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.

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.

scholarly journals APPLICATION OF A GIS BASED APPROACH TO ASSESS THE ENVIRONMENTAL IMPACTS OF THE MV WAKASHIO OIL SPILL IN THE SOUTHEAST OF MAURITIUS

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Reshma Sunkur ◽  
Chandradeo Bokhoree
Keyword(s):  
Environmental Impacts ◽  
Oil Spill ◽  
Oil Spills ◽  
Satellite Image ◽  
Marine Oil ◽  
Environmental Disasters ◽  
Island Communities ◽  
Short And Long Term ◽  
Marine Oil Spills ◽  
Sar Imagery

Marine oil spills are regarded as one of the most threatening environmental disasters that can have serious environmental and socio-economic impacts. For islands like Mauritius, such oil spills can have severe repercussions as island communities depend almost entirely on their coastal and marine resources. The MV Wakashio grounded on the coral reef on the south east coast of Mauritius on July 25th 2020, spilling 1000 tons of oil into its clear waters on August 06th 2020. It was the first time the island was faced with such a disaster and in this respect, this study aimed to use a GIS based approach to assess the environmental impacts of the Wakashio oil spill and demonstrate its usefulness in monitoring marine oil spills. SAR imagery was acquired from the Copernicus Platform and ArcGIS was used to process the images. An oil spill map was created using a SAR image dated August 10th 2020. GPS coordinates of the affected sites were recorded and overlaid on a terrain/road network map of Mauritius generated from layers of vector data obtained through the DIVA-GIS portal. The oil spill was mapped on the satellite image using ArcGIS and a vector map of the affected regions was created. From these maps, the short and long term impacts on the environments (marine waters, mangroves, coasts, biodiversity) were examined. This study concludes that GIS is an effective, inexpensive tool that coastal nations around the world, including Mauritius, can use to support management and decision making regarding oil spill preparedness and monitoring as well as disaster management

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 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.

ECOLOGICAL AND GEOMORPHOLOGICAL ASSESSMENT OF THE VULNERABILITY OF THE COASTS OF THE KARA SEA TO THE OIL SPILL

2017 ◽  
Author(s):  
Alexander Ermolov ◽  
Alexander Ermolov
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
International System ◽  
Kara Sea ◽  
Coastal Protection ◽  
Sensitivity Index ◽  
Oil Spill Response ◽  
Expert Assessments ◽  
International Systems

International experience of oil spill response in the sea defines the priority of coastal protection and the need to identify as most valuable in ecological terms and the most vulnerable areas. Methodological approaches to the assessing the vulnerability of Arctic coasts to oil spills based on international systems of Environmental Sensitivity Index (ESI) and geomorphological zoning are considered in the article. The comprehensive environmental and geomorphological approach allowed us to form the morphodynamic basis for the classification of seacoasts and try to adapt the international system of indexes to the shores of the Kara Sea taking into account the specific natural conditions. This work has improved the expert assessments of the vulnerability and resilience of the seacoasts.

An assessment of toxic metals content in the marine sediments of the Shuaiba Industrial Area, Kuwait, after the oil spill during the Gulf War

1996 ◽  
Vol 34 (7-8) ◽  
pp. 203-210 ◽  
Author(s):  
S. Al-Muzaini ◽  
P. G. Jacob
Keyword(s):  
Oil Spill ◽  
Marine Sediment ◽  
Oil Spills ◽  
Industrial Area ◽  
Gulf War ◽  
Oil Wells ◽  
Environmental Damage ◽  
Base Line ◽  
Sampling Locations ◽  
Very High

A field study was carried out involving seven fixed sampling stations. The sampling locations were selected to cover the distribution of pollutants in the Shuaiba Industrial Area (SIA), which was contaminated with oil released from oil wells and broken pipelines and with a vast amount of burnt and unburnt crude oil from the burning and gushing oil wells. The samples were collected biweekly between July 1993 and July 1994. The concentrations of V, Ni, Cr, Cd and Pb were determined and compared with the previously collected baseline data to assess the degree of environmental damage caused due to the oil spills during the Gulf war. The average concentrations (mg/kg) of various elements in the marine sediment were 17.3 for V, 30.8 for Ni, 55.5 for Cr, 0.02 for Cd and 1.95 for Pb. Our results show that even after the heavy spillage of oil, associated metal concentrations were not very high compared with previously reported base line values.

