 33.87 km (Fig. 11.3). The platform is far away from the oil belt. If the oil is caused by the leakage of the platform, the oil will gradually diffuse in the drift process, the boundary will become blurred and broken under the action of wind and waves, forming a discontinuous oil belt. By analyzing the shape of the oil belt, the width of the oil belt increases from point a to point b, and the boundary of the oil belt was clear, which belonged to the case of incomplete diffusion. Therefore, it is estimated that it was not the oil pollution caused by the recent platform leakage. In the process of rising from the seafloor to the sea surface, the natural leakage oil on the seafloor was affected by the current, and its position on the sea surface

Figure 11.3 Relative position of oil belt to platform and ships. In the figure. Point A and B are the two ends of the oil belt. The width of oil belt section I, II, and III is 345 m, 316, and 836 m respectively. Reuse from Liu et al. (2021) with permission.

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would not be fixed, and the leakage oil would fully diffuse to form a thin large area of oil film. This feature is inconsistent with the geometric shape of the oil found, so the possibility of oil film caused by natural leakage was eliminated. The oil belt was located on the ship route, and the extension direction of the oil belt was parallel to the ship route in the sea area. It was speculated that the oil belt is caused by the ship.

11.3 Tracing the source of spills using AIS In order to confirm this result, this paper simulates the diffusion range and trajectory of oil pollution in combination with the wind field and flow field data at the time when the oil pollution is found (Fig. 11.4). Assuming that the location of the oil spill point is between Point A and Point B, the time of oil spill leakage is 1 h. It can be found that the simulated oil spill trajectory extends from northeast to southwest. This feature further eliminates the possibility of oil platform leakage. Similarly, based on the simulation results of oil spill drift and diffusion, it was found that the results of oil spill drift path were consistent with the extension direction of oil belt detected on SAR image, and the diffusion range of oil spill simulation was larger than that detected by SAR. This indicated that the oil belt should leak during the process from Point B to Point A, and the leakage time is likely to be less than 1 h. Therefore, it is necessary to focus on the investigation of ships sailing from south to north in the past 1 h. In addition, the continuity of the oil belt in Fig. 11.3 with no

(a)

(b)

Figure 11.4 The predicted oil spill drift path (a) and drift path (b) of the simulated oil spill point are 1 h. Reuse from Liu et al. (2021) with permission.

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interruption in the middle or abnormal width of the oil belt indicates that the oil belt was not disturbed by the wake of other ships. The focus of the investigation is on the ships passing through this sea area recently. Considering the possibility of continuous emission of pollutants during ship navigation, the ship position at three typical times is selected to predict the sea surface oil spill drift, which is compared with the position of the suspected pollution zone, so as to determine the accident ship (Fig. 11.5). In order to confirm the vessel causing the accident, the staff of the competent maritime department need to board the ship for verification. After the ship docked at Huludao port, senior maritime investigators, PSC prosecutors with rich supervision experience, and law enforcement officers who had served as ocean captains and chief engineer launched a comprehensive investigation on the ship. Traces of cargo residues and cargo washing water dumping on the main deck were found in many areas of the ship’s deck (Fig. 11.6a). A recently used submersible pump and a bundle of plastic pipes were found in the deck workshop (Fig. 11.6b). In addition, the ship did not record the discharge and treatment of cargo tank washing water during the voyage in the garbage record book or other documents, but the relevant log on board recorded that the ship carried out cargo tank washing and cleaning of cargo tank sewage well during the voyage

Figure 11.5 AIS location data.

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Figure 11.6 (a) sewage traces found during boarding inspection and (b) used sewage equipment. Reuse from Liu et al. (2021) with permission.

(including after entering the Bohai Sea), which indicates that the ship had carried out tank washing and discharging tank washing water during the voyage. Thus, the illegal discharge vessel was tracked. The facts obtained through the observation and investigation form a chain of evidence, indicating that the suspect ship discharged the tank washing water containing cargo residues into the Bohai Sea. According to the provisions of Annex V of the International Convention for the Prevention of Pollution from Ships, it is prohibited to discharge tank washing water containing cargo residues within 12 nautical miles from the nearest land (territorial sea baseline). The Bohai Sea is China’s internal water (waters within the territorial sea baseline), so the discharge of tank washing water containing cargo residues in the Bohai Sea is illegal. It is the first time to realize the identification and tracking of the ships discharging oily sewage by the integrated application SAR and AIS.

References Bayramov, E., Knee, K., Kada, M., Buchroithner, M., 2018a. Using multiple satellite observations to quantitatively assess and model oil pollution and predict risks and consequences to shoreline from oil platforms in the Caspian Sea. Hum. Ecol. Risk Assess. 24, 1501e1514. Bayramov, E., Kada, M., Buchroithner, M., 2018b. Monitoring oil spill hotspots, contamination probability modelling and assessment of coastal impacts in the Caspian Sea using SENTINEL-1, LANDSAT-8, RADARSAT, ENVISAT and ERS satellite sensors. J. Oper. Oceanogr. 11, 27e43.

