Oil Spill Remote Sensing

Fingas, Mervin F.; Brown, Carl E.

doi:10.1007/978-1-4614-5684-1_15

Cited by 23 publications

(14 citation statements)

References 105 publications

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“…In parallel, state-of-the-art oil spill models use satellite SAR images/data to identify potential oil slicks and implement spill and drifter surveillance to improve slick forecasting. In detail, existing oil spill remote sensing techniques are presented in the review papers of Fingas and Brown [183,184]. The attention of the scientific community has been focused on enhancing 4D predictions by simulating oil spills backward in time to track the slick to its source [145].…”

Section: The New Generation Of Oil Spill Modelsmentioning

confidence: 99%

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Keramea

Spanoudaki

Zodiatis

et al. 2021

JMSE

145

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Several oil spill simulation models exist in the literature, which are used worldwide to simulate the evolution of an oil slick created from marine traffic, petroleum production, or other sources. These models may range from simple parametric calculations to advanced, new-generation, operational, three-dimensional numerical models, coupled to meteorological, hydrodynamic, and wave models, forecasting in high-resolution and with high precision the transport and fate of oil. This study presents a review of the transport and oil weathering processes and their parameterization and critically examines eighteen state-of-the-art oil spill models in terms of their capacity (a) to simulate these processes, (b) to consider oil released from surface or submerged sources, (c) to assimilate real-time field data for model initiation and forcing, and (d) to assess uncertainty in the produced predictions. Based on our review, the most common oil weathering processes involved are spreading, advection, diffusion, evaporation, emulsification, and dispersion. The majority of existing oil spill models do not consider significant physical processes, such as oil dissolution, photo-oxidation, biodegradation, and vertical mixing. Moreover, timely response to oil spills is lacking in the new generation of oil spill models. Further improvements in oil spill modeling should emphasize more comprehensive parametrization of oil dissolution, biodegradation, entrainment, and prediction of oil particles size distribution following wave action and well blow outs.

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Section: The New Generation Of Oil Spill Modelsmentioning

confidence: 99%

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Keramea

Spanoudaki

Zodiatis

et al. 2021

JMSE

145

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“…Although other reviews have been done on remote sensing for marine oil spill monitoring and management, such as those by Brekke and Solberg [30], Fingas and Brown [76], Robbe et al [207], most only consider satellite remote sensing without assessing airborne remote sensing. Fingas and Brown [25], Topouzelis [37], Jha et al [58], Leifer et al [60], Fingas and Brown [208], and Fingas and Brown [209] reviewed both airborne and satellite sensors for marine oil spill monitoring but the present review presents a more comprehensive comparison of both sensors. In addition, existing studies do not include an updated review of various automated techniques, particularly emerging Artificial intelligence approaches for discriminating, detecting, and classifying oil spills from false positive elements in remote sensing imageries, which is implemented in this study.…”

Section: Oil Spill Trajectory Modeling For Vulnerability Assessmentmentioning

confidence: 99%

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

Yekeen

Balogun

2020

Remote Sensing

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Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

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“…Oil spills are one of the most common contaminants in marine environments that seriously affect all aspects of marine ecology and transport [1,2]. Early determination and characterization of oil spills in terms of oil species, oil film thickness, and oil distribution can significantly facilitate decision-making for effective emergency disposal and cleanup programs [3,4].…”

Section: Introductionmentioning

confidence: 99%

Species Identification and Effects of Aromatic Hydrocarbons on the Fluorescence Spectra of Different Oil Samples in Seawater

Hou

et al. 2021

Journal of Spectroscopy

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The identification of oil species and their characteristics in oil spills is particularly important for their efficient disposal. Since aromatic hydrocarbons (AHs) with various characteristics are the major fluorescent components of oils in seawater, they can be used to detect different oil species in seawater. Here, we developed a composition method using the fluorescence spectra of eight AH categories analyzed by gas chromatography-mass spectrometry (GC-MS) to evaluate the contribution of AHs to the total fluorescence of six different oil species. Qualitative and quantitative difference analysis of the experimental and composite fluorescence spectra was performed based on the redshift of the main peak position, the fluorescence intensity distance, and the generalized included angle cosine, while correlation analysis was used to establish the relationship between the different fluorescence spectral parameters and the American Petroleum Institute gravity and viscosity of the oil species. The fluorescence spectra recorded for heavy oil samples indicated a reduction in the fluorescence signal of fluorene series and an increase in the contribution of acenaphthene and pyrene series, indicating that the currently developed composition method by fluorescence spectroscopy combined with GC-MS can be used to distinguish and identify oil species in seawater samples.

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Oil Spill Remote Sensing

Cited by 23 publications

References 105 publications

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

Species Identification and Effects of Aromatic Hydrocarbons on the Fluorescence Spectra of Different Oil Samples in Seawater

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