scholarly journals Determination of Sentinel-2 spectral reflectance to detect oil spill on the sea surface

2020 ◽  
Vol 4 (3) ◽  
pp. 144-154
Author(s):  
Pingkan Mayestika Afgatiani ◽  
Fanny Aditya Putri ◽  
Argo Galih Suhadha ◽  
Andi Ibrahim
Keyword(s):  
Oil Spill ◽  
Water Body ◽  
Spectral Reflectance ◽  
Oil Spills ◽  
Google Earth ◽  
Clean Water ◽  
Blue Band ◽  
Oil Refineries ◽  
Area Of Interest ◽  
Sentinel 2

Oil spill is one of the most common marine environmental problems. Oil spills can be caused by leakage at oil refineries at sea or disposal of vessel waste. This event has an impact on various sectors, such as fisheries, tourism, and marine ecosystems. This study aims to determine the spectral reflectance of Sentinel-2 response to detecting oil spill on the sea. Oil identification in the sea can be made visually by looking at colored patterns at sea level. Sentinel-2 image reflectance was obtained by processing the image using the Google Earth Engine platform. The results were clipped according to the area of ​​interest and divided to get a value between 0 and 1. Bands combination is possible to identify the oil spill visually. The silvery pattern saw in the red-green-blue combination, but it is arduous to estimate its distribution because of the silvery pattern seen for thick oil. The combination of SWIR-NIR-red bands proved effective in showing the distribution of oil with a deep black pattern. Spectral measurements in the field were undertaken by taking samples in the areas of oil spills and clean water bodies. The oil layer had a lower reflectance than the clean water body. The blue band gave a high response, but the red band gave less response. In the NIR and SWIR bands, the reflectance of oil was lower than the water body. In conclusion, the SWIR - NIR - RED band combination is better used to determine oil spills due to it shows the characteristics of oil generally, either thin or thick oil.

scholarly journals OIL SPILL INCIDENTS AND DISPERSANT APPLICATIONS IN KUWAIT

1987 ◽  
Vol 1987 (1) ◽  
pp. 247-253
Author(s):  
Salah M. Al-Mazidi ◽  
Omar Samhan
Keyword(s):  
Oil Spill ◽  
Marine Environment ◽  
Petroleum Hydrocarbons ◽  
Petroleum Hydrocarbon ◽  
Oil Spills ◽  
Petroleum Products ◽  
Coastal Environment ◽  
Industrial Activities ◽  
Oil Refineries ◽  
North And South

ABSTRACT Since the discovery of oil in Kuwait, most oil-related activities have been located along the coastline 50 km south of Kuwait City. Other related industrial activities have been developed in this area apart from oil and petroleum products export in order to diversify the national sources of income. For these reasons, the potential for large oil spills in Kuwait's marine environment is highest along the south coast, where oil refineries and exporting facilities are located. An average of 219 barrels of oil were spilled annually between 1979 and 1985, and 2,100 gallons of dispersants were used in cleanup operations. The majority of incidents involved less than 5 barrels of oil and 500 gallons of dispersants. Incidents involving more than 100 barrels of oil and 5,000 gallons of dispersants were confined to the Sea Island and Mina Al-Ahmadi North and South Piers. This distribution undoubtedly affects the concentration of petroleum residues in various components of the marine environment, resulting in an increase in tar ball density along this coast, reaching a maximum at Ras Az-Zor, and significantly higher levels of vanadium and petroleum hydrocarbons in sediments and oysters collected south of Mina Al-Ahmadi. The objective of this paper is to report on the number, volume, and frequency distribution of oil spill incidents in Kuwait and the usage of dispersants in cleanup operations. Vanadium and petroleum hydrocarbon concentrations also are described as is the sensitivity of the southern coastal environment to oil spills. Recommendations have been made on how to conduct cleanup operations for any future oil spill incidents along the southern shoreline of Kuwait.

scholarly journals Oil Spill Index (OSI) to Sentinel-2 Satellite Data: QU in International Contribution