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Bing, L., Xing, Q.-G., Liu, X., Zou, N.-N., 2019. Spatial distribution characteristics of oil spills in the Bohai Sea based on satellite remote sensing and GIS. J. Coast Res. 90, 164. Busler, J., Wehn, H., Woodhouse, L., 2015. Tracking vessels to illegal pollutant discharges using multisource vessel information. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 40, 927e932. Ding, Y., Cao, C., Huang, J., Song, Y., Liu, G., Wu, L., Wan, Z., 2016. Origins and features of oil slicks in the Bohai Sea detected from satellite SAR images. Mar. Pollut. Bull. 106, 149e154. Ferraro, G., Meyer-Roux, S., Muellenhoff, O., Pavliha, M., Svetak, J., Tarchi, D., Topouzelis, K., 2009. Long term monitoring of oil spills in European seas. Int. J. Rem. Sens. 30, 627e645. Fingas, M., Brown, C., 2014. Review of oil spill remote sensing. Mar. Pollut. Bull. 83, 9e23. Fingas, M., Brown, C., 2017. A review of oil spill remote sensing. Sensors 18, 91. Gauthier, M., Weir, L., Ou, Z., Arkett, M., De Abreu, R., 2007. Integrated satellite tracking of pollution: a new operational program. In: Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, pp. 967e970. Ivanov, A.Y., Kucheiko, A.A., 2014. Large discharges from ships in the Black Sea studied by synthetic aperture radar with the support of automated identification systems. Int. J. Rem. Sens. 35, 5513e5526. Ivanov, A.Y., Morovic, M., 2020. Oil seeps detection and mapping by SAR imagery in the adriatic sea. Acta Adriat. 61, 13e26. Liu, B., Zhang, W., Han, J., Li, Y., 2021. Tracing illegal oil discharges from vessels using SAR and AIS in Bohai Sea of China. Ocean Coast Manag. 211, 105763. Liu, X., Meng, R., Xing, Q., Lou, M., Chao, H., Bing, L., 2015. Assessing oil spill risk in the Chinese Bohai Sea: a case study for both ship and platform related oil spills. Ocean Coast Manag. 108, 140e146. Martínez, A., Moreno, V., 1996. An oil spill monitoring system based on SAR images. Spill Sci. Technol. Bull. 3, 65e71. Mizukoshi, N., Watanabe, T., Ouchi, K., 2020. Operational system for ship detection and identification using SAR and AIS for ships of illegal oil discharge. IEICE Tech. Rep. 119, 45e50. Shao, Y., Tian, W., Wang, S., Zhang, F., 2008. Oil spill monitoring using multitemporal SAR and microwave scatterometer data. Int. Geosci. Remote Sens. Symp. 3, 10e14. Solberg, A.H.S., Brekke, C., Husoy, P.O., 2007. Oil spill detection in Radarsat and envisat SAR images. IEEE Trans. Geosci. Rem. Sens. 45 (3), 746e755. Solberg, A.H.S., Dokken, S.T., Solberg, R., 2003. Automatic detection of oil spills in ENVISAT, Radarsat and ERS SAR images. In: 2003 IEEE International Geoscience and Remote Sensing Symposium, pp. 2747e2749. Proceedings. Solberg, A.H.S., Storvik, G., Solberg, R., Volden, E., 1999. Automatic detection of oil spills in ERS SAR images. IEEE Trans. Geosci. Rem. Sens. 37 (4), 1916e1924. Su, D.T., Tzu, F.M., Cheng, C.H., 2019. Investigation of oil spills from oil tankers through grey theory: events from 1974 to 2016. J. Mar. Sci. Eng. 7, 1e14. Wright, D., Janzen, C., Bochenek, R., Austin, J., Page, E., 2019. Marine observing applications using AIS: automatic identification system. Front. Mar. Sci. 6, 1e7.

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Xiong, S., Long, H., Tang, G., Wan, J., Li, H., 2015. The management in response to marine oil spill from ships in China: a systematic review. Mar. Pollut. Bull. 96, 7e17. Yang, C.-S., Kim, T.-H., Hong, D., Ahn, H.-W., 2013. Design of integrated ship monitoring system using SAR, RADAR, and AIS. In: Hou, W., Arnone, R. (Eds.), Proceedings of the Proceedings of SPIE Ocean Sensing and Monitoring V. SPIE, Baltimore, MD, p. 872411.

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12 Case study: remotely monitoring oil storage facilities With the sustained and rapid development of the national economy, China’s economy is growing year by year, the amount of oil imports is gradually increasing, and the degree of external dependence is increasing. The state and various relevant enterprises have prepared to build oil reserve bases to deal with emergencies and prevent oil supply risks. However, petroleum-related products are flammable, reactive, and toxic. If pipelines are broken, equipment is damaged or reactors and pressure vessels explode, a large number of flammable, explosive, and toxic substances will be released instantly. The leakage and diffusion of toxic substances may cause a wide range of poisoning and environmental pollution. The leakage of flammable and explosive substances may encounter ignition sources, which may cause unimaginable fire and explosion disasters. In particular, most of China’s oil transportation depends on sea transportation. Therefore, in order to facilitate offshore oil transportation, most oil storage bases are established near major ports. Once a large amount of oil spills enters the ocean, it will cause serious damage to the marine ecological environment. The greater the oil reserves, the greater the potential harm. Therefore, it is very important to reasonably estimate the oil reserves in the storage tank area, evaluate the risk level according to the reserves and take corresponding preventive measures to prevent the occurrence of oil spill accidents. The estimation of oil reserves can be obtained by counting the number of oil tanks in the oil reserve bases of various countries. In the oil storage base, the main storage mode of oil is oil tank. The volume of oil tank can be calculated to estimate the amount of oil stored in the oil storage base. At present, in the process of strategic oil reserve, the main container and measuring instrument for oil is vertical metal tank (hereinafter referred to as vertical tank). In order to obtain the volume of the whole oil tank, the main method is to measure the volume of the vertical tank by geometric measurement combined with photoelectric

Oil Spill Detection, Identification, and Tracing. https://doi.org/10.1016/B978-0-443-13778-5.00017-5 Copyright © 2024 Elsevier Inc. All rights reserved.

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ranging. Its core idea is to regard the vertical tank as an ideal cylinder, and then establish the cylindrical geometric model and calculate its capacity by measuring the diameter of each layer of ring plate. In the specific actual measurement process, it is time-consuming and laborious and is limited to the measurement of oil tanks in a certain area. However, it is very difficult to obtain the oil reserves in time and conveniently through the traditional measurement methods. Therefore, we need a method to meet the global detection of oil reserves around the world, so as to obtain the volume of vertical tanks quickly and conveniently. In recent years, with the continuous development and improvement of satellite remote sensing technology in China, high-resolution remote sensing satellites can easily obtain highresolution image information of oil storage tanks (Wang et al., 2019). At present, it has become a simple and important method to obtain information about buildings in the three-dimensional world from the two-dimensional plane image and fully use the shadow of buildings in the remote sensing image to obtain the height of buildings in the three-dimensional world (Irvin and McKeown, 1989). After the tanks are detected, the radius of the tank is obtained, the shadow length of the tank in the high score image is detected, and the height of the tank is calculated through the geometric relationship. Finally, the capacity of the tank can be obtained according to the cylindrical volume formula.

12.1 Inversion method for the height of oil tank The top of the oil tank is shown as a circle in the twodimensional plane, so the detection of the oil tank can be regarded as the detection of the circle. The detection of the circle can be roughly divided into two categories: the detection method based on a traditional algorithm and the detection method based on deep learning.

12.1.1 Target detection and recognition method based on traditional image processing and machine learning algorithm The traditional target detection and recognition methods can be expressed as (1) target feature extraction; (2) target recognition; (3) target location. The features used here are artificially designed, such as Hough transform algorithm, scale-invariant feature transform (SIFT),

Chapter 12 Case study: remotely monitoring oil storage facilities

histogram of oriented gradient (HOG), accelerated up robust features, etc. These features are used to identify the target and then combined with the corresponding strategies to locate the target. For example, Hough transform is suitable for detecting targets with specific shapes with analytical geometric equations. Its essence is to determine the specific shapes in the image by using point line duality and a voting mechanism. The test results are shown in Fig. 12.1 below.

