Long term monitoring of oil spills in European seas

Ferraro, Guido; Meyer-Roux, S.; Oliver, Muellenhoff; Pavliha, Marko; Švetak, Jelenko; Tarchi, D.; Topouzelis, Konstantinos

doi:10.1080/01431160802339464

Cited by 72 publications

(24 citation statements)

References 7 publications

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“…Fig. 6 Areas impacted by anthropogenic activities like maritime transport and consequent oil pollution, derived from a climatological analysis of SAR data on surface slicks (after Ferraro et al 2008) While a combination of environmental and geographical traits can be enough to identify the main eco-regions of the Mediterranean Sea, it should not be forgotten that a true ecosystem approach to environmental management should include also the human factor, among the indicators to be considered. As an example of an additional RS contribution to this goal, Fig.…”

Section: Resultsmentioning

confidence: 99%

Toward an ecosystem approach to ICM: assessing ecological provinces at sea by remote sensing

Vittorio

2009

J Coast Conserv

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The ecosystem approach requires that all elements of an ecosystem, and their mutual interactions, be taken into consideration in any management effort. The selection of suitable geographical units, where this approach can be taken, requires the assessment of ecological provinces, characterized by a coherent set of environmental traits. The marine side of coastal zones, where the interaction between atmosphere, land and sea is not bounded by evident geographic markers, represents a critical factor in this assessment. A coastal province can be defined by physical setting, but also by its bio-geo-chemical features, ideally on the basis of synoptic remote sensing data, collected at space/ time scales not accessible by other means. Classifications based on indicators such as temperature, wind speed and chlorophyll-like pigments, demonstrate the identification of potential ecological provinces in the Mediterranean Sea. The results suggest remote sensing as the ideal tool to set up the basis for an ecosystem approach to the management of each province.

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Section: Resultsmentioning

confidence: 99%

Toward an ecosystem approach to ICM: assessing ecological provinces at sea by remote sensing

Vittorio

2009

J Coast Conserv

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“…Sizes of layers [10,8,6, 2] Activation function Sigmoid Learning rate 1 Number of epochs 100 Batch size 100 Table 3. Parameter settings of the support vector machine (SVM).…”

Section: Parameter Valuementioning

confidence: 99%

“…Type [10,8,6, 2] Activation function of the neural network Sigmoid Learning rate when making fine-tuning 3 Number of epochs when making fine-tuning 100 Batch size when making fine-tuning 100 Table 5. Parameter settings of the deep belief network (DBN).…”

Section: Parameter Valuementioning

confidence: 99%

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Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images

Chen

Sun

et al. 2017

Applied Sciences

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Featured Application: Using polarimetric synthetic aperture radar (SAR) remote sensing to detect and classify sea surface oil spills, for the early warning and monitoring of marine oil spill pollution.Abstract: Polarimetric synthetic aperture radar (SAR) remote sensing provides an outstanding tool in oil spill detection and classification, for its advantages in distinguishing mineral oil and biogenic lookalikes. Various features can be extracted from polarimetric SAR data. The large number and correlated nature of polarimetric SAR features make the selection and optimization of these features impact on the performance of oil spill classification algorithms. In this paper, deep learning algorithms such as the stacked autoencoder (SAE) and deep belief network (DBN) are applied to optimize the polarimetric feature sets and reduce the feature dimension through layer-wise unsupervised pre-training. An experiment was conducted on RADARSAT-2 quad-polarimetric SAR image acquired during the Norwegian oil-on-water exercise of 2011, in which verified mineral, emulsions, and biogenic slicks were analyzed. The results show that oil spill classification achieved by deep networks outperformed both support vector machine (SVM) and traditional artificial neural networks (ANN) with similar parameter settings, especially when the number of training data samples is limited.

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“…This paper addresses these issues by proposing an integrated and inter-operable information system based on acquired data from a variety of electronic sensors. In particular, the proposed Marine Information System (MIS) integrates classical SAR satellite processed data [3], with multi-spectral aerial data, as well as environmental data from in situ monitoring stations (e.g., placed on static and floating buoys) [4], and dynamic data acquired from in situ mobile sources (e.g., Autonomous Underwater Vehicles (AUVs) equipped with sensing modules). The designed MIS provides a collection of environmental Decision Support Services (EDSS), for (i) automatically monitoring the overall environmental situation, (ii) quantitatively representing environmental risk factors and (iii) proactively notifying events that deserve the consideration of end users.…”

Section: Introductionmentioning

confidence: 99%

Environmental Monitoring Integrated with a Proactive Marine Information System

Moroni

Pieri

Tampucci

et al. 2018

International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)

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Abstract:In the framework of environmental monitoring, the remote detection and monitoring of oil spills at sea is an important ability due to the high demand of oil based products. This situation causes, that shipping routes are very crowded and the likelihood of oil slicks occurring is also increasing. In this paper we propose a fully integrated and inter-operable information system which can act as a valuable monitoring tool. Such a marine information system is able to monitor ship traffic and marine operators by integrating heterogeneous signals and data through sensing capabilities from a variety of electronic sensors, along with geo-positioning tools, and through a communication infrastructure. This system is able to transfer the integrated data, freely and seamlessly, between different elements of the system itself (and their users). The system also provides a set of decision support services capable of performing functionalities which can act as a support for decision makers.

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Long term monitoring of oil spills in European seas

Cited by 72 publications

References 7 publications

Toward an ecosystem approach to ICM: assessing ecological provinces at sea by remote sensing

Toward an ecosystem approach to ICM: assessing ecological provinces at sea by remote sensing

Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images

Environmental Monitoring Integrated with a Proactive Marine Information System

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