Hyperspectral Analysis of Oil and Oil-Impacted Soils for Remote Sensing Purposes

While conventional multispectral sensors record the radiometric signal only at a handful of wavelengths, hyperspectral sensors measure the reflected solar signal at hundreds contiguous and narrow wavelength bands, spanning from the visible to the infrared. Hyperspectral images provide ample spectral information to identify and distinguish between spectrally similar (but unique) materials, providing the ability to make proper distinctions among materials with only subtle signature differences. Hyperspectral images show hence potentiality for proper discrimination between oil slicks and other natural phenomena (look-alike); and even for proper distinctions between oil types. Additionally they can give indications on oil volume. At present, many airborne hyperspectral sensors are available to collect data, but only two civil spaceborn hyperspectral sensors exist as technology demonstrator: the Hyperion sensor on NASA’s EO-1 satellite and the CHRIS sensor on the European Space Agency’s PROBA satellite. Consequently, the concrete opportunity to use spaceborn hyperspectral remote sensing for operational oil spill monitoring is yet not available. Nevertheless, it is clear that the future of satellite hyperspectral remote sensing of oil pollution in the marine/coastal environment is very promising. In order to correctly interpret the hyperspectral data, the retrieved spectral signatures must be correlated to specific materials. Therefore specific spectral libraries, containing the spectral signature of the materials to be detected, must be built up. This requires that highly accurate reflected light measurements of samples of the investigated material must be performed in the lab or in the field. Accurate measurements of the spectral reflectance of several samples of oil-contaminated soils have been performed in the laboratory, in the 400-2500 nm wavelength range. Samples of the oils spilt from the Erika and the Prestige tankers during the major accidents of 1999 and 2002 were also collected and analyzed in the same spectral range, using a portable spectrophotometer. All measurements showed the typical absorption features of hydrocarbon-bearing substances: the two absorption peaks centered at 1732 and 2310 nm.JRC.G.3-Agricultur

Anomaly Detection of Mobility Data with Applications to COVID-19 Situational Awareness

This work introduces a live anomaly detection system for high frequency and high-dimensional data collected at regional scale such as Origin Destination Matrices of mobile positioning data. To take into account different granularity in time and space of the data coming from different sources, the system is designed to be simple, yet robust to the data diversity, with the aim of detecting abrupt increase of mobility towards specific regions as well as sudden drops of movements. The methodology is designed to help policymakers or practitioners, and makes it possible to visualise anomalies as well as estimate the effect of COVID-19 related containment or lifting measures in terms of their impact on human mobility as well as spot potential new outbreaks related to large gatherings

Passive Automatic Identification System for Maritime Surveillance

This work describes the main achievements in the Passive AIS (P-AIS) project stage. The extensive literature research in the second chapter concludes performing additional in-situ experiments to estimate reliable target RCS and clutter reflectivity values at the AIS frequency range. The typical effective RCS distribution for ferry, yacht and small wooden boat is experimentally drawn; it reaches up to 26dBsm for the ferry. A clutter model is created, taking into account the literature and the experimental study. The AIS signal waveform is analyzed and the potential range and Doppler resolution is defined. More specifically, the signal ambiguity function gives approximately 20km of range resolution and 40Hz Doppler resolution. A coverage prediction tool, based on the bistatic radar equation, including the aforementioned clutter model; bistatic geometry theory; the effective target RCS; the antenna pattern; the AIS air interface parameters is made. The tool estimates the possible P-AIS coverage area. The work concludes that: even in case of high sea state, the sea is considered as a smooth surface reflection for low grazing angle of observation in the VHF range; the equidistant SNR areas change from Cassini shape to single oval receiver centered; the AIS energy provides excellent target “visibility” if the clutter is not considered. Discussions for further clutter reduction and system sophistication are arisen.JRC.G.4-Maritime affair

Atlas of Migration - 2019

The Atlas of Migration is a reference book providing a snapshot of migration and a knowledge base for policymakers, stakeholders, businesses, researchers and the general public. It provides insights on migration up to 2018 for all EU Member States and 160 non-EU countries.JRC.E.6-Demography, Migration and Governanc