Figure 12.1 Detection results using Hough transformation. Reuse from Wang et al. (2019) under CC-BY 4.0.

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12.1.2 Target detection and recognition method based on deep learning algorithm Traditional target detection algorithms mainly rely on manually selected features to detect objects. As a feature learning method, deep learning can automatically learn the useful features of the target, avoid manual feature extraction, and ensure a good detection effect. Nowadays, target detection and recognition based on deep learning have become the mainstream method. Its workflow can be mainly expressed as (1) feature extraction of image; (2) target recognition and location based on depth neural network. convolution neural network (CNN) is the commonly-used deep neural network model for target detection. The existing target detection and recognition algorithms based on deep learning can be roughly divided into the following two categories: two-stage target detection and recognition algorithms based on regional suggestions, such as faster-RCNN (Ren et al., 2015) and SSP-Net (He et al., 2015); One-stage target detection and recognition algorithm based on regression, such as YOLO (Redmon et al., 2016) and SSD (Liu et al., 2016). Due to the difference in model structure between one-stage and two-stage detection algorithms, their performances are also different. The former is superior in detection accuracy and positioning accuracy, while the latter is more advantageous in detection speed. Taking the single-stage algorithm as an example, the detection results are shown in Fig. 12.2 below.

Figure 12.2 Oil tank detection results using single-stage object detection algorithm.

Chapter 12 Case study: remotely monitoring oil storage facilities

12.2 Detection method of storage tank The height information of oil tank is the inherent attribute information of oil tank and the key factor to calculate the volume of oil tank. How to obtain the accurate height information of buildings through high-resolution remote sensing images is still one of the hot topics in the relevant fields (Liasis and Stavrou, 2016; Qi et al., 2016). The building height is calculated based on the shadow in a single remote sensing image (Cheng and Thiel, 1995; Hartl and Cheng, 1995). The shadow part of the building in the image is obtained through a series of map image processing of the remote sensing image. Then, the height information of the building can be calculated according to the positional relationship among the building, satellite, and sun during remote sensing imaging (Izadi and Saeedi, 2012).

12.2.1 Spatial geometry between shadow and building The building blocks the sunlight, thus forming a shadow area on the backlight side of the building. Therefore, the total length of the building shadow, solar height angle, solar azimuth, and building height have a certain geometric relationship in space. During remote sensing imaging, the information of the shadow part of the building in the image has a certain geometric relationship with the satellite-related parameters during imaging. Comprehensive analysis shows that the formation of building shadows in high-score satellite remote sensing image is related to the size of the building itself, solar altitude angle, solar azimuth, satellite azimuth, satellite altitude angle, and other parameters. This study investigated the oil tank in the national petroleum reserve base. There is no large fluctuation in the topographic area, which will not affect the shadow of the oil tank on the ground, and the oil tank is perpendicular to the ground surface. There is a certain interval between oil tanks during construction. In the selected remote sensing satellite images, the shadow of oil tanks completely falls on the ground. Based on the above conditions, the height information of oil tank is obtained according to the traditional research method of the relationship between satellite azimuth, solar azimuth, solar altitude and building position. Traditionally, the relationship between building height and its shadow on remote sensing images can be divided into three types. (1) When the difference between satellite azimuth and solar azimuth is greater than 180 degrees, all the shadows of buildings

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Figure 12.3 The satellite and the sun are on the opposite side of the building. Reuse from Wang et al. (2019) under CCBY 4.0.

can be observed on satellite images (Fig. 12.3). The geometry relationship can be shown in Fig. 12.4. Under such conditions, the geometric relationship between the shadow length of the building and the height of the building itself is Eq. (12.1) H ¼ S  tan a

(12.1)

where a is the height angle of the sun, H is the height of the building, and S is the shadow length of the building projected on the ground. (2) When the azimuth of the satellite is equal to that of the sun and the influence of satellite azimuth on the shadow in remote sensing image is not considered (Fig. 12.5), the geometric relationship between the building itself, the solar azimuth, and the satellite azimuth is shown as Fig. 12.6.

Sun

Satellite

Ground

Figure 12.4 The satellite and the sun are on the opposite side of the building. Reuse from Wang et al. (2019) under CCBY 4.0.

Chapter 12 Case study: remotely monitoring oil storage facilities

Figure 12.5 The azimuth of the satellite is equal to that of the sun.

Sun

Satellite

Ground

Figure 12.6 Schematic diagram of equal azimuth between satellite and sun.

where a is the satellite altitude angle; b is the height angle of the sun; H is the height of the building; S is the total length of the shadow projected by the building on the ground; M is the length of the shadow that can be observed on the remote sensing image; L is the length of the shadow that cannot be observed on the remote sensing image, and its geometric relationship Eqs. (12.2) and (12.3). M ¼ SL ¼

H H  tan b tan a

(12.2)

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H ¼M

tan b  tan a tan a  tan b

(12.3)

(3) When the satellite azimuth and the sun azimuth are not equal and less than 180 degrees and the influence of satellite azimuth on the shadow image in remote sensing image needs to be considered. Under such conditions, the geometric relationship among the building itself, solar azimut,h and satellite azimuth is shown in Fig. 12.7 where a is the solar altitude angle; Y is the azimuth of the sun; b is the satellite altitude angle; s is the satellite azimuth; u is the angle formed between the projection direction of the shadow clockwise and the direction of the building; ED represents the height H of the ground building in the remote sensing image. A ground triangle is formed by three points A, B, and D on the ground. According to the nature of the triangle q ¼ :BDA ¼ g  s, that is, the difference between the azimuth of the sun and the azimuth of the satellite. In DBDE: BD ¼ ED  cot a; in DADE: AD ¼ ED  cot b. AC is parallel to the direction of ground objects and buildings in remote sensing images. As shown in Fig. 12.7, :ACD ¼ u. In DACD, according to the inner angle and property of trigonometric theorem: :CAD ¼ 180  u  q ¼ 180  ðu þ g  sÞ sin½180

CD AD ¼  ðu þ g  sÞ sin u

(12.4) (12.5)

The shadow length of buildings can be observed in remote sensing images, L ¼ BC¼BDCD, according to Eqs. (12.4) and (12.5):

Satellite

Sun

Ground

Figure 12.7 Schematic diagram of the difference between satellite azimuth and solar azimuth.

Chapter 12 Case study: remotely monitoring oil storage facilities

H ¼

L sin u cot a sin u  cot b sinðu þ g  sÞ

(12.6)

k¼

sin u cot a sin u  cot b sinðu þ g  sÞ

(12.7)

H ¼ Lk

(12.8)

The shadow length of buildings can be observed in remote sensing images. After obtaining the original data of remote sensing satellite images, the fixed parameters of satellite imaging can be known. According to Eq. (12.7), the shadow length of the building in the remote sensing satellite image is directly proportional to the height of the building itself.

