Oil Spill Detection Research Articles

Optical discrimination of emulsified oil in optically complex estuarine waters.

Coastal estuaries, characterized by highly varying waters with complex optical properties, pose challenges for effective oil discrimination via optical remote sensing. In this study, an object-based spectra comparison (OBSC) approach was proposed to extract emulsified oil slicks from Balikpapan Bay, Indonesia, using optical imagery from Sentinel-2 Multispectral Instrument (MSI) and PlanetScope. The OBSC approach utilizes the spectral signatures of oil emulsion to develop emulsified oil index and normalized emulsified oil index to identify potentially emulsified oil objects from MSI and PlanetScope, respectively. Reflectance spectra of the potential objects were compared with those from dynamically-selected nearby waters to rule out false-positive detections, accounting for the varying water optical properties in the estuary. This OBSC approach performed well in extracting emulsified oil slicks from optical images. Moreover, multiple sensor capabilities for oil detection were compared, and requirements for detection and discrimination of oil spills in the coastal estuary environment were further discussed.

The capability of Sentinel-1 polarization combinations for oil spill detection (study case: Karawang, Indonesia)

Marine pollution is one of issues that is widely discussed such as marine debris, chemical contamination, and oil spill. It has many impacts on the environment. Remote sensing helps detect the presence of an oil spill via satellite platform. One commonly used satellite is Synthetic-Aperture Radar (SAR) imagery, which can see oil spills even in cloudy conditions. However, the black and white appearance of the radar sometimes makes it difficult to distinguish between an oil spill and look alike. Therefore, this study aims to determine the polarization combination in the Sentinel-1 SAR imagery. Sentinel-1 Single Look Complex (SLC) is used as input and processes through various stages to obtain Coherence Vertical-Vertical (VV) and Vertical-Horizontal (VH) results. There are four steps for processing the data; convert the data to backscattering coefficient from SLC data, stacking, Terrain Observation by Progressive Scan (TOPS) coherence, and building the robust combinations. The polarization in the Sentinel-1 was calculated to determine artificial bands for the correct combination, namely Divide (VV/VH) as band 1, Average ((VV+VH)/2) as band 2, Multiple (VVxVH) as band 3, Add (VV+VH) as band 4, and Subtract (VV-VH) as band 5. Furthermore, the determination of the band used in the polarization composite display is based on the intensity value. As a result, the recommendation for Sentinel-1 polarization is obtained, which involves bands 2, 3, and 5.

Oil spill identification in X-band marine radar image using K-means and texture feature.

Marine oil pollution poses a serious threat to the marine ecological balance. It is of great significance to develop rapid and efficient oil spill detection methods for the mitigation of marine oil spill pollution and the restoration of the marine ecological environment. X-band marine radar is one of the important monitoring devices, in this article, we perform the digital X-band radar image by "Sperry Marine" radar system for an oil film extraction experiment. First, the de-noised image was obtained by preprocessing the original image in the Cartesian coordinate system. Second, it was cut into slices. Third, the texture features of the slices were calculated based on the gray-level co-occurrence matrix (GLCM) and K-means method to extract the rough oil spill regions. Finally, the oil spill regions were segmented using the Sauvola threshold algorithm. The experimental results indicate that this study provides a scientific method for the research of oil film extraction. Compared with other methods of oil spill extraction in X-band single-polarization marine radar images, the proposed technology is more intelligent, and it can provide technical support for marine oil spill emergency response in the future.

Monitoring Marine Oil Spills in Hyperspectral and Multispectral Remote Sensing Data by the Spectral Gene Extraction (SGE) Method

