Feasibility of Oil Slick Detection Using BeiDou-R Coastal Simulation

Oil spills, which can cause severe immediate and long-term harm to marine ecological environments for decades after the initial accident, require rapid and accurate monitoring. Currently, optical and radar satellite images are used to monitor oil spills; however, remote sensing generally needs a long revisit period. Global Navigation Satellite System reflected signals (GNSS-R) can provide all-weather and all-day ocean monitoring and is therefore more suitable for oil spill monitoring. To assess the feasibility of the BeiDou Navigation Satellite System reflected signals (BeiDou-R) in detecting oil slicks, a BeiDou-R coastal simulated experiment is performed in this study on the oil slick distribution of an oil pipeline explosion accident. We set up an observation point and selected observation satellites, and a delay-Doppler map (DDM) of an oil-slicked sea surface under coastal scenarios was created by combining the mean-square slope (MSS) model for oil-slicked/clean surfaces and the Zavorotny–Voronovich (Z–V) scattering model. DDM simulation of the coastal scenarios effectively represents the scattering coefficient distribution of the presence of an oil slick. Theoretical analysis revealed that oil slicks can be detected within a radius of less than 5 km around the specular reflection point (SP) for BeiDou-R coastal simulation

Analyzing Anomalous Artefacts in TDS-1 Delay Doppler Maps

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Global Navigation Satellite System Reflectometry (GNSS-R) uses the GNSS reflected signals to study parameters of the Earth's surface such as ocean surface height, wind speed, soil moisture, sea surface target detection. In this paper fourteen DDMs (Delay Doppler Maps) of TechDemoSat-1 (TDS-1) containing anomalous artefacts are presented and analyzed. Anomalous artefacts are not caused by the reflection from Earth surface targets, occultation, nor the leakages of direct signals, but likely - according to their delays- from reflection of targets above the Earth's surface (either airborne or spaceborne).Postprint (author's final draft

Space-based Global Maritime Surveillance. Part I: Satellite Technologies

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

Sea surface oil slick detection and wind field measurement using global navigation satellite system reflectometry

In this thesis, research for improving sea surface remote sensing using the Global Navigation Satellite System-Reflectometry (GNSS-R) signals is presented. Firstly, a method to enable the simulation of GNSS-R delay Doppler Map (DDM) of an oil slicked sea surfaces under general scenarios is proposed. The DDM of oil slicked sea surface under general scenarios is generated by combining the mean-square slope model for oil slicked/clean surfaces and the GNSS-R Zavorotny-Voronovich (Z-V) scattering model. The coordinate system transformation appropriate for general- elevation-angle scenarios is also incorporated. Secondly, a technique to detect sea surface oil spills using reflections from Global Navigation Satellite System (GNSS) satellites is presented. This technique is implemented by compensating the distor- tion induced during the DDM deconvolution process of scattering coefficient retrieval and employing the spatial integration approach (SIA) to retrieve the scattering co- efficients unambiguously using the DDMs obtained by two separate antenna beams. A performance characterization including retrieval accuracy and resolution is demon- strated with respect to the signal-to-noise ratio and the size of oil slicks, respectively. Simulation based on the oil slick distribution of the Deepwater Horizon oil spill ac- cident shows that the retrieval error can be reduced by the SIA after the distortion correction. The technique proposed here can be used to map oil slick extent on the ocean surface or it may be applied generically to produce physical surface maps of the bistatic scattering coefficient from multiple DDM’s from a single space-based platform. Lastly, a novel method is presented to retrieve sea surface wind speed and direction by fitting the two-dimensional simulated GNSS-R DDMs to measured data. An 18- second incoherent correlation is performed on the measured signal to reduce the noise level. Meanwhile, a variable step-size iteration as well as a fitting threshold are used to reduce the computational cost and error rate of the fitting procedure, respectively. Unlike previous methods, all the DDM points with normalized power higher than the threshold are used in the least-square fitting. An optimal fitting threshold is also proposed. To validate the proposed method, the retrieval results based on a dataset from the United Kingdom Disaster Monitoring Constellation satellite are compared with the in-situ measurements provided by the National Data Buoy Center, and good correlation is observed between the two

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

Sea target detection using spaceborne GNSS-R delay-doppler maps: theory and experimental proof of concept using TDS-1 data

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This study addresses a novel application of global navigation satellite system-reflectometry (GNSS-R) delay-Doppler maps (DDMs), namely sea target detection. In contrast with other competing remote sensing technologies, such as synthetic aperture radar and optical systems, typically exploited in the field of sea target detection, GNSS-R systems could be employed as satellite constellations, so as to fulfill the temporal requirements for near real-time ships and sea ice sheets monitoring. In this study, the revisit time offered by GNSS-R systems is quantitatively evaluated by means of a simulation analysis, in which three different realistic GNSS-R missions are simulated and analyzed. Then, a sea target detection algorithm from spaceborne GNSS-R DDMs is described and assessed. The algorithm is based on a sea clutter compensation step and uses an adaptive threshold to take into account spatial variations in the sea background and/or noise statistics. Finally, the sea target detector algorithm is tested and validated for the first time ever using experimental GNSS-R data from the U.K. TechDemoSat-1 dataset. Performance is assessed by providing the receiver operating characteristic curves, and some preliminary experimental results are presented.Peer ReviewedPostprint (published version

Wind direction signatures in GNSS-R observables from space

Peer ReviewedPostprint (published version

Selected Papers from the 2018 IEEE International Workshop on Metrology for the Sea

This Special Issue is devoted to recent developments in instrumentation and measurement techniques applied to the marine field. ¶The sea is the medium that has allowed people to travel from one continent to another using vessels, even today despite the use of aircraft. It has also been acting as a great reservoir and source of food for all living beings. However, for many generations, it served as a landfill for depositing conventional and nuclear wastes, especially in its deep seabeds, and we are assisting in a race to exploit minerals and resources, different from foods, encompassed in it. Its health is a great challenge for the survival of all humanity since it is one of the most important environmental components targeted by global warming. ¶ As everyone may know, measuring is a step that generates substantial knowledge about a phenomenon or an asset, which is the basis for proposing correct solutions and making proper decisions. However, measurements in the sea environment pose unique difficulties and opportunities, which is made clear from the research results presented in this Special Issue

Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1

Global Navigation Satellite System (GNSS) signals can be exploited to remotely sense atmosphere and land and ocean surface to retrieve a range of geophysical parameters. This paper proposes two new methods, termed as power-summation of differential Delay-Doppler Maps (PS-D) and pixel-number of differential Delay-Doppler Maps (PN-D), to distinguish between sea ice and sea water using differential Delay-Doppler Maps (dDDMs). PS-D and PN-D make use of power-summation and pixel-number of dDDMs, respectively, to measure the degree of difference between two DDMs so as to determine the transition state (water-water, water-ice, ice-ice and ice-water) and hence ice and water are detected. Moreover, an adaptive incoherent averaging of DDMs is employed to improve the computational efficiency. A large number of DDMs recorded by UK TechDemoSat-1 (TDS-1) over the Arctic region are used to test the proposed sea ice detection methods. Through evaluating against ground-truth measurements from the Ocean Sea Ice SAF, the proposed PS-D and PN-D methods achieve a probability of detection of 99.72% and 99.69% respectively, while the probability of false detection is 0.28% and 0.31% respectively

Remote Sensing Applications in Coastal Environment

Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments