Review of oil spill remote sensing

Adaptive Enhancement of X-Band Marine Radar Imagery to Detect Oil Spill Segments

Oil spills generate a large cost in environmental and economic terms. Their identification plays an important role in oil-spill response. We propose an oil spill detection method with improved adaptive enhancement on X-band marine radar systems. The radar images used in this paper were acquired on 21 July 2010, from the teaching-training ship “YUKUN” of the Dalian Maritime University. According to the shape characteristic of co-channel interference, two convolutional filters are used to detect the location of the interference, followed by a mean filter to erase the interference. Small objects, such as bright speckles, are taken as a mask in the radar image and improved by the Fields-of-Experts model. The region marked by strong reflected signals from the sea’s surface is selected to identify oil spills. The selected region is subject to improved adaptive enhancement designed based on features of radar images. With the proposed adaptive enhancement technique, calculated oil spill detection is comparable to visual interpretation in accuracy.

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.

Hyperspectral and Radar Airborne Imagery over Controlled Release of Oil at Sea

Remote sensing techniques are commonly used by Oil and Gas companies to monitor hydrocarbon on the ocean surface. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as thickness and composition of the detected oil, which is critical for both exploration purposes and efficient cleanup operations. Today, state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI (Système Expérimental de Télédection Hyperfréquence Imageur), the airborne system developed by ONERA (the French Aerospace Lab), during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this dataset lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the EM spectrum. Specific processing techniques have been developed to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows estimating slick surface properties such as the age of the emulsion released at sea, the spatial abundance of oil and the relative concentration of hydrocarbons remaining on the sea surface.

ZnO Quantum Dot Decorated Zn2SnO4 Nanowire Heterojunction Photodetectors with Drastic Performance Enhancement and Flexible Ultraviolet Image Sensors

Here we report the fabrication of high-performance ultraviolet photodetectors based on a heterojunction device structure in which ZnO quantum dots were used to decorate Zn₂SnO₄ nanowires. Systematic investigations have shown their ultrahigh light-to-dark current ratio (up to 6.8 × 10⁴), specific detectivity (up to 9.0 × 10¹⁷ Jones), photoconductive gain (up to 1.1 × 10⁷), fast response, and excellent stability. Compared with a pristine Zn₂SnO₄ nanowire, a quantum dot decorated nanowire demonstrated about 10 times higher photocurrent and responsivity. Device physics modeling showed that their high performance originates from the rational energy band engineering, which allows efficient separation of electron-hole pairs at the interfaces between ZnO quantum dots and a Zn₂SnO₄ nanowire. As a result of band engineering, holes migrate to ZnO quantum dots, which increases electron concentration and lifetime in the nanowire conduction channel, leading to significantly improved photoresponse. The enhancement mechanism found in this work can also be used to guide the design of high-performance photodetectors based on other nanomaterials. Furthermore, flexible ultraviolet photodetectors were fabricated and integrated into a 10 × 10 device array, which constitutes a high-performance flexible ultraviolet image sensor. These intriguing results suggest that the band alignment engineering on nanowires can be rationally achieved using compound semiconductor quantum dots. This can lead to largely improved device performance. Particularly for ZnO quantum dot decorated Zn₂SnO₄ nanowires, these decorated nanowires may find broad applications in future flexible and wearable electronics.