scholarly journals Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Osman Mamun ◽  
Madison Wenzlick ◽  
Arun Sathanur ◽  
Jeffrey Hawk ◽  
Ram Devanathan
Keyword(s):  
Pearson Correlation ◽  
Rupture Life ◽  
Model Performance ◽  
Austenitic Stainless Steels ◽  
Generative Model ◽  
Austenitic Steels ◽  
Gradient Boosting ◽  
Variational Autoencoder ◽  
Feature Importance ◽  
Boosting Algorithm

AbstractThe Larson–Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high-temperature applications; however, poor generalizability and dependence on the constant C often result in sub-optimal performance. In this work, we show that the direct rupture life parameterization without intermediate LMP parameterization, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pearson correlation coefficient >0.9 for 9–12% Cr and >0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Finally, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9–12% Cr ferritic–martensitic alloys and austenitic stainless steel datasets.

scholarly journals Next-Generation Smart Response Web (NG-SRW): An Operational Spatial Decision Support System for Maritime Oil Spill Emergency Response in the Gulf of Finland (Baltic Sea)

2021 ◽  
Vol 13 (12) ◽  
pp. 6585
Author(s):  
Mihhail Fetissov ◽  
Robert Aps ◽  
Floris Goerlandt ◽  
Holger Jänes ◽  
Jonne Kotta ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Oil Spill ◽  
Situational Awareness ◽  
Oil Spills ◽  
Relevant Information ◽  
Response Strategy ◽  
Spatial Decision Support System ◽  
Next Generation ◽  
Oil Spill Response ◽  
The Baltic

The Baltic Sea is a unique and sensitive brackish-water ecosystem vulnerable to damage from shipping activities. Despite high levels of maritime safety in the area, there is a continued risk of oil spills and associated harmful environmental impacts. Achieving common situational awareness between oil spill response decision makers and other actors, such as merchant vessel and Vessel Traffic Service center operators, is an important step to minimizing detrimental effects. This paper presents the Next-Generation Smart Response Web (NG-SRW), a web-based application to aid decision making concerning oil spill response. This tool aims to provide, dynamically and interactively, relevant information on oil spills. By integrating the analysis and visualization of dynamic spill features with the sensitivity of environmental elements and value of human uses, the benefits of potential response actions can be compared, helping to develop an appropriate response strategy. The oil spill process simulation enables the response authorities to judge better the complexity and dynamic behavior of the systems and processes behind the potential environmental impact assessment and thereby better control the oil combat action.

An intelligent evolutionary extreme gradient boosting algorithm development for modeling scour depths under submerged weir

2021 ◽  
Author(s):  
Hai Tao ◽  
Maria Habib ◽  
Ibrahim Aljarah ◽  
Hossam Faris ◽  
Haitham Abdulmohsin Afan ◽  
...  
Keyword(s):  
Gradient Boosting ◽  
Algorithm Development ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithm

scholarly journals SAR Oil Spill Detection System through Random Forest Classifiers

2021 ◽  
Vol 13 (11) ◽  
pp. 2044
Author(s):  
Marcos R. A. Conceição ◽  
Luis F. F. Mendonça ◽  
Carlos A. D. Lentini ◽  
André T. C. Lima ◽  
José M. Lopes ◽  
...  
Keyword(s):  
Random Forest ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection System ◽  
Feature Space ◽  
Dark Spot ◽  
Sar Image ◽  
Extensive Search ◽  
Gradient Based ◽  
Biological Films

A set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset. The first random forest model is an ocean SAR image classifier where the labeling inputs were oil spills, biological films, rain cells, low wind regions, clean sea surface, ships, and terrain. The second one was a SAR image oil detector named “Radar Image Oil Spill Seeker (RIOSS)”, which classified oil-like targets. An optimized feature space to serve as input to such classification models, both in terms of variance and computational efficiency, was developed. It involved an extensive search from 42 image attribute definitions based on their correlations and classifier-based importance estimative. This number included statistics, shape, fractal geometry, texture, and gradient-based attributes. Mixed adaptive thresholding was performed to calculate some of the features studied, returning consistent dark spot segmentation results. The selected attributes were also related to the imaged phenomena’s physical aspects. This process helped us apply the attributes to a random forest, increasing our algorithm’s accuracy up to 90% and its ability to generate even more reliable results.

Sign in / Sign up

Export Citation Format

Share Document