Oil spill incidents threaten the marine ecological environment. Detecting sea surface oil slicks by remote sensing images provides support for the efficient treatment of oil spills. This is important for sustainable marine development. However, traditional methods based on field analysis are time-consuming. Spectral indices lack applicability. In addition, traditional machine learning methods strictly rely on training and testing samples which are in short supply in oil spill images. Inspired by the spectral DNA encoding method, a spectral gene extraction (SGE) method was proposed to detect oil spills in hyperspectral images (HSI) and multispectral images (MSI). The SGE method contained a parameter and two strategies. The parameter of elimination was designed based on the population genetic frequency. It was used to control the number of spectral genes. The spectral gene extraction strategies, named largest in-class similarity (LIS) strategy and largest inter-class difference (LID) strategy, were proposed to mine the spectral genes by oil spill samples. The oil spills would be determined by calculating the similarity of the extracted spectral genes to the DNA encoded images. In this research, the SGE method was validated by two AVIRIS images of the Gulf of Mexico oil spill, one MODIS image of the Gulf of Mexico oil spill, and one Landsat 8 image of a Persian Gulf oil spill. The oil spills in different remote sensing images could be detected accurately by the proposed method in a small set of samples. Experimental results indicated that the proposed method was suitable for detecting marine oil spills in AVIRIS, MODIS, and Landsat 8 images. In addition, the SGE method with the LIS strategy was more suitable for detecting oil spills in HSI. Its proper elimination rates were 0.8~1.0. The SGE method with the LID strategy was more suitable for detecting oil spills in MSI. Its proper elimination rates were 0.5~0.7.

A Dual Attention Encoding Network Using Gradient Profile Loss for Oil Spill Detection Based on SAR Images

Marine oil spills due to ship collisions or operational errors have caused tremendous damage to the marine environment. In order to better monitor the marine environment on a daily basis and reduce the damage and harm caused by oil pollution, we use marine image information acquired by synthetic aperture radar (SAR) and combine it with image segmentation techniques in deep learning to monitor oil spills. However, it is a significant challenge to accurately distinguish oil spill areas in original SAR images, which are characterized by high noise, blurred boundaries, and uneven intensity. Hence, we propose a dual attention encoding network (DAENet) using an encoder–decoder U-shaped architecture for identifying oil spill areas. In the encoding phase, we use the dual attention module to adaptively integrate local features with their global dependencies, thus improving the fusion feature maps of different scales. Moreover, a gradient profile (GP) loss function is used to improve the recognition accuracy of the oil spill areas’ boundary lines in the DAENet. We used the Deep-SAR oil spill (SOS) dataset with manual annotation for training, testing, and evaluation of the network, and we established a dataset containing original data from GaoFen-3 for network testing and performance evaluation. The results show that DAENet has the highest mIoU of 86.1% and the highest F1-score of 90.2% in the SOS dataset, and it has the highest mIoU of 92.3% and the highest F1-score of 95.1% in the GaoFen-3 dataset. The method proposed in this paper not only improves the detection and identification accuracy of the original SOS dataset, but also provides a more feasible and effective method for marine oil spill monitoring.

Oil Spill Detection by CP SAR Based on the Power Entropy Decomposition

In recent years, marine oil spills have adversely affected the marine economy and ecosystem, and the detection of marine oil slicks has attracted great attention. Combining different polarimetric features for better oil spill detection is a topic that needs to be studied in depth. Previous studies have shown that the compact polarimetric (CP) synthetic aperture radar (SAR) can be effectively applied to the detection of sea surface oil spill due to its own ability, which is conducive to the extraction of sea surface oil slick. In this paper, we apply the power–entropy (PE) decomposition theory, which decomposes the total scattered power according to the entropy contribution of each cell in the response, to CP SAR data for oil spill detection. The purpose of this study is to enhance the oil slick and the separability of the sea. As a result, an oil spill detection method based on the low-entropy radiation amplitude parameter lesa is proposed. We compare lesa with the other five popular polarimetric features and validate by quantitative evaluation that lesa is superior to other types of polarization feature parameters under different band data. Moreover, the random forest classification is performed on the feature map and achieves the visualization results of oil spill detection. The experimental results show that the lesa can combine the information of the two polarimetric characteristic parameters of entropy and total scattering power, and can clearly indicate the oil slick information under different scenarios.

Marine Oil Spill Detection from SAR Images Based on Attention U-Net Model Using Polarimetric and Wind Speed Information.

