Rossana Paciello

Assessment of the Robust Satellite Technique (RST) for volcanic ash plume identification and tracking

Volcanic clouds pose a serious threat for both aircrafts and passengers because of ash, which may cause serious damages to the flight control systems and to jet engines. Starting from 2007, an automatic satellite monitoring system has been implemented at IMAA (Institute of Methodologies of Environmental Analysis) to identify and track volcanic ash plumes using NOAA-AVHRR data. This system is capable of providing reliable information about possible volcanic ash plumes over a region of interest (ROI) within a few minute after the sensing time, thanks to the implementation of a robust multi-temporal approach of satellite data analysis named RST (Robust Satellite Technique). This approach has already shown a high potential in successfully identifying and tracking volcanic ash clouds compared to traditional techniques, both in its standard (i.e. two-channel) and advanced (i.e. three-channel) configuration. In this paper, RST performances for ash plume detection and monitoring will be further assessed, showing some recent results obtained during December 2006 and analyzing a time series of satellite observations carried out over Mount Etna area for different months in different observational conditions. In order to validate and assess RST performances, a long-term time domain analysis is in progress, also investigating periods mainly characterised by quiescent phases (i.e. with no ash emission episodes). Preliminary results of such a statistical analysis will be presented and the possible contribution of this satellite monitoring system in supporting management of strong eruptive crisis will also be discussed.

A review of RSTVOLC, an original algorithm for automatic detection and near-real-time monitoring of volcanic hotspots from space

Abstract The observation of volcanic thermal activity from space dates back to the late 1960s. Several methods have been proposed to improve detection and monitoring capabilities of thermal volcanic features, and to characterize them to improve our understanding of volcanic processes, as well as to inform operational decisions. In this paper we review the RSTVOLC algorithm, which has been designed and implemented for automated detection and near-real-time monitoring of volcanic hotspots. The algorithm is based on the general Robust Satellite Techniques (RST) approach, representing an original strategy for satellite data analysis in the space–time domain. It has proven to be a useful tool for investigating volcanoes worldwide, by means of different satellite sensors, onboard polar orbiting and geostationary platforms. The RSTVOLC rationale, its requirements and main operational capabilities are described here, together with the advantages of the tool and the known limitations. Results achieved through the study of two past eruptive events are shown, together with some recent examples demonstrating the near-continuous monitoring capability offered by RSTVOLC. A summary is also made of the type products that the method is able to generate and provide. Lastly, the future perspectives, in terms of its possible implementation on the new generation of satellite systems, are briefly discussed.

A Robust Satellite Technique (RST) for dust storm detection and monitoring: The case of 2009 Australian event

In this paper, an original method of satellite data analysis named RST (Robust Satellite Technique), already successfully used to study and monitor several natural and environmental hazards, is applied for the first time to a recent dust storm occurred in Australia in September 2009. This event was analyzed implementing RST on MTSAT-1R (Multi-functional Transport Satellite-1Replacement) Japanese geostationary satellite data. Some preliminary results of this study are presented, discussing RST performances even in comparison with traditional split window satellite techniques.

Robust satellite techniques for thermal volcanic activity monitoring, early warning and possible prediction of new eruptive events

Among the several satellite techniques developed for volcanic activity monitoring an original multi-temporal approach, named RST (Robust Satellite Techniques), has shown high performances in detecting hotspots, with a low false positive rate under different observational and atmospheric conditions. This approach has been successfully used to monitor volcanoes at different geographic location (e.g. Etna, Merapi, Rabaul, etc.) even thanks to its native exportability on whatever satellite platforms. In particular, the recent RST implementation on SEVIRI (Spinning Enhanced Visible & InfraRed Imager) sensor data has shown that even abrupt changes in thermal signals, related to new phase of volcano unrest, may be accurately and timely identified by satellite. In this paper, the potential in timely detecting sudden eruptive events will be further assessed, analyzing the Jebel Al Tair (Yemen) eruption of 30 September 2007. Moreover, the RST performances in recognizing possible thermal precursors of impending eruptions will also be discussed.

A multi-sensors analysis of RST-based thermal anomalies in the case of the Abruzzo earthquake

Space-time anomalies in Thermal InfraRed (TIR) satellite imagery, from weeks to days, before severe earthquakes are reported in several studies. Among the various genetic models, the increase of green-house gas (such as CO2, CH4, etc.) emission rates, have been suggested to explain the appearance of anomalous TIR signal transients in some relation with the place and time of earthquake occurrence. Among the others, a Robust Satellite data analysis Technique (RST) was proposed to investigate possible relations between earthquake occurrence and space-time fluctuations of Earths emitted TIR radiation observed from satellite. In this paper, independent RST analysis performed over the Italian peninsula at the time of the Abruzzo earthquake (April 6, 2009; ML∼5.8) using different satellite system (MSG/SEVIRI, NOAA/AVHRR and EOS/MODIS) are presented and compared.