2021 ◽  
Author(s):  
S Rajendran ◽  
AS Fahad ◽  
FN Sadooni ◽  
HAS Al-Kuwari ◽  
P Vethamony ◽  
...  
Keyword(s):  
Oil Spill ◽  
Satellite Data ◽  
Emergency Preparedness ◽  
Oil Spills ◽  
European Space Agency ◽  
Optical Data ◽  
Spectral Absorption ◽  
Spectral Bands ◽  
Space Agency ◽  
Sentinel 2

An Oil Spill Index (OSI = (B3+B4)/B2) was developed and applied to Sentinel-2 optical satellite data of the European Space Agency (ESA) to map marine oil spills using spectral absorption characters of spectral bands of the Sentinel-2. The potential application of OSI and derived indices [i. (5+6)/7, (3+4)/2, (11+12)/8 and ii. 3/2, (3+4)/2, (6+7)/5] were demonstrated to the oil spills that occurred off Mauritius, Indian Ocean, on August 06, 2020, and Norilsk region, Russia on May 29, 2020, and the results were published in the peer-reviewed research journals. Recently (August 19, 2021), our methodology was recognized by the Sentinel-Hub (a repository of custom scripts) https://custom-scripts.sentinel-hub.com/sentinel-2/oil-spill-index/ for OSI calculation. We validated the remote sensing results with the drone images taken during the incident. Our OSI index is the first to be applied to Sentinel-2 optical data to map oil spills. We proved the potential of indices and the capability of Sentinel sensors to detect, map, monitor, and assess the oil spill, which can be used for emergency preparedness of oil spills.

AUTOMATED SYSTEM FOR NEAR-REAL TIME PREDICTION OF OIL SPILLS FROM EU SATELLITE-BASED DETECTION SERVICE

2017 ◽  
Vol 2017 (1) ◽  
pp. 1574-1593 ◽  
Author(s):  
Rodrigo Fernandes ◽  
Francisco Campuzano ◽  
David Brito ◽  
Manuela Juliano ◽  
Frank Braunschweig ◽  
...  
Keyword(s):  
Real Time ◽  
Oil Spill ◽  
Hazard Assessment ◽  
Situational Awareness ◽  
Oil Spills ◽  
Oil Pollution ◽  
Warning System ◽  
Pollution Source ◽  
Google Earth ◽  
Automated System

ABSTRACT 2017-244: The state-of-the-art in both operational oceanography, remote sensing, and computational capacity, enables now the possibility of developing near-real time, holistic automated services capable of dramatically improving maritime situational awareness to responding to oil spill emergencies. Based on the European satellite-based oil spill and vessel detection service – CleanSeaNet (EMSA – European Maritime Safety Agency), which distributes oil pollution detection standardized notification packages in less than 30 minutes, a new automated early warning system (EWS) for near-real time modelling and prediction of the detected oil spills was developed. This EWS provides 48-hour oil spill forecasts + 24-hour backward simulations, delivering results 5–10 minutes after the reception of the oil spill detection notifications. These forecasts are then distributed in multiple formats and platforms (e.g. Google Earth, e-mail). The oil spill fate and behaviour model used in this EWS is part of MOHID modelling system, and is coupled offline with metocean forecast solutions, taking advantage of autonomous models previously run in multiple institutions. The system is currently able to integrate various metocean forecasting systems, being agnostic about the data sources and applied locations, as long as their outputs comply with commonly adopted formats, including CF compliant files or CMEMS (Copernicus Marine Environment Monitoring Service). The EWS is currently operational in western Iberia, supporting Portuguese Maritime Authority, and is being expanded to neighbourhood regions (from Spain and Morocco) with high resolution metocean models (MARPOCS project funded by European Union Humanitarian Aid & Civil Protection). Taking advantage of the coupling of MOHID oil spill model and CleanSeaNet, an oil spill hazard assessment is made in the Portuguese continental coast, based on the cumulative analysis of drift model simulations from previously detected spills using metocean model data, for a period between 2011–2016. Although this EWS doesn’t replace on-demand operational oil spill forecasting systems, it supports maritime authorities with a fast first-guess forecast solution, allowing:Anticipation of tactical response (including visual inspection of the spill) and mitigation of the pollution episode;A more effective identification of the pollution source, and in case of suspected illegal spill, earlier actions towards effective prosecution of the polluter;In the other hand, the hazard assessment generated is a valuable instrument for the development of efficient planning and prevention strategies. The EWS can be connected to any satellite-based detection service (inside or outside Europe) as long as the detected oil slicks are automatically distributed in a structured and standardized data format similar to CleanSeaNet.