With the rapid development of marine trade, marine oil pollution is becoming increasingly severe, which can exert damage to the health of the marine environment. Therefore, detection of marine oil spills is important for effectively starting the oil-spill cleaning process and the protection of the marine environment. The polarimetric synthetic aperture radar (PolSAR) technique has been applied to the detection of marine oil spills in recent years. However, most current studies still focus on using the simple intensity or amplitude information of SAR data and the detection results are not reliable enough. This paper presents a deep-learning-based method to detect oil spills on the marine surface from Sentinel-1 PolSAR satellite images. Specifically, attention gates are added to the U-Net network architecture, which ensures that the model focuses more on feature extraction. In the training process of the model, sufficient Sentinel-1 PolSAR images are selected as sample data. The polarimetric information from the PolSAR dataset and the wind-speed information of the marine surface are both taken into account when training the model and detecting oil spills. The experimental results show that the proposed method achieves better performance than the traditional methods, and taking into account both the polarimetric and wind-speed information, can indeed improve the oil-spill detection results. In addition, the model shows pleasing performance in capturing the fine details of the boundaries of the oil-spill patches.

Oil Spill Detection Using MODIS and Sentinel-1 Images; A Case Study of Northern Persian Gulf

در سال‌های اخیر، نظارت دریایی و هوایی برای مهار آلودگی‌های دریایی رایج شده است اما این روش‌ها، به‌دلیل محدودیت‌های شرایط آب‌وهوا، زمان و مکان، به‌تنهایی نمی‌توانند پایش سریع و منظمی فراهم آورند. در این زمینه، سنجش از دور ماهواره‌ای می‌تواند نقش مهمی در تشخیص اولیه و پایش مداوم نشت نفت در دریا ایفا کند. سنجندة رادار آنتن مصنوعی سیستم سنجش از دور فعالی است که می‌تواند، برای آشکارسازی نشت نفت همراه با سنجنده‌های اپتیکال مانند مادیس، با توانایی تصویربرداری هم‌زمان، استفاده شود. هدف از این پژوهش آشکارسازی نشت نفت پیرامون سکوهای نفتی بخش شمالی خلیج فارس، در تاریخ‌های پانزدهم و هفدهم ژوئن 2015، با استفاده از تصاویر فروسرخ گرمایی مادیس و تصاویر راداری سنتینل‌ـ 1 است. برای محاسبة دمای سطح دریا، الگوریتم پنجرة مجزا روی تصویر باند 20 سنجندة مادیس اجرا شد. نتایج نشان داد که منطقة دارای نشت نفت دمایی کمتر از مناطق پیرامونی دارد. برای آشکارسازی دقیق‌تر لکه‌های نفتی و درستی‌سنجی نتایج پردازش‌های اعمال‌شده بر تصویر مادیس، از تصویر پلاریزاسیون VV سنتینل‌ـ 1 و پردازش‌های حذف نوفه مانند فیلترگذاری و مولتی‌لوکینگ استفاده شد. در پایان، از راه مقایسة دمای بویة موج‌نگار سازمان بنادر و دریانوردی بوشهر و دمای حاصل از تصویر مادیس و موقعیت جغرافیایی گسترة لکه‌های نفتی بارزشده، درستی نتایج این پژوهش و پردازش‌های صورت‌گرفته تأیید شد. استفاده از باند 20 سنجندة مادیس با هدف استخراج دمای سطح دریا و باندهای فروسرخ گرمایی آن، به‌منظور آشکارسازی لکه‌های نفتی در سطح دریا، اولین بار در این پژوهش ارزیابی شده است.

