Oil slick volume estimation from combined use of airborne hyperspectral and pool experiment data

International audienceTo date, estimating oil thickness on the sea surface remains a challenge in most cases. When oil thickness estimation using optical data is limited by the absorption properties of the target, a solution consists in combining experimental and airborne hyperspectral data. We developed a method to identify thickness classes from hyperspectral data which, combined with realistic thickness values derived from a pool experiment, allows to estimate slick volume. Hyperspectral images of the same oil emulsion were acquired over a pool and at sea, under real conditions. From the pool data, we derived two classes: the sheen and the thick pixels, along with their respective thickness. These classes are then identified on the airborne images acquired during the NOFO campaign by generating a detection mask and using two classification approaches based on spectral indices. The proposed method allows to correctly identify the two thickness classes and, combined with the data from the pool experiment, provides a total slick volume larger than the one derived for the Bonn Agreement Oil Appearance Code

Introduction to oil quantification on sea surface from microwaves polarimetric SAR measurements

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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

WIND-WAVE-POOL EXPERIMENTAL DATA OF CONTAMINATED SEAWATER SURFACES: STATISTICAL SURFACES AND RADAR BACKSCATTERED FIELD

International audienceThis paper depicts an experiment on contaminated sea watersurfaces with measurements of surface geometrical propertiesand radar backscattering estimations from X- to K-band.These measurements are carried out in a sea water pool, thewind waves are generated thanks to two fans. The observationsare realized by a radar system operating in near-fieldand downwind configuration for various azimuth and incidenceangles and also by seven high-resolution capacitancewave probes. Three pollutants are measured, colza oil, crudeoil and gas oil. Both of them reduce the surface radar crosssection compared to clean sea water configuration. Colza andgas oils get a similar electromagnetic behaviour and the radarcross section of the sea water surface contaminated by crudeoil is greater than the one considering a colza oil spill

Quaternary tectonic and climate changes at the origin of travertine and calcrete in the eastern Betics (Almería region, SE Spain)

International audienc

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

Potentiel HSE de détection, caractérisation et quantification de nappes en offshore grâce à l'imagerie radar ou optique

International audienceDuring five years, in the frame of the NAOMI (New Advanced Observation Method Integration) research project, Total and ONERA have worked on radar and optical imagery to detect, characterize and quantify slicks at sea. Laboratory and pool measurements, physical modelling and offshore experiments have been combined to fully understand the signal collected over slick-covered area. As the measured signal is analytically expressed according to the geophysical parameters of the imaged slick, it enables to fully monitor the ocean surface: is a slick present? What kind of slick is it (extremely thin or not)? Is it a known product (existing in the data base)? Can the thickness be probed by the used of optical or radar device? What is the slick volume? In the Health Security and Environment (HSE) context, an exhaustive measurement campaign can be done in order to create a data base with hydrocarbon or hydrocarbon emulsion signatures, extinction coefficients, skin depths, minimum thicknesses perceptible thanks to extinction and thickness values. Thus, it offers more processing options in the optic branch of the tool to monitor the slick. Depending on the available data, optical and/or radar imagery, the capability of slick detection, characterization and quantification will be presented. After a recall of the HSE specificity, the paper will give an overview of the main features of the input data that is to say SAR and optical images. Then, based on modelling results, the optimal observation conditions for radar and optical imagery will be introduced. Afterwards, capability of detection will be described and illustrated for both the radar and the optical case. In the optical domain, the process will distinguish at least two classes: thin and thick. In the HSE context, a database can be used to identify some detected products. The last step is quantification. A sophisticated method, relying on L band radar imagery, will be used to identify pixels covered by a film, meaning presence of oil at the surface, and the ones for which the oil may be as droplets in the volume. The traditional use of SAR data is also extended to the estimation of the oil concentration within an oil and seawater mixture. For optical data, the most direct quantification process relies on automatic Bonn code classification. The code links a class with a range of thickness and computes a minimum and a maximum volume of product in each class. If the product is in the data base a more suited classification and volume assessment can be done. If the thickness is too thin (spectral signature due to absorption is too weak) or too thick (only the upper part of the product layer contributes to the signal), a thickness estimated thanks to pool experiment is associated to each class enabling to compute a volume per class and a global volume. In the other cases, in a near future, modelling would enable to assess the thickness. Concerning hydrocarbon emulsions, modelling in the optical domain is in progress in order to predict skin depth and to derive water content.Pendant cinq ans, dans le cadre du projet de recherche NAOMI (New Advanced Observation Method Integration), Total et l'ONERA ont travaillé dans le domaine de l'imagerie radar et optique pour détecter, caractériser et quantifier les nappes d'hydrocarbures en mer. Des mesures en laboratoire et en bassin, de la modélisation physique et des expérimentations en mer ont été associées pour comprendre pleinement le signal mesuré au dessus d'une nappe. L'expression analytique du signal en fonction des paramètres géophysiques permet d'analyser et de suivre la surface marine : Une nappe est-elle présente ? Quel type de nappe est-ce (très fine ou pas) ? Le produit est-il connu (dans la base de données existante) ? L'épaisseur de la nappe peut-elle être mesurée par imagerie radar ou optique ? Quel est le volume de la nappe ?Dans le contexte Hygiène Sécurité et Environnement (HSE), une campagne de mesure exhaustive peut être faite pour créer une base de données avec les signatures des hydrocarbures ou des émulsions, les coefficients d'extinction, les épaisseurs de peau, les épaisseurs minimales perceptibles par extinction et les épaisseurs.Selon les données disponibles, imagerie radar et/ou optique, le potentiel de détection, caractérisation et quantification de nappe sera présenté. Après un rappel des spécificités HSE, le papier passera en revue les principales caractéristiques des données d'entrée, à savoir les images SAR et optiques. Ensuite, à partir des résultats de modélisation, on introduira les conditions d'observation optimales pour le radar et pour l'optique. Après cela, la capacité de détection sera présenté et illustrée pour le radar et pour l'optique. En optique, le traitement distingue a minima deux classes : fin et épais. Dans le contexte HSE, une base de données peut être utilisée pour identifier les produits détectés. La dernière étape est la quantification. Une méthode sophitiquée, s'appuyant sur la bande L en imagerie radar, sera utilisée pour identifier les pixels couverts par un film, ce qui signifie une présence l'huile en surface, et les pixels pour lesquels l'huile est sous forme de gouttelettes dans le volume. L'utilisation classique du SAR est aussi étendue à l'estimation de la concentration en huileau sein d'un mélange huile et eau de mer. Pour les données optiques, le traitement de quanticication le plus direct repose sur une classification automatique de type code de Bonn. Le code lie une classe avec une plage d'épaisseur et calcule un volume minimum et un volume maximum de produit dans chaque classe. Si le produit se trouve dans la base de données, une classification et une estimation de volume plus adaptées peuvent être faites. Si l'épaisseur est trop fine (signature spectrale due à l'absorption trop faible) ou trop épaisse (seule la partie supérieure de la couche de produit contribue au signal), une épaisseur estimée grâce à l'expérimentation en bassin est associée à chaque classe permettant de calculer un volume par classe et un volume global. Dans les autres cas, à court terme, la modélisation devrait permettre d'estimer l'épaisseur. Concernant les émulsions d'hydrocarbure, la modélisation en optique est en cours pour prédire l'épaisseur de peau et le contenu en eau