12.2.2 Image shadow length calculation The shadow length can be obtained by calculating the shadow length from the number of pixels and directly measuring and calculating the shadow length with ENVI software. We used the idea of subpixel subdivision and positioning to obtain the corresponding upper and lower edge points in all shadow areas and obtain the length between them, and then use the median of these lengths as the final shadow calculation.

12.3 Application cases The data applied in the study is obtained from GF-2 satellite on 10:51, Apr. 16, 2015. The spatial resolution is 0.81 m, solar elevation angle is 57.96 degrees, solar azimuth angle is 150.66 degrees, and satellite azimuth angle is 310.48 degrees. The image is shownin Fig. 12.8.

Figure 12.8 GF-2 satellite image used in the experiments. Reuse from Wang et al. (2019) under CC-BY 4.0.

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Figure 12.9 Shadow area of the oil tank. Reuse from Wang et al. (2019) under CC-BY 4.0.

The extracted effective shadow area is shown in Fig. 12.9. The calculated heights of the oil tanks are shown in Table 12.1. The estimated tank height is inversely calculated according to the median of the shadow length of the tank. The absolute error between the estimated tank height and the actual tank height is within 0.25 m, and the relative error is within 1.15%. The estimation error accuracy of the tank height is less than 0.5 m, which meets the needs of tank height estimation and practical application. The results of the storage tank radius obtained by Hoffman transformation are shown in Table 12.2 below.

Chapter 12 Case study: remotely monitoring oil storage facilities

259

Table 12.1 Take the shadow length according to the median of subpixel subdivision positioning to reverse the result table of oil tank height.

Number Calculated height (m) Measured height (m) Absolute error(m) Relative error(%) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

21.5611 21.5611 21.5643 21.5611 21.5611 21.5611 21.5611 21.5611 21.5611 21.5611 21.5611 21.5611 21.5611 21.5643 21.5611 21.5611 21.5611 21.5611 21.5643 21.5611 21.5611 21.5643 21.5643 21.5611

21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000 21.8000

0.2389 0.2389 0.2357 0.2389 0.2389 0.2389 0.2389 0.2389 0.2389 0.2389 0.2389 0.2389 0.2389 0.2357 0.2389 0.2389 0.2389 0.2389 0.2357 0.2389 0.2389 0.2357 0.2357 0.2389

Reuse from Wang et al. (2019) under CC-BY 4.0.

From the experimental results shown above, it can be seen that the absolute error range between the calculated radius and the actual radius of the oil tank is 0.1134e0.3413 m, the relative error range is 0.28%e0.85%, and the estimated radius of the oil tank is within. Therefore, it can meet the needs of practical application. The calculated volume of the oil tank is shown in Table 12.3 below.

1.10% 1.10% 1.08% 1.10% 1.10% 1.10% 1.10% 1.10% 1.10% 1.10% 1.10% 1.10% 1.10% 1.08% 1.10% 1.10% 1.10% 1.10% 1.08% 1.10% 1.10% 1.08% 1.08% 1.10%

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Table 12.2 Calculation of the radius of tank top.

Number Calculated radius (m) Measured radius (m) Absolute error (m) Relative error (%) 1 2 3 4 6 7 8 9 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Average

39.7177 39.7584 39.8245 39.7335 40.1134 39.6921 39.6844 39.7004 39.6587 39.7253 39.6668 39.6587 39.7168 39.7166 39.6834 39.7171 39.7168 40.1461 39.7335 39.6752 39.7255 39.6668 39.7469

40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000 40.0000

0.2823 0.2416 0.1755 0.2665 0.1134 0.3079 0.3156 0.2996 0.3413 0.2747 0.3332 0.3413 0.2832 0.2834 0.3166 0.2829 0.2832 0.1461 0.2665 0.3248 0.2745 0.3332 0.2767

0.71% 0.60% 0.44% 0.67% 0.28% 0.77% 0.79% 0.75% 0.85% 0.69% 0.83% 0.85% 0.71% 0.71% 0.79% 0.71% 0.71% 0.37% 0.67% 0.81% 0.69% 0.83% 0.69%

Reuse from Wang et al. (2019) under CC-BY 4.0.

According to the experimental results, the absolute error range between the estimated calculated volume of the oil tank and the actual volume of the oil tank is 0.0416  104 m3e0.3050  104 m3, the relative error range is, and the estimated volume of the oil tank is within. Therefore, it can meet the demand of actual large oil tank volume estimation.

Chapter 12 Case study: remotely monitoring oil storage facilities

261

Table 12.3 The calculated volume of oil tank.

Number

Calculated volume (104 m3)

Measured volume (104 m3)

Absolute error (m3)

Relative error (%)

1 2 3 4 6 7 8 9 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Average

10.6799 10.7018 10.7390 10.6884 10.8938 10.6662 10.6620 10.6706 10.6482 10.6840 10.6526 10.6498 10.6795 10.6793 10.6615 10.6796 10.6810 10.9116 10.6884 10.6587 10.6857 10.6526 10.6961

10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532 10.9532

0.2733 0.2514 0.2142 0.2648 0.0594 0.2870 0.2912 0.2826 0.3050 0.2692 0.3006 0.3034 0.2737 0.2739 0.2917 0.2736 0.2722 0.0416 0.2648 0.2945 0.2675 0.3006 0.2571

2.50% 2.30% 1.96% 2.42% 0.54% 2.62% 2.66% 2.58% 2.78% 2.46% 2.74% 2.77% 2.50% 2.50% 2.66% 2.50% 2.49% 0.38% 2.42% 2.69% 2.44% 2.74% 2.35%

Reuse from Wang et al. (2019) under CC-BY 4.0.

References Cheng, F., Thiel, K.-H., 1995. Delimiting the building height in a city from the shadow in a panchromatic SPOT image: Part 1: test of forty-two buildings. Int. J. Rem. Sens. 16, 409e415, 1995. Hartl, P., Cheng, F., 1995. Delimiting the building heights in a city from the shadow on a panchromatic SPOT-image: Part 2: test of a complete city. Int. J. Rem. Sens. 16, 2829e2842.