Preface

3rd INTERNATIONAL CONFERENCE ON ENERGY AND SUSTAINABLE ENVIRONMENT ICESE 2021 The third edition of the International Conference on Energy and Sustainable Environment – ICESE 2021 held 26 – 28 October, 2021. As usual, the conference was hosted by Covenant University, Ota, Nigeria. The theme of this edition is “Inclusive and Innovative Solutions for Sustainable Development” and the sub-themes included: Environment and Climate Change, Energy/Energy Management, Built Environment and Biotechnology.ICESE 2021, like the two previous editions, continued to build on the foundation of collaboration by research clusters in Covenant University. The clusters are: Energy and Environment Research Cluster, the Centre for Economic Policy and Development Research (CEPDeR), Renewable Energy Research Cluster (RERC), Built Environment Research Cluster (BERC), and most recently, the Biotechnology Research Cluster.The world energy and environment outlook has continued to elicit interest among researchers and policy makers. Given the multi-dimensional nature of the energy and environment challenge, an all-inclusive approach is deemed necessary. Hence, ICESE 2021 put together inclusive and innovative ideas towards addressing the numerous challenges associated with our environment. The keynote speakers were carefully selected to address some specific aspects of the energy and environment challenge.In this respect, the oil and gas industry and the energy sector in Nigeria come into sharp focus. Two keynote papers were presented during the plenary session. The first keynote address focused on the environmental challenges posed by the oil and gas sector from the regulatory perspective. The keynote speaker was Mr. Idris O. Musa, the Director General of the National Oil Spill Detection and Response Agency (NOSDRA), which is under the Federal Ministry of Environment, Nigeria. The keynote address was titled: Environmental Pollution – Impacts of Vandalism, Oil Theft and Artisanal Refining.Also, given the prevailing energy situation in Nigeria and the world over, new thinking in the area of utilizing fossil fuels such as coal becomes apposite. Hence, the second keynote Dr. Samson O. Bada of the University of Witwatersrand, South Africa, addressed the evolving role of coal even in the light of advancement of renewable energy technologies. The keynote paper was titled: Clean Coal Technologies and the role of Fossil Fuels in Renewable Technologies.Thereafter, Sixty-Five papers were presented in eight technical sessions. The built environment aspect of ICESE has remained very pivotal and over half of the papers came from the cluster. In all, the keynote papers and presentations at the ICESE 2021 were relevant to the achievement of the Sustainable Development Goals (SDGs) of the United Nations. Given the restricted travels as a result of the COVID-19 pandemic, the conference was planned and executed as a hybrid conference incorporating in-person and virtual components. After careful review of the papers presented at the conference, the selected papers in this proceedings were found to meet the requirements of the publishing organization – IOP PublishingList of Acknowledgements, Funding Support, Conference Technical/Scientific Committee, Local Organising Committee are available in this pdf.

An Analysis of the Optimal Features for Sentinel-1 Oil Spill Datasets Based on an Improved J–M/K-Means Algorithm

With the rapid development of world shipping, oil spill accidents such as tanker collisions, illegal sewage discharges, and oil pipeline ruptures occur frequently. As the SAR system expands from single polarization to multipolarization, the Polarmetric Synthetic Aperture Radar (Pol-SAR) system has been widely used in marine oil spill detection. However, in the studies of the oil spill extraction in SAR images, there are some problems that limit large-scale oil spill detection work. As a transition from single-polarized to full-polarized, the dual-polarized system carries some polarization information and can be obtained in large quantities for free, which has become a major breakthrough in solving the problem of large-scale oil spill detection. In order to optimize the multisource features that can be extracted from dual-polarized SAR images, greatly improve the utilization rate of dual-polarized SAR oil spill images under the premise of reducing workload, and ensure the accuracy of marine oil spill extraction, this paper adopts the metric of inter-class separability, the Jeffries–Matusita distance, which improves on the traditional K-means algorithm by focusing on the noise sensitivity defect of the K-means algorithm; the artificial influence of J–M distance in measuring the separability between classes improves the algorithm in three aspects: sample selection, distance calculation, and data evaluation. Finally, using the inter-sample J–M distance of multisource features, the overall accuracy of image segmentation, the F1-score, and the results of correlation analysis between features, three advantageous features and three subdominant features are selected that can be used for marine oil spill detection.

A Gamma-Log Net for Oil Spill Detection in Inhomogeneous SAR Images

Due to the complexity of ocean environments, inhomogeneous phenomenon always exist in SAR images of oil spills on the sea surface. In order to address this issue, a universal parameter adaptive Gamma-Log net for detecting oil spills in inhomogeneous SAR images is proposed in this paper. The Gamma-Log net consists of an image feature division module, a correction parameter extraction module, a Gamma-Log correction module and a feature integration module. The normalized input image features are divided into four blocks for correction in the image feature division module. According to the input characteristics, the Gamma-Log correction input parameters are obtained in the correction parameter extraction module. Subsequently, an adaptive method is introduced to adjust the parameters independently by the network to improve efficiency. Then, the input features are corrected in the Gamma-Log correction module by Gamma correction and logarithmic correction. Both correction methods can adjust the gray imbalance in the image and change the overall gray value and contrast. The separated feature blocks are finally reunited together by the feature integration module. In order to avoid information loss, an attention mechanism is added to this module. In the experiments, by adding Gamma-Log Net to multiple semantic segmentation networks, the MIoU and dice indicators increased to some extent, and the HD distance(Hausdorff-95) decreased. Our work demonstrates that the Gamma-Log net can be helpful for oil spill detection in inhomogeneous SAR images.