Measurement of thickness of highly inhomogeneous crude oil slicks

As part of the Deepwater Horizon toxicity testing program, a number of laboratories generated oil slicks in the laboratory to study potential toxic effects of these oil slicks on aquatic organisms. Understanding the details of how these slicks affect aquatic organisms requires careful correlation between slick thickness and the observed detrimental effects. Estimating oil film thickness on water can be challenging since the traditional color-based technique used in the field is very imprecise. Also, as we demonstrate here, the films formed on the water surface are highly nonuniform on a microscale level, and thus uniform thin film thickness measurement techniques based on optical interference do not work. In this paper, we present a method that estimates the local thickness of weathered oil slicks formed on artificial seawater using light transmission and Beer-Lambert's law. Here, we demonstrate results of careful calibration together with the actual thickness estimation. Due to the heterogeneity of the slicks formed, we present slick thickness as a range of thicknesses collected from multiple points within the oil slick. In all the experiments we used oil samples provided by the Natural Resource Damage Assessment toxicity testing program for the Deepwater Horizon oil spill. Therefore, this study has an important practical value and successfully addresses unique challenges related to measurements involving complex, viscous, paste-like heterogeneous substances such as weathered crude oil.

Synergistic use of an oil drift model and remote sensing observations for oil spill monitoring

In case of oil spills due to disasters, one of the environmental concerns is the oil trajectories and spatial distribution. To meet these new challenges, spill response plans need to be upgraded. An important component of such a plan would be models able to simulate the behaviour of oil in terms of trajectories and spatial distribution, if accidentally released, in deep water. All these models need to be calibrated with independent observations. The aim of the present paper is to demonstrate that significant support to oil slick monitoring can be obtained by the synergistic use of oil drift models and remote sensing observations. Based on transport properties and weathering processes, oil drift models can indeed predict the fate of spilled oil under the action of water current velocity and wind in terms of oil position, concentration and thickness distribution. The oil spill event that occurred on 31 May 2003 in the Baltic Sea offshore the Swedish and Danish coasts is considered a case study with the aim of producing three-dimensional models of sea circulation and oil contaminant transport. The High-Resolution Limited Area Model (HIRLAM) is used for atmospheric forcing. The results of the numerical modelling of current speed and water surface elevation data are validated by measurements carried out in Kalmarsund, Simrishamn and Kungsholmsfort stations over a period of 18 days and 17 h. The oil spill model uses the current field obtained from a circulation model. Near-infrared (NIR) satellite images were compared with numerical simulations. The simulation was able to predict both the oil spill trajectories of the observed slick and thickness distribution. Therefore, this work shows how oil drift modelling and remotely sensed data can provide the right synergy to reproduce the timing and transport of the oil and to get reliable estimates of thicknesses of spilled oil to prepare an emergency plan and to assess the magnitude of risk involved in case of oil spills due to disaster.

Offshore platform sourced pollution monitoring using space-borne fully polarimetric C and X band synthetic aperture radar

Use of polarimetric SAR data for offshore pollution monitoring is relatively new and shows great potential for operational offshore platform monitoring. This paper describes the development of an automated oil spill detection chain for operational purposes based on C-band (RADARSAT-2) and X-band (TerraSAR-X) fully polarimetric images, wherein we use polarimetric features to characterize oil spills and look-alikes. Numbers of near coincident TerraSAR-X and RADARSAT-2 images have been acquired over offshore platforms. Ten polarimetric feature parameters were extracted from different types of oil and 'look-alike' spots and divided into training and validation dataset. Extracted features were then used to develop a pixel based Artificial Neural Network classifier. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spill and look-alike spots. Polarimetric features such as Scattering Diversity, Surface Scattering Fraction and Span proved to be most suitable for operational services.

Satellite data lift the veil on offshore platforms in the South China Sea

Oil and gas exploration in the South China Sea (SCS) has garnered global attention recently; however, uncertainty regarding the accurate number of offshore platforms in the SCS, let alone their detailed spatial distribution and dynamic change, may lead to significant misjudgment of the true status of offshore hydrocarbon production in the region. Using both fresh and archived space-borne images with multiple resolutions, we enumerated the number, distribution, and annual rate of increase of offshore platforms across the SCS. Our results show that: (1) a total of 1082 platforms are present in the SCS, mainly located in shallow-water; and (2) offshore oil/gas exploitation in the SCS is increasing in intensity and advancing from shallow to deep water, and even to ultra-deep-water. Nevertheless, our findings suggest that oil and gas exploration in the SCS may have been over-estimated by one-third in previous reports. However, this overestimation does not imply any amelioration of the potential for future maritime disputes, since the rate of increase of platforms in disputed waters is twice that in undisputed waters.