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He, K., Zhang, X., Ren, S., Sun, J., 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37 (9), 1904e1916. Irvin, R.B., McKeown, D.M., 1989. Methods for exploiting the relationship between buildings and their shadows in aerial imagery. IEEE Trans. Syst. Man Cybern. 19, 1564e1575. Izadi, M., Saeedi, P., 2012. Three-dimensional polygonal building model estimation from single satellite images. IEEE Trans. Geosci. Rem. Sens. 50, 2254e2272. Liasis, G., Stavrou, S., 2016. Satellite images analysis for shadow detection and building height estimation. ISPRS J. Photogrammetry Remote Sens. 119, 437e450. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C., 2016. SSD: Single Shot Multibox Detector. Paper presented at the European Conference on Computer Vision, Amsterdam, the Netherland, pp. 21e37, 11e14 Oct. 2016. Qi, F., Zhai, J.Z., Dang, G., 2016. Building height estimation using Google Earth. Energy Build. 118, 123e132. Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016. You Only Look once: Unified, Real-Time Object Detection. Paper Presented at the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 779e788, 27e30 June 2016. Ren, S., He, K., Girshick, R., Sun, J., 2015. Faster-RCNN: Towards Real-Time Object Detection with Region Proposal Networks. Paper Presented at the Neural Information Processing Systems, Montreal, QC, Canada, pp. 265e283, 7e12 December 2015. Wang, T., Li, Y., Yu, S., Liu, Y., 2019. Estimating the volume of oil tanks based on high-resolution remote sensing images. Rem. Sens. 11 (7), 793.

13 Case study: Oil spill tracing based on stable carbon isotope of petroleum hydrocarbons 13.1 Theoretical basis of stable carbon isotope of petroleum hydrocarbon 13.1.1 Stable carbon isotope The isotope refer to elements with the same atomic number but different atomic weights in the periodic table of elements. Stable isotopes are isotopes whose nuclear structure will not change spontaneously. At present, 274 kinds of stable isotopes have been found. Some common stable isotope studies mainly include hydrogen (H/D), carbon (13C/12C), oxygen (18O/16O and 17 O/16O), sulfur (34S/32S and 33S/32S), nitrogen (15N/14N), etc. Carbon mainly consists of two stable isotopes, i.e., 12C and 13C, with relative abundances of 98.89% and 1.11%, respectively (Fig. 13.1). Stable carbon isotope ratio refers to the ratio of various stable isotope abundances in the carbon element, i.e., the ratio of 13 C/12C abundances, which is usually expressed in the form of d. Petroleum is a complex mixture formed by the continuous evolution of organic matter under long geological conditions, and the hydrocarbons formed by carbon and hydrogenation constitute the main components of petroleum. The stable carbon isotope composition of oil is mainly controlled by the sedimentary environment and the type of organic parent material of oil generation and is relatively less affected by other factors such as the thermal evolution of organic matter. It is generally believed that if crude oil is related to hydrocarbon source rocks, their carbon isotope composition is similar and has the “parent material inheritance effect” (Xu et al., 2001). Therefore, the isotopic compositions of petroleum of different origins are quite different. According to the inheritance and affinity of the carbon isotope composition of crude oil, its generation environment and parent material source can be determined, which lays the theoretical Oil Spill Detection, Identification, and Tracing. https://doi.org/10.1016/B978-0-443-13778-5.00014-X Copyright © 2024 Elsevier Inc. All rights reserved.

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Chapter 13 Oil spill tracing based on stable carbon isotope

Figure 13.1 Distribution of carbon isotopes in nature.

foundation for the comparative study of oil-source, oil-oil, and oil-rock using isotopes. The research shows that the distribution range of oil’s d13C value ranges between
20& and
35&. When the oil source rock is under a marine sedimentary environment, the d13C value is less than
30&, while that under a lake sedimentary environment ranges between
29.5& and
28&. The d13C value of the crude oil comes from continental sedimentary environment, or coal-formed oil, and ranges between
28& and
24& (Li et al., 2020). According to the composition characteristics of oil isotopes, we can identify the oil types (Asif et al., 2011), judge the sedimentary environment of hydrocarbon source rocks (Al-Areeq and Maky, 2015), discuss the origin of organic parent materials (Yu et al., 2012), reconstruct the paleoenvironment and paleoclimate (Nabbefeld et al., 2010), and identify the source of oil spills (Liu et al., 2017; Li et al., 2018).

13.1.2 Isotope fractionation The two most significant properties of stable isotopes are stability and fractionation. Stability means that the nuclear structure does not change after complex chemical reactions. In a stable system, the characteristic phenomenon that the isotope of an element is divided into several substances or phases according

Chapter 13 Oil spill tracing based on stable carbon isotope

to different ratios due to the isotope effect is called isotope fractionation. In the research of physical chemistry, isotope fractionation is divided into three parts, namely, thermodynamic equilibrium fractionation, kinetic nonequilibrium fractionation, and nonmass-related fractionation. A brief introduction to each fractionation type is as follows: (1) Thermodynamic equilibrium fractionation The isotopic equilibrium fractionation process includes many physical and chemical processes that have different mechanisms but can ultimately reach the equilibrium state of isotope distribution. After this isotope equilibrium fractionation state is established, its physical and chemical properties remain unchanged in the stable system, so the distribution and ratio of the isotope of this element in different organic matter or different phase states remain unchanged, which is the characteristic of the thermodynamic equilibrium state in isotope fractionation. (2) Kinetic nonequilibrium fractionation Also known as dynamic nonequilibrium fractionation, isotopic nonequilibrium fractionation is an isotope fractionation phenomenon that deviates from isotope equilibrium fractionation. According to the records of available data, most isotope equilibrium fractionation is independent of time, while the basic characteristics of isotope kinetic nonequilibrium fractionation are different. In this system, the distribution of elemental isotopes in organic matter or between phases will change with the progress of time and reaction. (3) Mass-independent fractionation According to the data and results of a large number of experiments, it can be concluded that the isotope fractionation effect is directly proportional to the mass difference of isotopes, especially in the three-isotope fractionation system. The isotope ratio in the low-mass tri-isotope fractionation system is a function of the reciprocal difference between the masses of isotopes. For example, we can draw a linear function with a slope k of 0.516 by using the d17O to the d18O. This linear relationship is also known as the mass-related fractionation line or the mass fractionation line, as shown in Fig. 13.2. However, the isotope fractionation rule that a small part of the isotope mass does not obey the mass-dependent fractionation rule is called nonmass-dependent fractionation. The isotopic effect refers to the difference in physical and chemical properties of substances caused by the difference in

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Figure 13.2 Mass correlation fractionation line of tri-isotopes of oxygen in earth samples.