Detection of Oil Spills in the Northern South China Sea Using Landsat-8 OLI

Petroleum extraction, transportation, and consumption in the marine environment contribute to a large portion of anthropogenic oil spills into the ocean. While previous research focuses more on large oil spill accidents from oil tankers or offshore oil platforms, there are few systematic records on occasional regional oil spills. In this study, optical imagery from Landsat-8 OLI was used to detect oil slicks on the ocean surface through spatial analysis and spectral diagnosis in the northern South China Sea (NSCS). The source of the slicks was identified through datasets from traffic density and platform locations. A total of 632 oil slicks were detected in the NSCS from 2015 to 2019, where 57 were from platforms sources, and 490 were from ships. The average area of the detected slicks was 4.8 km2, and half of the slicks had areas <1.7 km2. Major oil spill hot spots included coastal Guangdong (ship origins), southeast and northeast Dongsha Island (ship origins), middle of south Beibu Gulf (ship and platform origins), and southeast Pearl River Estuary (platform origins). Through this study, we demonstrate the capability of medium-resolution optical imagery in monitoring regional oil spills. Such results and methods may help in near real-time oil spill monitoring and further environmental assessments.

Oil Spill Detection with Dual-Polarimetric Sentinel-1 SAR Using Superpixel-Level Image Stretching and Deep Convolutional Neural Network

Synthetic aperture radar (SAR) has been widely applied in oil spill detection on the sea surface due to the advantages of wide area coverage, all-weather operation, and multi-polarization characteristics. Sentinel-1 satellites can provide dual-polarized SAR data, and they have high potential for successful application to oil spill detection. However, the characteristics of the sea surface and oil film on different images are not the same when imaging at different locations and in different conditions, which leads to the inconsistent accuracy of these images with the application of the current oil spill detection methods. In order to avoid the above limitation, we propose an oil spill detection method using image stretching based on superpixels and a convolutional neural network. Experiments were carried out on eight Sentinel-1 dual-pol data, and the optimal superpixel number and image stretching parameters are discussed. Mean intersection over union (MIoU) was used to evaluate classification accuracy. The proposed method could effectively improve the classification accuracy; when the expansion and inhibition coefficients of image stretching were set to 1.6 and 1.2 respectively, the experiments achieved a maximum MIoU of 85.4%, 7.3% higher than that without image stretching.

Novel Dynamic Technique, Nano-DIHM, for Rapid Detection of Oil, Heavy Metals, and Biological Spills in Aquatic Systems.

Numerous anthropogenic and natural particle contaminants exist in diverse aquatic systems, with widely unknown environmental fates. We coupled a flow tube with a digital in-line holographic microscopy (nano-DIHM) technique for aquatic matrices, for in situ real-time analysis of particle size, shape, and phase. Nano-DIHM enables 4D tracking of particles in water and their transformations in three-dimensional space. We demonstrate that nano-DIHM can be automated to detect and track oil spills/oil droplets in dynamic systems. We provide evidence that nano-DIHM can detect the MS2 bacteriophage as a representative biological-viral material and mercury-containing particles alongside other heavy metals as common toxic contaminants. Nano-DIHM shows the capability of observation of combined materials in water, characterizing the interactions of various particles in mixtures, and particles with different coatings in a suspension. The observed sizes of the particles and droplets ranged from ∼1 to 200 μm. We herein demonstrate the ability of nano-DIHM to characterize and distinguish particle-based contaminants in water and their interactions in both stationary and dynamic modes with a 62.5 millisecond time resolution. The fully automated software for dynamic and real-time detection of contaminants will be of global significance. A comparison is also made between nano-DIHM and established techniques such as S/TEM for their different capabilities. Nano-DIHM can provide a range of physicochemical information in stationary and dynamic modes, allowing life cycle analysis of diverse particle contaminants in different aquatic systems, and serve as an effective tool for rapid response for spills and remediation of natural waters.

