Treating the time dimension (i.e., the 4th dimension) of geospatial parameters, likewise the other dimensions is important for some applications to better understand the possible space-time relations among the physical parameters underlying the dynamics of complex phenomena. In this paper we present the potential of an innovative solution, named 4 Dimensions Environmental ObServation (4DEOS), which is a platform able to handle the 4th dimension in an asynchronous way (i.e., to visualize more signals, holding some of them fixed in the time domain while moving others over time). 4DEOS is based on a Client-Broker-Server architecture for the easy integration and visualization of heterogeneous, asynchronous, geospatial products. The prototype system was evaluated in the case of earthquake prediction studies where the asynchronous visualization of independent observations could allow a timely identification of the spatial correlations appearing at different time lags, which could be missed using other existing 4D Geographic Information System software.

Flooding represents a serious threat to millions of people around the world and its hazard is rising as a result of climate changes. From this perspective, flood risk management is a key focus of many governments, whose priority is to have frequently updated and accurate information about the flood state and evolution to promptly react to the disaster and to put in place effective countermeasures devoted to limit damages and human lives losses. Remote sensing technology allows for flood monitoring at different spatial and temporal resolutions with an adequate level of accuracy. In particular, for emergency response purposes, an integrated use of satellite data, acquired by both optical and passive or active microwave instruments, has to be preferred to have more complete and frequently updated information on soil conditions and to better support decision makers. In this framework, multi-year time series of MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) data were processed and analyzed. In detail, the Robust Satellite Techniques (RST), a multi-sensor approach for satellite data analysis, has been implemented for studying the August 2002 Elbe river flood occurred in Germany, trying to assess the potential of such an integrated system for the determination of soil status and conditions (i.e. moisture variation, water presence) as well as for a timely detection and a near real time monitoring of critical soil conditions.

In this paper, the robust satellite techniques (RST), a multi-temporal scheme of satellite data analysis, was implemented to analyze the flaring activity of the Val d'Agri Oil Center (COVA), the largest Italian gas and oil pre-treatment plant, owned by Ente Nazionale Idrocarburi (ENI). For this site, located in an anthropized area characterized by a large environmental complexity, flaring emissions are mainly related to emergency conditions (i.e., waste flaring), as industrial processes are regulated by strict regional laws. While regarding the peculiar characteristics of COVA flaring, the main aim of this work was to assess the performances of RST in terms of sensitivity and reliability in providing independent estimations of gas flaring volumes in such conditions. In detail, RST was implemented for 13 years of Moderate Resolution Imaging Spectroradiometer (MODIS) medium and thermal infrared data in order to identify the highly radiant records associated with the COVA flare emergency discharges. Then, using data provided by ENI about gas flaring volumes in the period 2003-2009, a MODIS-based regression model was developed and tested. The results achieved indicate that the such a model is able to estimate, with a good level of accuracy (R-2 of 0.83), emitted gas flaring volumes at COVA.

In the evening of 20 March 2010, after about two centuries of quiescence, an effusive eruption took place at Eyjafjoll (Iceland) volcano, from a small vent localized on the northeast flank (Fimmvorduhals Pass) of the volcano edifice. On 31 March, a new eruptive fissure opened on the same region emitting lava. About 2 weeks later, on 14 April, a strong explosive eruption took place under the Eyjafjallajokull glacier, injecting copious amounts of ash in the atmosphere and causing an unprecedented air traffic disruption in Northern and Central Europe. In this paper, the changes in thermal signals occurring at Eyjafjoll volcano during I March 20,April 2010 are investigated, testing the RSTVOLC algorithm for the first time in a subpolar environment. Outcomes of this retrospective study, performed by means of infrared Moderate Resolution Imaging Spectroradiometer (MODES) data, show that both effusive and explosive eruptions of the Eyjafjoll volcano could be identified in a timely manner and well monitored from space. Moreover, in spite of a lack of pre-eruptive hot spots detection, this paper reveals a general increasing trend of the middle infrared signal at crater area, beginning 2 weeks before the explosion, stimulating and suggesting further investigations devoted to better characterize the thermal behavior of the monitored volcano.