isotope quality in organic matter or different phase states. Therefore, isotope effects can also be divided into three aspects: physical isotope effects, thermodynamic isotope effects, and kinetic isotope effects. The physical isotope effect means that many physical and chemical properties of substances, such as density, viscosity, refractive index, electromagnetic properties, and solubility, will vary due to the differences in isotope quality. The thermodynamic isotope effect mainly refers to the isotope effect of substances in the processes of chemical equilibrium and phase equilibrium. The difference in thermodynamics and physical and chemical properties in the pyrolysis reaction experiment of organic matter will lead to a relatively large allelic effect in the reaction. Kinetic isotope effect mainly refers to the relationship between isotope effect and chemical reaction rate. In the chemical reaction process of organic matter pyrolysis, isotope substitution will change the energy state of reactants, resulting in different chemical reaction rates. The study on the carbon and hydrogen isotopic compositions can simulate the geological accumulation environment of organic matter in ancient times, analyze the formation process of organic hydrocarbon reservoirs, and determine the source of oil and gas resources. Since the carbon isotope of crude oil will be affected by many external factors with the change of the geological

Chapter 13 Oil spill tracing based on stable carbon isotope

Figure 13.3 Distribution of petroleum hydrocarbon d13C in different geological ages.

environment, the analysis and research of the carbon isotope of crude oil can simulate important characteristic information of crude oil, such as the nature of source rock, maturity, reservoir maturity, and geological migration (Fig. 13.3). It is of great significance to the study of oil spill tracing through the analysis of the nature of the source rock, the process of reservoir formation, and the change in the geological environment. Stable carbon isotope fractionation mechanisms mainly include isotope exchange reactions, dynamic effect of photosynthesis, the dynamic effect of thermal and chemical reactions, and the physical and chemical effect s of isotopes. The research shows that when carbon isotope exchange reactions occur, compounds with high valence carbon will preferentially enrich d13C, and the enrichment order of d13C is: CO2
3 > CO2 > CO > C > CH4. In physical processes such as evaporation and diffusion, the lighter d12C is preferentially lost, the remaining d13C is heavier. Because the chemical bond strength of 13Ce13C, 13Ce12C, and 12Ce12C weakens in turn, the d13C bond is more prone to fracture during the decomposition of organic matter. Plants will give priority to the use of CO2 molecules rich in d12C in photosynthesis, while C3, C4, and CAM plants adopt different ways of photosynthesis, and the carbon isotope composition of various plant types is

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also different. For example, sugar beet CO2 fixation follows the Calvin cycle, in which CO2 is fixed to form 3-phosphate glycerate. As a C4 plant, sugarcane has another way to fix CO2. The initial product of CO2 fixation is C4 dicarboxylic acid, mainly through the Hatch-Slake pathway. Different ways of carbon assimilation and carbon fixation lead to different abundances of d13C. In general, C3 plants are easier to fix d12C than d13C in the process of photosynthesis and have a relatively low stable isotope ratio, ranging from
35& to
24&. The rate of fixation of d12C and d13C in C4 plants is basically the same, and the rate of fixation of d13C in C4 plants is from
17& to
11&, which is relatively high. CAM plants have the most extensive distribution, with a value of
34&
13& (Liang et al., 1998). The isotopic photosynthesis fractionation mechanism is closely related to the isotopic composition of biogenic geological bodies such as coal, oil, and natural gas (Liu et al., 2019). Isotopic equilibrium fractionation is the cornerstone of stable isotope geochemistry (Li et al., 2019).

13.1.3 Standard stable carbon isotope ratio The appropriate standard should be selected first when making isotope analysis, and the comparison between different samples must also use the same standard to make sense. The general requirements for reference materials of isotopes are: (1) Uniform and stable composition; (2) large quantity and easy to access; (3) simple procedures for chemical preparation and isotope measurement; and (4) it is roughly the middle value of the variation range of the natural isotope ratio for the determination of most samples. At present, the internationally accepted isotope standards are issued by the International Atomic Energy Agency and the National Institute of Standards and Technology of the United States. The isotope ratio of an element is the ratio of the abundance of each isotope in the element: the ratio of heavy isotope atomic abundance to light isotope atomic abundance of this element. Because the isotope ratio is difficult to measure accurately, the isotope ratio of the sample is usually expressed as the thousandth difference between the isotope ratio of the sample and that of a reference material. The calculation formula of the isotope ratio is given as Eq. (13.1):
 RSa 13
1  103 (13.1) d C¼ RSt

Chapter 13 Oil spill tracing based on stable carbon isotope

where Rsa is the isotope ratio of the sample and Rst is the ratio of the standard stable isotope. The measurement and comparison unit of isotope ratio is generally expressed in thousands of fractions (&).

13.2 Stable isotope analysis of oil spill 13.2.1 General methodology In order to further study the change mechanism of the impact of weathering stress on the chemical composition and oil fingerprints of oil spills, representative crude oil and fuel oil were selected for simulated weathering experiments to study the chemical compositions and d13C values of crude oil and fuel oil under weathering stress. By screening the index parameters with strong weathering resistance, the study is expected to reveal the change characteristic mechanism of the d13C of specific target compounds carrying the original information of oil and achieve the identification and tracing of oil spills. The overall workflow of oil spill isotope analysis is shown in Fig. 13.4. Crude oil and representative fuel oils from different regions are selected as the research objects. First, a whole sample of the experimental oil samples analyzed to obtain the stable

Figure 13.4 The overall workflow of oil spill isotope analysis.

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carbon isotope ratio of the whole sample. Then, the experimental oil sample is pretreated to extract the n-alkane component, polycyclic aromatic hydrocarbon (PAH) component, and marker compound. The molecular-level chromatographic analysis and atomic-level stable isotope analysis are carried out to obtain the original chromatogram, characteristic ratio, and stable carbon isotope ratio of the monomer n-alkane component, PAH component, and marker compound of the experimental sample. Through data processing and result comparison, the oil spill isotope analysis is expected to reveal the change mechanism of stable carbon isotope composition of oil under weathering stress, establish the traceability analysis system of oil spills on water, and realize the identification and traceability of oil spills.