Depuration of oil taint and muscle pigment from fish

1995 ◽  
Vol 31 (11) ◽  
pp. 23-28
Author(s):  
H. K. Davis
Keyword(s):  
Oil Spill ◽  
Diesel Fuel ◽  
Oil Spills ◽  
Clean Water ◽  
Flesh Colour ◽  
Farmed Salmon

Fish in the vicinity of large oil spills acquire characteristic flavour taints but little is known about their subsequent depuration. In this study, trout were exposed to 3 different tainting treatments with diesel fuel, then transferred to clean water, and samples assessed for taint at intervals for up to 15 weeks. The observed depuration periods were 2 to 17 times longer than others have reported but less than was needed for some of the farmed salmon affected by the Braer oil spill. Comparison of taint depuration times with changes in flesh pigmentation showed that flesh colour cannot be used to predict clearance of taint.

Detect Oil Spill in Offshore Facility using Convolutional Neural Network and Transfer Learning

2021 ◽  
Author(s):  
D. Raharjo
Keyword(s):  
Transfer Learning ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection System ◽  
The United States ◽  
Environmental Damage ◽  
Large Area ◽  
Artificial Eye ◽  
Deep Convolutional Neural Networks ◽  
Area Of Interest

The oil spill has a detrimental effect on the environment due to its pollution and long-term damage to sea wildlife. As the facility ages, the pipeline leak may increase as integrity reduces due to corrosion or erosion and worsens by minimal maintenance activity. To detect the oil leak, some assessments in the United States statistically found that leak detection system (LDS) effectiveness is less than 20% based on Aloqaily and Arafat (2018). Probably, LDS might not always give a satisfactory result to detect leaks and oil spills and may need to rely on other manual surveillance. Nevertheless, due to limited personnel and the large area of interest, oil spill usually goes undetected until local people and fishermen report it. In an oil spill case, having an early notification is crucial to limiting the leakage and improving mitigation time. To put it in perspective, one of the largest oil spills is the Deepwater Horizon, with an estimation of oil discharged around 4.1 – 4.9 million bbls, and legal fees cost up to 61.6 billion dollars. Looking at this number, we can estimate how important it is to stop oil spills at the very initial of occurrence to minimize environmental damage. This paper aims to exhibit a new approach in oil spill detection using deep convolutional neural networks and transfer learning. We develop an “artificial eye” to automatically classify the surrounding image and identify external manifestations to detect oil spills. We offer a concept upon how we leverage artificial intelligence to automatically classify a stream of the picture, whether it is an oil spill or not. Furthermore, we introduce an IoT and drone technology concept to maximize it to survey the pipeline path regularly. The image captured by these devices is then fed through a deep learning classifier model that decides whether the leak is present or not. By utilizing this technology, we hope to create automatic early notification if leakage occurs so that the oil spill combat team can cure the problem as fast as possible before the leak expands further.

DIGITAL MODEL OF THE OIL SPILL PROCESS OM THE EARTH’S SURFACE

2021 ◽  
Vol 115 ◽  
pp. 92-98
Author(s):  
Georgy Alekseevich Dorrer ◽  
◽  
Sergey Victorovich Yarovoy ◽  
Anton Yurievich Komarov ◽  
◽  
...  
Keyword(s):  
Soil Contamination ◽  
Oil Spill ◽  
Water Body ◽  
Oil Spills ◽  
Digital Model ◽  
Moving Grids ◽  
Adjacent Water ◽  
Significant Damage ◽  
Northern Regions

