Oil Detection in a Coastal Marsh with Polarimetric Synthetic Aperture Radar (SAR)

The National Aeronautics and Space Administration’s airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was deployed in June 2010 in response to the Deepwater Horizon oil spill in the Gulf of Mexico. UAVSAR is a fully polarimetric L-band Synthetic Aperture Radar (SAR) sensor for obtaining data at high spatial resolutions. Starting a month prior to the UAVSAR collections, visual observations confirmed oil impacts along shorelines within northeastern Barataria Bay waters in eastern coastal Louisiana. UAVSAR data along several flight lines over Barataria Bay were collected on 23 June 2010, including the repeat flight line for which data were collected in June 2009. Our analysis of calibrated single-look complex data for these flight lines shows that structural damage of shoreline marsh accompanied by oil occurrence manifested as anomalous features not evident in pre-spill data. Freeman-Durden (FD) and Cloude-Pottier (CP) decompositions of the polarimetric data and Wishart classifications seeded with the FD and CP classes also highlighted these nearshore features as a change in dominant scattering mechanism. All decompositions and classifications also identify a class of interior marshes that reproduce the spatially extensive changes in backscatter indicated by the pre- and post-spill comparison of multi-polarization radar backscatter data. FD and CP decompositions reveal that those changes indicate a transform of dominant scatter from primarily surface or volumetric to double or even bounce. Given supportive evidence that oil-polluted waters penetrated into the interior marshes, it is reasonable that these backscatter changes correspond with oil exposure; however, multiple factors prevent unambiguous determination of whether UAVSAR detected oil in interior marshes

Studies of the Deepwater Horizon Oil Spill With the UAVSAR Radar

On 22- 23 June 2010, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L band radar imaged the Deepwater Horizon oil spill and the effects of oil that was transported within the Gulf of Mexico. We describe the campaign and discuss the unique contributions of the UAVSAR radar to the study of the detection, migration, and impact of oil from the spill. We present an overview of UAVSAR data analyses that support the original science goals of the campaign, namely, (1) algorithm development for oil slick detection and characterization, (2) mapping of oil intrusion into coastal wetlands and intercoastal waterways, and (3) ecosystem impact studies. Our study area focuses on oil-affected wetlands in Barataria Bay, Louisiana. The results indicate that fine resolution, low-noise, L band radar can detect surface oil-on-water with sufficient sensitivity to identify regions in a slick with different types of oil/emulsions and/or oil coverage; identify oil on waters in inland bays and differentiate mixed/weathered oil from fresh oil as it moves into the area; identify areas of potentially impacted wetlands and vegetation in the marshes; and support the crisis response through location of compromised booms and heavily oiled beaches

Oil Spills and Slicks imaged by Synthetic Aperture Radar

Oil spills and slicks occur in the ocean around the world due to natural seeps, oil extraction, transportation, and consumption. Satellite synthetic aperture radar (SAR) has proven to be an efficient tool for identifying and classifying oil on the sea surface. This information can be used to monitor areas for potential illegal marine discharge or to respond to an oil spill incident. When used to monitor shipping lanes or drilling platforms, timely analysis can identify offending parties and lead to prosecution. Following an oil spill such as that from the Deepwater Horizon rig in the Gulf of Mexico in 2010, SAR can be used to direct response activities and optimize available resources

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Offshore oil spill detection using synthetic aperture radar

Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and ‘look-alike’ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements

A Study of Polarimetric Parameters Used for Sea Ice Classification

The Arctic region of the Earth are largely covered by sea ice, which is important for the marine ecosystems locally in the region, but it also has an impact on the climate systems. Recent climate changes means that the sea ice is melting and the coverage is shrinking which can have a global impact. Monitoring these sea ice changes are important to get an understanding of this impact. There are multiple options for how to carry out this monitoring, but the Arctic is a cold, dark and remote region, so a good option is using satellite images. A special advantage of using SAR (Synthetic Aperture Radar) satellites is that they are insensitive to weather conditions and darkness, which can be a challenge in the Arctic regions, especially during the winter months. Today operational ice services produces sea ice charts using such SAR images but they primarily reply on dual-polarimetric data. These images covers large areas due to the large swath for these modes and are idea for a Pan-Arctic coverage. However, they are impacted by e.g., effects of varying incidence angle throughout the images. This thesis will study the possibilities that lies within radar polarimetry, a field that utilized fully polarimetric data to extract even more information regarding the target on the surface, in this case sea ice. Moreover, these images enabled extraction of scattering type information and through this added information improve the sea ice type classification maps. The images studied here are two sets of fully polarimetric RADARSAT-2 data that covers the same sea ice but with varying incidence angles. Four different polarimetric features; the co-polarization ratio, the M-parameter, degree of polarization as well as the polarimetric coherence, were investigated for their separability measures between different sea ice types as well as how affected they are by incidence angle variations. Out of these four the co-polarization ratio was found to be useful to separate the thinner sea ice types as well as open water from the surrounding thicker sea ice. Both the M-parameter and the degree of polarization showed promise in separating deformed sea ice from the other ice types at higher incidence angles. Though limited consistent separability between different ice types were observed for the polarimetric coherence. The polarimetric coherence was found to be independent on the incidence angle, though the other three parameters showed an incidence angle dependency

Microwave satellite remote sensing for a sustainable sea

The oceans cover roughly 2/3 of the Earth’s surface and are a fundamental ecosystem regulating climate, weather and representing a huge reservoir of biodiversity and natural resources. The preservation of the oceans is therefore not only relevant on an environmental perspective but also on an economical one. A sustainable approach is requested that cannot be simply achieved by improving technologies but calls for a shared new vision of common goods.Within such a complex and holistic problem, the role of satellite microwave remote sensing to observe marine ecosystem and to assist a sustainable development of human activities must be considered. In such a view the paper is meant. Accordingly, the key microwave sensor technologies are reviewed paying particular emphasis on those applications that can provide effective support to pursue some of the UN Sustainable Development Goals. Three meaningful sectors are showcased:oil and gas, where microwave sensors can provide continuous fine-resolution monitoring of critical infrastructures; renewable energy, where microwave satellite remote sensing allows supporting the management of offshore wind farms during both feasibility and operational stages; plastic pollution, where microwave technologies that exploit signals of opportunity offer large-scale monitoring capability to provide marine litter maps of the oceans

Oil spill and ship detection using high resolution polarimetric X-band SAR data

Among illegal human activities, marine pollution and target detection are the key concern of Maritime Security and Safety. This thesis deals with oil spill and ship detection using high resolution X-band polarimetric SAR (PolSAR). Polarimetry aims at analysing the polarization state of a wave field, in order to obtain physical information from the observed object. In this dissertation PolSAR techniques are suggested as improvement of the current State-of-the-Art of SAR marine pollution and target detection, by examining in depth Near Real Time suitability