13.2.2 Stable carbon isotope fingerprint identification system for spilled oil on water 13.2.2.1 Operating principle of isotope proportional mass spectrometer The principle of an isotope proportional mass spectrometer is to first convert the sample into gas (such as CO2, N2, SO2, or H2), ionize the gas molecules in an ion source (stripping one electron from each molecule, causing each molecule to carry a positive charge), and then inject the ionized gas into the flight tube. The flight tube is curved with a magnet placed above it, and the charged molecules are separated by mass. Molecules containing heavy isotopes are less curvy than molecules containing light isotopes. At the end of the flight tube is a Faraday collector that measures the strength of the ion beam with a specific mass after separation by a magnet. It is also called a gas isotope proportional mass spectrometer because it converts the sample into gas for measurement. Taking CO2 as an example (Fig. 13.5), three Faraday collectors are required to collect ion beams with masses of 44, 45, and 46, respectively. Different mass ions are collected simultaneously, allowing accurate measurement of the ratio between different mass ions. When the charged particles move in a magnetic field, they deflect, and the degree of deflection is inversely proportional to the mass charge ratio m/e. A charged ion carries an electric charge e’ and obtains energy eV when passing through an electric field, which should be equal to the kinetic energy of the ion: 1 = 2m0 v02 ¼ e 0 V

(13.2)

Chapter 13 Oil spill tracing based on stable carbon isotope

Figure 13.5 Working principle of isotope proportional mass spectrometer.

where m0 and v0 are the mass and velocity of the particles, e0 is the particle charge, and V is the voltage. When charged particles enter the magnetic field in a direction perpendicular to the magnetic force line, they are subjected to the Lorentz force, which is perpendicular to the direction of the magnetic field and the direction of motion. The magnitude of the force is given as Eq. (13.3): F ¼ e 0 VB=c

(13.3)

where B is the magnetic field strength and c is the speed of light. Combine Eqs. (13.2) and (13.3) to obtain Eq. (13.4): pffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffi ffi Be 0 2e 0 V Be 0 2V pffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffi ¼  e 0 =m (13.4) F¼ c c m Obviously, F is a function of the mass of a particle, or rather, a pffiffiffiffiffiffiffiffiffiffiffi function of the charge-mass ratio e 0 =m. According to this, when charged particles move in a magnetic field, they deflect due to the Lorentz force, resulting in the separation of isotopes of different masses. The deflection radius of heavy isotopes is large, while the deflection radius of light isotopes is small. In actual measurement, it is not necessary to directly determine the absolute content of isotopes. Rather, it is necessary to measure the ratio of two isotopes, such as 18O/16O or 34S/32S. Mass spectrometry used for stable isotope analysis only compares the isotopic ratios of samples and standards for measurement.

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13.2.2.2 Basic structure of isotopic proportional mass spectrometer Like other mass spectrometers, the structure of isotope proportional mass spectrometers can be divided into four parts: an injection system, an ion source, a mass analyzer, and a detector. In addition, there is electrical and vacuum system support (Fig. 13.6). A sample injection system introduces the gas to be measured into a mass spectrometer. It requires the introduction of samples without damaging the vacuum of the ion source and analysis chamber. In order to avoid isotope fractionation caused by diffusion, it is required to form a viscous gas flow in the sample injection system, that is, the average free path of the gas molecules is smaller than the diameter of the sample reservoir and gas flow pipe, so that the gas components can frequently collide with each other and interact with each other to form a whole. In the ion source, the gas molecules of the sample to be tested undergo ionization, accelerate, and focus into a beam. For certain elements, it is often possible to use more than one ion source to determine isotopic abundance. The ion sources need to have high ionization efficiency and good monochromaticity. The mass analyzer receives ion separations with different charge mass ratios. The main body is a fan-shaped magnet. The ion detector receives ion beams with different charge mass ratios from a mass analyzer, amplifies them, and records them. It consists of an ion receiver and an amplification measuring device. After passing through the magnetic field, the ion beam to be analyzed passes through a special slit and then refocuses onto the receiver and collects it, which is like a Faraday cylinder. Modern mass spectrometers have two or more receivers to simultaneously receive ion beams of different mass numbers, alternating vehicle

Figure 13.6 Structural diagram of isotope mass spectrometer.

Chapter 13 Oil spill tracing based on stable carbon isotope

samples, and standard isotope ratios. Measurements with high accuracies can be achieved by comparing the two parts.

13.2.2.3 Stable carbon isotope fingerprint traceability system for oil spill The stable carbon isotope fingerprint traceability system for oil spills, as shown in Fig. 13.7, is composed of three subsystems, including a stable isotope ratio mass spectrometer, an element analyzer, gas chromatography, and gas mass spectrometry. The stable carbon isotope composition characteristics of crude oil/fuel oil contain a large amount of information on the source and sedimentary environment of organic parent materials, and the total carbon d13C values of crude oil/fuel oil of different origins have significant differences. By analyzing the differences in the d13C characteristics of crude oil/fuel oil, the stable carbon isotope ratio in the entire oil sample can be determined, and the oil formation types of offshore oil spills can be identified, which provides the theoretical basis for the identification and traceability of oil spills, as well as the technical support for the investigation and treatment of oil spills. Petroleum generally carries organic compounds generated under specific geological environments (pressure, temperature,

Figure 13.7 Photograph of the stable carbon isotope fingerprint traceability system for oil spill.

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biological composition, etc.), such as steranes, terpenes, etc. These biomarkers often do not evolve with later changes (e.g., petrochemical, natural weathering, oil dispersant interference, etc.), and have strong chemical stability. They can be applied to infer the content of total saturated hydrocarbons, total aromatic hydrocarbons, and total petroleum hydrocarbons in crude oil/ fuel oil. They can also be used to screen specific target compounds that carry original petroleum information and establish an oil fingerprint “map spectrum” sample library. Offshore oil spills are often accompanied by later weathering processes. The later weathering process will cause changes in the chemical composition of offshore oil spills. When this chemical composition change is calibrated using regular analytical methods, its chromatographic characteristics and component indicators will undergo significant changes, making it impossible to obtain preweathering fingerprint information. Weathering resistance is the greatest advantage of stable carbon isotope analysis. Based on the analysis of the stable carbon isotope composition of single molecular hydrocarbons, it is possible to discover the stable carbon isotope fingerprint characteristics of typical petroleum hydrocarbon compounds and achieve synchronous detection of the chemical composition and stable carbon isotope ratio of single molecular hydrocarbons. The stable carbon isotope analysis technology promotes a technological leap in oil pollution traceability from the molecular qualitative level to the atomic quantitative level and achieves accurate traceability of oil pollution.