The possibility of creating a digital model of the process of oil spills with their penetration into the ground and the adjacent water body, which causes significant damage to the environment and the economy, especially in the northern regions, is being considered. An approach to solving this problem is proposed, based on the representation of the soil contamination area in the form of a set of flat layers, each of which is calculated by the method of moving grids.

scholarly journals AN APPROACH OF VICARIOUS CALIBRATION OF SENTINEL-2 SATELLITE MULTISPECTRAL IMAGE BASED ON SPECTRAL LIBRARY FOR MAPPING OIL SPILLS

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 117-121 ◽  
Author(s):  
J. J. A. Althawadi ◽  
M. Hashim
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Arabian Gulf ◽  
Region Growing ◽  
Multispectral Image ◽  
High Temporal Resolution ◽  
Random Errors ◽  
Spectral Library ◽  
Gulf Region ◽  
Sentinel 2

Abstract. Sentinel-2 satellite Multispectral Image (MSI) is one of the recent advancement of satellite optical imaging for detecting and tracking oil spills. MSI equipped with enhanced radiometric and spatial resolutions, apart from relatively high temporal resolution of every 5 days revisit capability. Both systematic errors of the geometric and radiometric of level 1 and 2 data were successfully treated before any data download for users’ levels applications. As such, leaving the random errors, crucially to be minimized to enable oil spill detection and tracking due to non-discernible absolute signatures of spills against the scene background and the look-alikes. The magnitude of these random errors’ minimization and the efficacy of the MSI absolute signatures within visible bands for oil spills is very crucial. However, it is rarely reported; in fact, it is a new issue to be addressed accordingly. The calibrating tool was created with oil spill spots revealed by the official authorities. Whereas, the spill pixels are identified in the corresponding pre-processed Sentinel MSI image using region growing segmentation algorithm. These spill pixels grown were analyzed against the RGB bands, logistically regressed against the oil spill via a spectral library of the crude oil type. Originated from Arabian Gulf region with an average film thickness of 0.5 to 4 mm; reporting a calibrating function in a form gain and bias corrections for RGB bands, respectively. The results indicated that calibrated MSI spill pixels have higher correlation (r2 > 0.85, p < 0.001). As the signature variations were used to formulate calibration matrices for spills identified from satellite images which can be used for processing of spill monitoring system.

scholarly journals HOW TO LIST A NEW PRODUCT ON THE NATIONAL OIL AND HAZARDOUS POLLUTION, SUBPART J PRODUCT SCHEDULE

2008 ◽  
Vol 2008 (1) ◽  
pp. 657-660 ◽  
Author(s):  
Leigh DeHaven ◽  
Rebecca Tirrell
Keyword(s):  
United States ◽  
Oil Spill ◽  
Oil Spills ◽  
Clean Water Act ◽  
New Products ◽  
New Product ◽  
The United States ◽  
Background Information ◽  
Clean Water ◽  
Hazardous Substance

ABSTRACT In light of the recently updated U.S. Coast Guard Regulation for Vessel Response Plans, which include requirements for vessel dispersant response capabilities and with the recent expansion of many U.S. Coastal Dispersant ?reauthorization Zones, it is important that both alternative oil spill chemical countermeasure product manufacturers and oil spill responders have an understanding of the National Oil and Hazardous Substance Pollution, Subpart J Product Schedule (NCP Product Schedule). The NCP Product Schedule lists alternative chemical countermeasures which may be used in oil spills in the United States if authorized by a Federal On-Scene Coordinator with consultation from the Regional Response Team including local Trustees. The product types currently listed on the NCP Product Schedule include dispersants, surface washing agents, bioremediation agents and miscellaneous oil spill control agents. Sorbents are also defined in the NCP Product Schedule, but they may or may not be required to be listed on the NCP Product Schedule depending upon their composition. The unauthorized use of oil spill chemical countermeasure products listed on the NCP Product Schedule on an on water oil spill is a violation of the Clean Water Act. In addition, the use of chemical products that are not listed on the NCP Product Schedule on oil spills on waters of the United States is also a violation of the Clean Water Act. Before a chemical countermeasure product is used during an oil spill in waters of the United States, new products must meet the data requirements stated in Subpart J of the NCP Product Schedule regulation (40 Code of Federal Regulations Part 300.900). The United States Environmental Protection Agency (EPA) maintains and updates the NCP Product Schedule. The EPA reviews the required data packages for new products and regularly updates the NCP Product Schedule and Technical Notebook on the NCP Product Schedule website (www.epa.gov/emergencies). This paper and poster will outline the steps to list a new product and provide background information on the NCP Product Schedule.