Marine Oil Spill Detection with X-Band Shipborne Radar Using GLCM, SVM and FCM

Marine oil spills have a significant adverse impact on the economy, ecology, and human health. Rapid and effective oil spill monitoring action is extraordinarily important for controlling marine pollution. A marine oil spill detection scheme based on X-band shipborne radar image with machine learning is proposed here. First, the original shipborne radar image collected on Dalian 7.16 oil spill accident was transformed into a Cartesian coordinate system and noise suppressed. Then, texture features and SVM were used to indicate the effective monitoring location of ocean waves. Third, FCM was applied to classify the oil films and ocean waves. Finally, the oil spill detection result was transformed back to a polar coordinate system. Compared with an improved active contour model and another oil spill detection method with SVM, our method performed more intelligently. It can provide data support for marine oil spill emergency response.

Analysis of Remotely Piloted Aircraft Payload for Oil Spill Detection

Abstract Operational monitoring of large sea aquatorium areas with the aim of detecting and controlling oil pollution is now carried out using various technological systems, such as satellite remote sensing, sea-going vessels, various aircraft and remotely piloted aircraft (RPA). Currently, the use of RPA for the fulfilment of monitoring tasks in the aquatorium is being intensively developed and can eliminate problems of remote sensing performed by satellites and piloted aircraft, such as short presence in the monitoring area, very long delay of information (up to 48 hours) and low quality of imagery. This paper presents mathematical modelling of RPA multi-sensor pay-loads for oil spill detection, monitoring and control. Information obtained from payload sensors is critical for increasing effectiveness of detection and monitoring of oil spills. Nowadays, many types of sensors are used for oil spill detection and monitoring. The most common sensors for detection of oil pollution are optical, multispectral, hyperspectral, thermal and laser fluorometers. Some oil pollution detection sensors have limitations, such as false alarm, only daytime operation, weather restrictions. Airborne remote sensors cannot provide all information required for detection of and response to oil spills, and water quality monitoring in the spill area. A model for selecting sensors for multi sensor payload that will make it possible to optimize the application of RPA for oil spill detection was developed. The RPA payload can be increased/reduced to the greatest possible extent with the help of different types of equipment at various parameters. The mathematical model of the integrated payload considers detection capability of sensors, weather conditions, sensor characteristics, and false alarm rate. The optimal multi-sensor payload will optimize the application of RPA for oil spill detection and monitoring.

Multispectral imagery for detection and monitoring of vegetation affected by oil spills and migration pattern in Niger Delta Region, Nigeria

Oil spills in the Niger Delta area can be detected and monitored using this novel technique. Landsat 5 and 8 images were used to assess various vegetation stress, such as Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Atmospheric Resistant Vegetation Index, Green Near Infrared and Green Short-wave Infrared, from the spill site in 2019, non-spill site (1992 pre-oil spill) and 2020 post-oil spill events, respectively. There is a substantial difference (p-value <0.005) in the vegetation conditions at the Spill Site and the non-spill Site in 2020 in terms of NDWI, SAVI, ARVI2 and G-NIR and G-SWIR. NDVI, SAVI, ARVI2, G-NIR, G-SWIR, and G-SWIR. There is a very significant difference in vegetation conditions between the pre-spill event and the post-spill event in 2020 (p-value <0.005). The oil spills' migration patterns and flow directions indicate from north to south along the runoff water using SRTM data. The sentinel 1 data revealed visualization of the flooded areas, including water surfaces that are stable around oil pipelines and the surroundings, using calibration threshold and the RGB band method to distinguish flooded areas from permanent water bodies. This was used to map areas affected by the oil spill on land and water bodies for proper environmental assessment before and during the flood of the oil spill environment. Multi spectral imagery is therefore a veritable tool for detection, response and monitoring of oil spills from pipelines.