The coastal marine environment is a complex and dynamic ecosystem, strongly subject to environmental degradation, because of both natural and anthropogenic causes. In the last years several remote sensing-based approaches for coastal water monitoring and investigation have been proposed. Among the few satellite packages useful for these purposes, the Moderate-Resolution Imaging Spectroradiometer (MODIS), aboard the Earth Observing System (EOS) Terra (since 2000) and Aqua (since 2002) satellites is one of the most used. It is the only one, still operational, that assures a long historical series of data with a good trade-off between spatial and temporal resolution, as well as an adequate spectral resolution in the visible portion of the electromagnetic spectrum. One of the main limit of the MODIS-based Ocean Color (OC) products is their low sensitivity to the retrieved parameters in shallow waters, where the signal at the sensor is influenced also by bottom reflectance. To overcome these limits, as well as to better identify the actual sea water status for a specific region of the interest, in this work we implemented the Robust Satellite Techniques (RST) approach on long-term (i.e. ten years) historical series of MODIS OC products. In detail, the parameters investigated by RST was the Chlorophyll-a (Chl-a) concentration, while the region of interest was the Ionian sea water off the coast of Basilicata region (southern Italy), in the Gulf of Taranto. Achieved results shown in this paper confirm that RST, analyzing the historical behavior of the signal at pixel level, is capable to detect long term trend of the investigated parameter as well as its medium/short temporal changes, allowing for a recognition of the most exposed areas as well as for a timely identification of any possible critical situation.

Timely and frequently updated information about flood-affected areas and their space-time evolution are often crucial in order to correctly manage the emergency phases. In such a context, optical data provided by meteorological satellites, offering the highest available temporal resolution (from hours to minutes), could have a great potential. As cloud cover often occurs reducing the number of usable optical satellite images, an appropriate integration of observations coming from different satellite systems will surely improve the probability to find cloud-free images over the investigated region. To make this integration effective, appropriate satellite data analysis methodologies, suitable for providing congruent results, regardless of the used sensor, are envisaged. In this paper, a sensor-independent approach (RST, Robust Satellites Techniques-FLOOD) is presented and applied to data acquired by two different satellite systems (Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration platforms and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Earth Observing System satellites) at different spatial resolutions (from 1 km to 250 m) in the case of Elbe flood event occurred in Germany on August 2002. Results achieved demonstrated as the full integration of AVHRR and MODIS RST-FLOOD products allowed us to double the number of satellite passes daily available, improving continuity of monitoring over flood-affected regions. In addition, the application of RST-FLOOD to higher spatial resolution MODIS (250 m) data revealed to be crucial not only for mapping purposes but also for improving RST-FLOOD capability in identifying flooded areas not previously detected at lower spatial resolution.

Radio-frequency interference (RFI) is increasingly a severe problem for present and future microwave satellite missions. RFI at C- and X-bands can contaminate remotely sensed measurements, as experienced with the Advanced Microwave Scanning Radiometer (AMSR-E) and the WindSat sensor. In this work, the multitemporal Robust Satellite Techniques approach has been implemented on C-band AMSR-E data in order to identify areas systematically affected by different levels of RFI, trying to discriminate them from natural geophysical variability zones. To the scope, nine years of AMSR-E data have been investigated, allowing us also to better infer RFI impact on data acquired during ascending or descending passes, as well as in horizontal or vertical polarization. In detail, two analyses were carried out: one considering only measurements at C-band and another one taking into account a combination between C- and X-band measurements. The results of this study will be shown and discussed in this paper.

The scientific objective of Pre- Earthquakes is to demonstrate how far the systematic integration of measures of different physical and chemical parameters may improve our present ability to forecast strong earthquakes in the short term.Observations are collected from 18 different satellite systems and more than 100 ground stations will be used to study the anomalous variations of surface and atmospheric parameters (up to the ionosphere) that have long been proposed as possible precursors of strong earthquakes. The Sakhalin peninsula in eastern Russia, Turkey and Italy have been selected as initial testing areas where observation inte- gration will be particularly intensified up to a period of real-time monitoring at the end of the project, expected for December 2012.After only 1 year of activity, automatic data-product generation chains were fully implemented for seven moni- tored parameters, using 20 independ- ent observing technologies (including both satellite systems and ground sta- tions), 11 different data analysis algo- rithms for (more than) three testing areas and 24 different testing periods. A Pre-Earthquakes Geoportal (PEG), is now ready to operate, serving prod- uct integration, cross-validation and scientific interpretation not only for project partners but also for network- ing members (presently more than 20) asking to join the project in the framework of the EQuOS (Earthquake Observation System) initiative. In this paper project rationale, scientific approach and achieved results to date are briefly presented.