scholarly journals A method for modeling of the consequences of super-continuous accidents on oil production objects in the Arctic region

2018 ◽  
Vol 64 (4) ◽  
pp. 439-454
Author(s):  
S. N. Zatsepa ◽  
A. A. Ivchenko ◽  
V. V. Solbakov ◽  
V. V. Stanovoy
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Free Water ◽  
Arctic Region ◽  
Open Water ◽  
Simulation Method ◽  
The Arctic ◽  
Area Of Interest ◽  
Specially Protected Natural Areas ◽  
Total Oil

The elimination of the oil spill at the DWH (Deep-Water Horizon) well in the Gulf of Mexico took place in almost ideal hydrometeorological conditions, which did not create serious difficulties for the operation of the response forces and means. There is a problem of assessing the possible consequences of an accident of this scale in the Arctic conditions. The simulation method of a long-lasting oil spill in the ice infested region is considered. A new model for the spreading of an elementary spill (ES) is described. A total oil spill is constructed as superposition of a number of an elementary spill. Taking into account the transport of oil by drifting ice, the potential impact of spills on vulnerable areas in the sea and coasts can reach distance of hundreds and thousands of kilometers. The distribution of ES on the surface of ice-free water is limited by the lifetime, depending on the properties of oil and regional hydro-meteorological conditions and elongated by the duration of the ice capture. The paper presents examples of estimating the lifetime of an ongoing oil spill in open water conditions based on the analysis of wind conditions in the area of interest by long-term series of meteorological reanalysis. On the basis of the analysis, an efficient computational algorithm to estimate the probability of a long-lasting oil spills impact on specially protected natural areas is constructed.

scholarly journals MAPPING OIL SPILLS ON SEA SURFACE FROM SENTINEL 2 IMAGES USING PRINCIPAL COMPONENTS AND CATEGORICAL BOOSTING

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 33-38
Author(s):  
R. J. L. Argamosa ◽  
A. C. Blanco ◽  
R. B. Reyes
Keyword(s):  
Oil Spill ◽  
Principal Components ◽  
Oil Spills ◽  
Satellite Image ◽  
Gradient Boosting ◽  
Manila Bay ◽  
Boosting Algorithm ◽  
Explained Variance ◽  
Sentinel 2 ◽  
Band Image

Abstract. A large oil spill in Iloilo Straight that occurred on July 3, 2020, as well as a possible deliberate, small but frequent oil spill and surfactant contamination in Manila Bay, were mapped. The method employs the Sentinel 2-1C image, which is transformed into principal components to reveal the presence of oil spills and possibly surfactants. Additionally, a gradient boosting algorithm was trained to discriminate between pixels that were contaminated with oil and those that were not. The multi-band image with three principal components with a 99% cumulative explained variance ratio highlights the occurrence of an oil spill in Iloilo Straight. Further, the classified image produced by pixel-based classification clearly distinguishes between water and oil pixels in the said area. The methodology was applied to a Sentinel 2-1C image of Manila Bay, with pixels observed/identified as oil and classified as well. The highest density of supposedly oil-contaminated pixels (large or small but frequent) was observed on the eastern side of Manila Bay (Bataan). While there were no documented oil spills concurrent to the satellite image used, historical reports on the area indicate that the likelihood of an oil spill is extremely high due to the massive amount of shipping activity. Pixels supposedly contaminated by oil spills also occur in areas near ports where oil spills could occur as a result of ship operations. Pixels with the same properties as oil contamination are also visible in areas adjacent to fishponds and aquaculture, where phytoplankton and fish contribute to surfactant contamination.

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