13.2.3 Oil sample collection For the oil samples directly collected on the sea surface, multiple sampling points should be set up on the oil layer or in the contaminated area for sampling. Samples should be taken at the thicker and thinner parts of the oil layer. When collecting thin oil film samples, attention should be paid to avoid contamination of the samples by other oils (such as lubricating oil, fuel oil, etc.). If the oil spill occurs in waters with typical human impacts, such as bays, estuaries, and basins that contain oil in the water, background samples should also be collected. For collecting thin oil film samples, the following three methods are recommended: (1) Sampling using tapered polytetrafluoroethylene bags: Fix the tapered bag together with a metal ring with a handle. Cut a circular hole with a diameter of about 1e2 cm at the bottom of the bag. Skim oil from the water’s surface and drain excess water from its bottom. Repeat the above action until

Chapter 13 Oil spill tracing based on stable carbon isotope

sufficient oil is skimmed. When the water in the bag is drained, place the sampling bottle under the bag and drain the oil into the bottle. (2) Sampling using polytetrafluoroethylene mesh: Fix the sampling net with a metal ring with a handle. Move it on the oil layer to filter the oil-water mixture through the sampling net to absorb oil samples. Slowly move the oil-collecting net back and forth several times. Then remove the oilcollecting net from the metal ring and put the entire oilcollecting net into the sampling bottle. (3) Using oil suction pads The oil absorption sheet is made of polytetrafluoroethylene material or glass fiber sprayed with polytetrafluoroethylene. Place the oil suction piece on the water’s surface. Let it stand for a few minutes or move it back and forth to absorb the oil slick, and then directly load the oil suction piece into a sampling bottle. For the oil sampling from shore, the oil sample should be scraped off and placed in the sample bottle. If it is difficult to scrape off the oil stains on stones, seaweed, or other materials, then the contaminated materials and the oil stains should be completely placed in the bottle. Early oil spills, tar balls, and other oil sources on the beach should be carefully observed to avoid contamination of the sample. If there is a possibility of contamination, a background sample should also be collected. Oil samples can also be collected from oily animals. When sampling from oily animals, the contaminated oil should be manually scraped off the animal to avoid prolonged contact with feathers or fur. If the above work is difficult, the oily animal fur or feathers can be cut off and placed in a sample bottle, or the oil-contaminated animal carcass frozen as a sample can be transported back to the laboratory. When sampling from ships or other suspected oil spill sources, the oils should be sampled by personnel with some experience or who are familiar with the ship’s structure. Sampling personnel should be familiar with the relevant regulations for entering the enclosed space on the ship and should promptly consult if in doubt. All waste oil tanks, residual oil tanks, and engine room slop water on the ship should be sampled. First, a sketch of the path of spilled oil flowing from the ship to the water’s surface should be drawn. After determining the sampling point, one of the following methods can be used for sampling: (1) For oil tanks above the double bottom, the oil can be directly placed into a sampling bottle through a valve or sampled through various

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pipelines; (2) Sampling of sewage wells can be carried out using small sampling barrels; (3) Take samples from manholes and measure openings in the oil tank. When sampling from oil production, storage, and transportation facilities, the sampling locations include mobile drilling rigs, fixed or anchored oil production systems, oil pipelines, oil terminals, oil storage tanks, oil transportation vehicles, etc. When sampling oil wells, oil platforms, etc., it is necessary to fully understand their production conditions, including production processes, production rates, geological horizons, etc., to determine the number of samples and sampling methods. Oil samples collected directly from oil wells may contain a large amount of water and gas and have a high temperature. They must be stirred, allowed to stand, separated, and cooled before being loaded into a sample bottle. After sampling, immediately package the sample box, lock it, store it in a lowtemperature, dark environment, and send the sample to the laboratory as soon as possible. If the sample is water, 1e2 g of fungicides (such as mercury chloride) can be added to inhibit microbial degradation. The sample should be kept at a low temperature and protected from light during transportation. After transporting the samples to the laboratory, they should be stored in a refrigerator or freezer for refrigeration, and the temperature should be maintained at 3e4 C. As long as the sample quantity is sufficient, backup samples should be reserved and refrigerated at
10 to
15 C.

13.2.4 Sample preprocessing 13.2.4.1 Extraction of PAHs Accurately weigh 0.150 g of the oil sample, add n-hexane, bring to a constant volume of 10.00 mL, centrifuge at 3000 r/min for 15 min, and take the supernatant (F1) for use. Fill the chromatographic column with 2.00 g of activated silica gel, then put the supernatant (F1) into the chromatographic column and take the adsorbed liquid (F2). Put 200.00 g of activated 5A molecular sieve into a conical flask, and then put the adsorbed liquid (F2) into a conical flask containing 5A molecular sieve. Add cyclohexane to the conical flask until the liquid level in the flask is 2e3 cm high. Heating the conical flask on a heating plate at 80 C for 24 h. Cool to room temperature, transfer the solution in a conical flask to a rotary evaporator, and transfer the cyclohexane solution washing the surface of the molecular sieve to a rotary evaporator, where it is concentrated to 5 mL. Dilute the concentrated eluent to 1.00 mL for use.

Chapter 13 Oil spill tracing based on stable carbon isotope

13.2.4.2 Extraction of n-alkane Add a mixture of n-pentane and cyclohexane (volume ratio 1: 10) to the conical flask containing the 5A molecular sieve with a liquid level height of 4.0 cm. Place the conical flask on a heating plate and heat it at 85 C for 8 h. Separate the eluent after two elutions, transfer to a rotary evaporator, and concentrate to 5 mL. Dilute the concentrated eluent to 1.00 mL for use.

13.2.5 Data analysis 13.2.5.1 Quantitative analysis of n-alkanes By comparing the retention time of n-C10 to n-C24 standard samples with actual oil samples, the component changes of crude oil before and after weathering are analyzed. The relative error of the retention time within 0.03 s is considered the same target analyte. The quantitative method of gas chromatography adopts the area normalization method. The area normalization method does not require internal or external standards for quantification. It directly performs normalization calculations on peak area or peak height. The calculation formula is shown in the following Eq. (13.5): mi ¼

Ai Ai  100% ¼ n  100% P A1 þ A2 þ / þ A n A

(13.5)

i
1

where mi is the content of component i in the oil sample; A1 to An is the peak area of each component in the spectrum; Ai is the peak area of component i in the spectrogram of the standard sample. Due to the fact that quantitative analysis of a single n-alkane component cannot well reflect the changing trend of each component with weathering, the ratios of peak areas of several characteristic components before and after weathering are compared and analyzed, and these ratios are used as characteristic parameters for the chemical fingerprints of crude oil.

13.2.5.2 Quantitative analysis of PAHs Compare and analyze the synchronous fluorescence spectrum of crude oil samples with PAH standards to determine the PAH content in crude oil. From its fluorescence intensity, the concentration of fluorescent substances in the solution can be derived. The relationship between fluorescence intensity and solution concentration is shown in Eq. (13.6): F ¼ K 0 ðI0
IÞ

(13.6)

I ¼ I0 e
2:303εbc

(13.7)

where:

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By applying Eq. (13.7) to Eq. (13.6), we can obtain Eq. (13.8):   F ¼ K 0 I0 1
e
2:303εbc (13.8) where F is the fluorescence intensity; K0 is the ability to emit fluorescence, its value, and the fluorescence quantum yield Ф of I0 is the intensity of incident light; I is the transmitted light intensity; and c is the concentration of fluorescent substances in the solution. When εbc