A New Approach of Oil Spill Detection Using Time-Resolved LIF Combined with Parallel Factors Analysis for Laser Remote Sensing

In hope of developing a method for oil spill detection in laser remote sensing, a series of refined and crude oil samples were investigated using time-resolved fluorescence in conjunction with parallel factors analysis (PARAFAC). The time resolved emission spectra of those investigated samples were taken by a laser remote sensing system on a laboratory basis with a detection distance of 5 m. Based on the intensity-normalized spectra, both refined and crude oil samples were well classified without overlapping, by the approach of PARAFAC with four parallel factors. Principle component analysis (PCA) has also been operated as a comparison. It turned out that PCA operated well in classification of broad oil type categories, but with severe overlapping among the crude oil samples from different oil wells. Apart from the high correct identification rate, PARAFAC has also real-time capabilities, which is an obvious advantage especially in field applications. The obtained results suggested that the approach of time-resolved fluorescence combined with PARAFAC would be potentially applicable in oil spill field detection and identification.

The effect of dispersed Petrobaltic oil droplet size on photosynthetically active radiation in marine environment

Oil pollution in seawater, primarily visible on sea surface, becomes dispersed as an effect of wave mixing as well as chemical dispersant treatment, and forms spherical oil droplets. In this study, we examined the influence of oil droplet size of highly dispersed Petrobaltic crude on the underwater visible light flux and the inherent optical properties (IOPs) of seawater, including absorption, scattering, backscattering and attenuation coefficients. On the basis of measured data and Mie theory, we calculated the IOPs of dispersed Petrobaltic crude oil in constant concentration, but different log-normal size distributions. We also performed a radiative transfer analysis, in order to evaluate the influence on the downwelling irradiance Ed, remote sensing reflectance Rrs and diffuse reflectance R, using in situ data from the Baltic Sea. We found that during dispersion, there occurs a boundary size distribution characterized by a peak diameter d0  = 0.3 μm causing a maximum E d increase of 40% within 0.5-m depth, and the maximum Ed decrease of 100% at depths below 5 m. Moreover, we showed that the impact of size distribution on the "blue to green" ratios of Rrs and R varies from 24% increase to 27% decrease at the same crude oil concentration.

The management in response to marine oil spill from ships in China: A systematic review

Historical trends about marine ship-source oil spill incidents from 1990 to 2010 in China were analyzed, and it provided an overview of the status quo of China's management in response to marine oil spill from ships. The Chinese government has issued a series of laws on marine environmental protection since 1982, and promulgated many regulations to prevent and tackle ship-source oil spill. At present, the oil spill emergency response system established in China has five levels: the national level, sea level, provincial level, port level, and ship level. China has demonstrated its ability to control and remove small-scale oil spill from ships in port area and near-shore coastal waters, and also paid attention to related research and development projects. Although China has made significant progress in managing shipping oil spill, challenges still exist, including strengthening oil spill emergency cooperation, enhancing China's response capability, and improving relevant research and development projects.

Utilization of a genetic algorithm for the automatic detection of oil spill from RADARSAT-2 SAR satellite data

In this work, a genetic algorithm is applied for the automatic detection of oil spills. The procedure is implemented using sequences from RADARSAT-2 SAR ScanSAR Narrow single-beam data acquired in the Gulf of Mexico. The study demonstrates that the implementation of crossover allows for the generation of an accurate oil spill pattern. This conclusion is confirmed by the receiver-operating characteristic (ROC) curve. The ROC curve indicates that the existence of oil slick footprints can be identified using the area between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills, and the ScanSAR Narrow single-beam mode serves as an excellent sensor for oil spill detection and survey.