A New Approach for Detecting and Monitoring Saharan Dusts from Space

A New Approach for Detecting and Monitoring Saharan Dusts from Space The Saharan region has long been indicated as the main source in the world of soil dust in the atmosphere. Saharan dust storms are particularly investigated because they represent a potential risk for human health and cause damages and disruptions to the transport routes and communication. They can have direct implications (strictly related to the desertification processes affecting the Sub Saharan region) on the Earth’s climatic system and/or on the precipitation regimes. In recent years, in addition to the ground monitoring systems, several satellite techniques have been proposed to detect and monitor Saharan dust clouds. The success of these methodologies, as those exploiting the reverse absorption behaviour shown by silicate particles, in comparison with ice crystals and water droplets, at 11 and 12 μm wavelengths (split-window), is strongly dependent on the observational conditions (day/night, land/sea, etc.) and on the specific aerosol properties (mainly size distribution and complex refractive index). In particular, although dust and meteorological clouds generally show a different spectral behaviour in the split window bands, an effective discrimination of these features still represent a major issue. In this paper, a Robust Satellite data analysis Technique (RST), which already highlighted good performances in detecting desert dust aerosol, has been further tested, analyzing an important Saharan dust event affecting Mediterranean basin in May 2010, with results compared to those provided by two traditional split window methods. Outcomes of this study, achieved using, for the first time, daytime infrared MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infra-red Imager) data, confirm that RST, thanks to a good trade-off between sensitivity and reliability of detection, may profitably be used for monitoring Saharan dust events from space in different observational conditions.

PRE-EARTHQUAKES, an FP7 project for integrating observations and knowledges on earthquake precursors: Preliminary results and strategy

PRE-EARTHQUAKES (Processing Russian and European EARTH observations for earthQUAKE precursors Studies) EU-FP7 project is devoted to demonstrate - integrating different observational data, comparing and improving different data analysis methods - how it is possible to progressively increase reliability of short term seismic risk assessment. Three main testing area were selected (Italy, Turkey and Sakhalin) in order to concentrate observations and integration efforts starting with a learning phase on selected events in the past devoted to identify the most suitable parameters, observations technologies, data analysis algorithms. For these areas, different ground (80 radon and 29 spring water stations in Turkey region, 2 magneto-telluric in Italy) and satellite (18 different systems) based observations, 11 data analysis methods, for 7 measured parameters, have been compared and integrated. A specific integration platform (PEG, Pre-Earthquakes Geoportal) based on OGC (Open Geospatial Consortium) standards, was developed to operate a products integration, cross-validation and scientific interpretation.

Robust Satellite Techniques (RST) for active volcanoes monitoring

The RST (Robust Satellite Techniques) approach is a multi-temporal scheme of data analysis recently implemented in an automatic processing chain developed at IMAA-DIFA laboratories in order to monitor Italian volcanoes in near real time. In this paper some recent results of satellite monitoring of Etna and Stromboli volcanoes are presented. Preliminary results of RST implementation on SEVIRI (Spinning Enhanced Visible and Infrared Imager) data, for ash plume identification and tracking will also be, for the first time, analyzed and discussed.

On the use of temporal vegetation indices in support of eligibility controls for EU aids in agriculture

ABSTRACT The use of remote sensing in the context of the Common Agricultural Policy (CAP) has progressively become an official method to support European (EU) Member States in carrying out controls about declarations of farmers requiring EU subsidies in agriculture. Reliable automatic or semi-automatic methodologies aiming at crop identification are still being developed and the only technique, which is officially accepted in the CAP context, remains photo interpretation of high/very high (satellite or aerial) orthoimages. To verify past situations, only orthophotos can be used but, unfortunately, they are not always available. In these cases, the use of satellite sensors with adequate spatial, spectral, and temporal resolutions, together with a reliable data analysis technique, could support or even substitute orthophoto interpretation. In this study, we propose a multi-temporal, multispectral algorithm exploiting the Thematic Mapper/Enhanced Thematic Mapper Plus data on Landsat platforms to identify different land covers in the context of CAP. Here it is presented to discriminate arable from non-arable lands. Assessment of the methodology was carried out using Corine 2012 and more than 1500 validation points over Basilicata region (Southern Italy). A general good agreement was found (74%), which increases to 82% in the specific case of arable land identification.

Issues and Possible Improvements in Winter Fires Detection by Satellite Radiances Analysis: Lesson Learned in Two Regions of Northern Italy

Lombardy is a region of Northern Italy characterized by an alpine/continental climate, where, in winter, strong dry winds and scarce plants hydration frequently cause surface fires with a relatively low intensity level. In Liguria, a coastal region of Northwestern Italy, climate and vegetation conditions are different from Lombardy; in both winter and summer, severe forest fires frequently occur. This study is based on the investigation of winter fire regimes characterizing the aforementioned regions, using Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. In particular, we compare the RST-FIRES and the well-established MOD14/MYD14 fire detection products in order to assess their performance in detecting winter fires in Northern Italy. The study shows as a result that, despite the advantages offered by self-adaptive algorithms like RST-FIRES at local/regional scale, wildfires occurring in Lombardy during the winter season are challenging to identify by satellite. In Liguria region, where morphological and weather conditions favor flame propagation, they seem to be better detectable from space. This study aims at investigating issues affecting satellite monitoring of winter fires by encouraging the usage of a multiplatform observing system, integrating data provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard Meteosat Second Generation (MSG) satellites, for better supporting fire management activities.