Oil spills in China Seas revealed by the national ocean color satellites

<p indent="0mm">Oil spill is one of the key targets in marine environment monitoring. In the process of weathering (e.g., spreading, drift, emulsification, evaporation, dispersion, and biological degradation), various weathered oils with different visual characteristics can form, such as non-emulsified oil slicks, water-in-oil (WO) emulsions and oil-in-water (OW) emulsions. Timely and accurate information about the spatial coverage, concentration, and thickness of various oil types is important for the recovery and cleanup. Satellite observation can serve as an effective technique for the detection and monitoring of spilled oils. Synthetic aperture radar (SAR) and optical remote sensing are the most-frequently used techniques. SAR provides data under all-weather conditions, but it is often restricted to presence/absence detection without classifying oil types. Passive optical remote sensing is another popular tool as it is able to classify different weathered oils and quantify the thickness, concentration, or volume of them. The operational monitoring of marine oil spills via remote sensing requires optical sensors for wide coverage, high spatial resolution, high signal-to-noise ratio (SNR), large radiative dynamic range, and short revisit period. Haiyang-1C (HY-1C) and Haiyang-1D (HY-1D), launched on 7 September 2018 and 11 June 2020, are the first operational ocean color satellites of China. They are equipped with five sensors, including Chinese ocean color and temperature scanner (COCTS), coastal zone imager (CZI), ultraviolet imager (UVI), calibration spectrometer, and automatic identification system. The integrated imaging system composed of HY-1C/D sensors can provide more frequent observations. Particularly, the CZI sensors onboard, which have a spatial resolution of <sc>50 m</sc> with high SNR, and ensure double coverage in China Seas every three days, have demonstrated excellent performance in optical remote sensing of marine oil spills; thus, they can be used for the operational monitoring of oil spills. The results indicate that 57 oil spill incidents were detected by the CZI sensors over the past three years, the image area of spilled oils can be up to <sc>1291.63 km².</sc> More than 47 oil spills occurred in China Seas, particularly in the Bohai Sea, Yellow Sea, and South China Sea, which require further attention. Among these, 13 oil spills could be detected by both HY-1C and HY-1D, indicating that the satellites network can increases observation times and is helpful the oil spill detection. Meanwhile, owing to the wide coverage and high temporal resolution, the CZI sensors can effectively distinguish oil types (i.e., non-emulsified oil slicks and oil emulsions) under different sunglint conditions. The results indicate that almost half of the oil spills have emulsified, and these emulsified spilled oils tend to have larger length and image area, posing a great threat to the marine environment. Moreover, based on the identification result, the thickness and volume of the spilled oils can be estimated preliminarily according to <italic>Bonn Agreement</italic>, and the damage caused by spilled oils can be assessed with less uncertainties. Therefore, the HY-1C/D satellites can provide reliable data and improve the monitoring of marine oil spills in China Seas.

Drone Equipment and Configuration for Crude Oil Spill Detection in Water

Purpose: The objectives of this article was to outline the various equipment and configuration of drones which can be used to detect and support oil spill response in water. The study aimed at identify the type of sensors that can be mounted on drones for effective crude oil spill detection in water.&#x0D; Methodology: A systematic review of past articles on drones in disaster management was done. In this article, various parts of drone and configuration was tested. Sensors capable of detecting crude oil in the visible, infra-red, near infrared, laser fluoro-sensor were elaborated.&#x0D; Findings: A drone can properly be used for oil spill response operations. Appropriate sensors mounted on the drone can detect oil spill in water and can also access locations which are not readily accessible to large ships and other aerial patrol platform. The limitation of drones is the payload capability and its inability to operate well in windy weather.&#x0D; Recommendation: Since data collection with drone are faster, cheaper and easier during an oil spill response operations, it is highly recommended to mount the suitable sensor capable of detecting oil sheen even during nighttime operations.

Design and experimental study of a polarization imaging optical system for oil spills on sea surfaces.

The marine environment is complex and changeable. To meet the urgent needs of accurate detection and identification of oil spills, a visible/infrared dual-band common-aperture polarization imaging optical system based on a defocused plane polarization detector is designed. The optical system is evaluated by ZEMAX simulation software, and we carried out the polarization imaging oil species discrimination experiment based on the split focal plane polarization detector, which proved that for some oil species that cannot be distinguished by intensity information, the polarization information can be distinguished. It verifies the feasibility of polarization detection in the discrimination of marine oil spills, which is of great practical significance in the field of marine oil spill detection.