EMN-KCMD Migration Country Factsheets: Statistical Annexes 2020 and how to read guide: Statistical annex based on 2019 data, complementing the 2020 issue of EMN-KCMD Country Factsheets

The Knowledge Centre on Migration and Demography (KCMD) co-operates with the European Migration Network (EMN) for the first time this year in the 2020 edition of the Migration Country Factsheets covering the 27 EU Member States plus Norway. The Country Factsheet is made by a narrative section, divided into ten thematic areas, and a statistical annex, providing for each area relevant data and visualizations. EMN will publish the Factsheets on the following page: https://ec.europa.eu/home-affairs/what-we-do/networks/european_migration_network/reports/factsheets_en The role of KCMD in the project is to produce the statistical annexes for the 27 EU Member States plus Norway.JRC.E.6-Demography, Migration and Governanc

How human mobility explains the initial spread of COVID-19: A European regional analysis

Countries in Europe took different mobility containment measures to curb the spread of COVID-19. The European Commission asked Mobile Network Operators to share on a voluntarily basis anonymised and aggregate mobile data to improve the quality of modelling and forecasting for the pandemic at EU level. In fact, mobility data at EU scale can help understand the dynamics of the pandemic and possibly limit the impact of future waves. Still, since a reliable and consistent method to measure the evolution of contagion at international level is missing, a systematic analysis of the relationship between human mobility and virus spread has never been conducted. A notable exceptions are France and Italy, for which data on excess deaths, an indirect indicator which is generally considered to be less affected by national and regional assumptions, are available at department and municipality level respectively. Using these information together with anonymysed and aggregated mobile data, this study shows that mobility alone can explain up to 92% of the initial spread in these two EU countries, while it has a slow decay effect after lockdown measures, meaning that mobility restrictions seem to have effectively contribute to save lives. It also emerges that internal mobility is more important than mobility across provinces and that the typical lagged positive effect of reduced human mobility on reducing excess deaths is around 14-20 days. An analogous analysis relative to Spain, for which an IgG SARS-Cov-2 antibody screening study at province level is used instead of excess deaths statistics, confirms the findings. The same approach adopted in this study can be easily extended to other European countries, as soon as reliable data on the spreading of the virus at a suitable level of granularity will be available. Looking at past data, relative to the initial phase of the outbreak in EU Member States, this study shows in which extent the spreading of the virus and human mobility are connected. The findings will support policymakers in formulating the best data-driven approaches for coming out of confinement, and mostly in building future scenarios in case of new outbreaks.JRC.E.6-Demography, Migration and Governanc

Mapping Mobility Functional Areas (MFA) using Mobile Positioning Data to Inform COVID-19 Policies: A European regional analysis

This work introduces the concept of data-driven Mobility Functional Areas (MFAs) as geographic zones with high degree of intra-mobility exchanges. Such information, calculated at European regional scale thanks to mobile data, can be useful to inform targeted reescalation policy responses in cases of future COVID-19 outbreaks (avoiding large-area or even national lockdowns). In such events, the geographic distribution of MFAs would define territorial areas to which lockdown interventions could be limited, with the result of minimising socio-economic consequences of such policies. The analysis of the time evolution of MFAs can also be thought of as a measure of how human mobility changes not only in intensity but also in patterns, providing innovative insights into the impact of mobility containment measures. This work presents a first analysis for 15 European countries (14 EU Member States and Norway).JRC.E.6-Demography, Migration and Governanc

Measuring the Impact of COVID-19 Confinement Measures on Human Mobility using Mobile Positioning Data: A European regional analysis

This paper presents a mobility indicator derived from anonimised aggregated mobile positioning data. Even though the indicator does not provide information about the behaviour of individuals, it captures valuable insights into the mobility patterns of the population in the EU and it is expected to inform responses against the COVID-19 pandemic. Spatio-temporal harmonisation is carried out so that the indicator can provide mobility estimates comparable across European countries. The indicators are provided at a high spatial granularity (up to NUTS3). As an application, the indicator is used to study the impact of COVID-19 confinement measure on mobility in Europe. It is found that a large proportion of the mobility patterns can be explained by these measures. The paper also presents a comparative analysis between mobility and the infection reproduction number Rt over time.JRC.E.6-Demography, Migration and Governanc