Optical and Radar Remote Sensing and Contamination Probability Modelling for the Advanced Quantitative Risk Assessment of Marine Petroleum and Gas Industry

Bayramov, Emil; Bayramov, Rafael; Aliyeva, Saida

doi:10.1016/j.ifacol.2018.11.240

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Increased activities on the coast and at sea, such as offshore oil and gas platforms, transport and shipping, as well as urban runoff, have increased the risk of pollution and contamination from synthetic compounds, hydrocarbons (oil and gas), and metals. RS data have played an important role in assessing oil spills, which are a major environmental concern that can have dramatic consequences [211][212][213]. SAR (SRS) data can be used to detect oil spills; nevertheless, they are limited in their capacity to separate mineral oil from natural, biogenic films [213], because of noise in the signal.…”

Section: Input Of Synthetic Compounds: Hydrocarbonsmentioning

confidence: 99%

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

et al. 2020

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Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.

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Section: Input Of Synthetic Compounds: Hydrocarbonsmentioning

confidence: 99%

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

et al. 2020

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“…They have a fairly large area and, due to the smoothing of short wind waves and the formation of so-called slicks, are clearly distinguishable on radar images [11,15,16]. Methods for classifying slicks formed by petroleum products or caused by natural surfactants are being actively developed [17][18][19][20][21][22][23][24]. However, slicks formed by underwater bubble streams have their own differences, which should be taken into account when developing a method for identifying them.…”

Section: Problem Statementmentioning

confidence: 99%

Investigation of the possibility of remote detection of gas outlets in the sea using X-band radar

Smirnova¹,

Kapustin²,

Ermoshkin³

2020

Sovr. Probl. DZZ Kosm.

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“…While many semi-/automatic oil spill extraction algorithms have been developed based on SAR [27][28][29][30][31][32], there are few semi-/automatic oil spill detection algorithms working for optical imagery. Currently, oil spill extent detection in optical remote sensing images largely relies on visual interpretation and manual delineation [33][34][35], which require extensive experience and expertise; and the derived oil spill map is also subject to interpretation. Other than visual interpretation, methods including pixel-based indices and deep learning have been developed to identify and classify oil slicks based on optical imagery [36][37][38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Detection of Massive Oil Spills in Sun Glint Optical Imagery through Super-Pixel Segmentation

Sun

Zhao

et al. 2022

JMSE

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Large volumes of crude oil accidentally released into the sea may cause irreversible adverse impacts on marine and coastal environments. Large swath optical imagery, acquired using platforms such as the moderate-resolution imaging spectroradiometer (MODIS), is frequently used for massive oil spill detection, attributing to its large coverage and short global revisit, providing rich data for oil spill monitoring. The aim of this study was to develop a suitable approach for massive oil spill detection in sun glint optical imagery. Specifically, preprocessing procedures were conducted to mitigate the inhomogeneous light field over the spilled area caused by sun glint, enhance the target boundary contrast, and maintain the internal homogeneity within the target. The image was then segmented into super-pixels based on a simple linear clustering method with similar characteristics of color, brightness, and texture. The neighborhood super-pixels were merged into target objects through the region adjacency graph method based on the Euclidean distance of their colors with an adaptive termination threshold. Oil slicks from the generated bright/dark objects were discriminated through a decision tree with parameters based on spectral and spatial characteristics. The proposed approach was applied to oil spill detection in MODIS images acquired during the Montara oil spill in 2009, with an overall extraction precision of 0.8, recall of 0.838, and F1-score of 0.818. Such an approach is expected to provide timely and accurate oil spill detection for disaster emergency response and ecological impact assessment.

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Optical and Radar Remote Sensing and Contamination Probability Modelling for the Advanced Quantitative Risk Assessment of Marine Petroleum and Gas Industry

Cited by 4 publications

References 2 publications

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

Investigation of the possibility of remote detection of gas outlets in the sea using X-band radar

Detection of Massive Oil Spills in Sun Glint Optical Imagery through Super-Pixel Segmentation

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