Francesco Marchese

On the Exportability of Robust Satellite Techniques (RST) for Active Volcano Monitoring

Satellite remote sensing has increasingly become a crucial tool for volcanic activity monitoring thanks to continuous observations at global scale, provided with different spatial/spectral/temporal resolutions, on the base of specific satellite platforms, and at relatively low costs. Among the satellite techniques developed for volcanic activity monitoring, the RST (Robust Satellite Techniques) approach has shown high performances in detecting hot spots as well as in automatically identifying ash plumes, effectively discriminating them from weather clouds. This method, based on an extensive, multi-temporal analysis of long-term time series of homogeneous satellite records, has recently been implemented on EOS-MODIS and MSG-SEVIRI data for which further performance improvements are expected. These satellite systems, in fact, offer improved spectral and/or temporal resolutions. In this paper, some preliminarily results of these analyses are presented, both regarding hot spot identification and ash cloud detection and tracking. The potential of RST, to be used within early warning systems devoted to volcanic hazard monitoring and mitigation, will also be discussed.

A Multi-temporal Robust Satellite Technique (RST) for Forest Fire Detection

In this work, an innovative approach, based on a multi-temporal satellite data analysis, named RST (Robust Satellite Technique), which has already been successfully applied for the monitoring of major natural and environmental risks, has been proposed for the detection of forest fires in near real time. RST is applied in the case of some important forest fires occurred in Northern Italy in recent years using MIR sensors onboard polar (NOAA-AVHRR) and geostationary (MSG-SEVIRI) satellites, moreover, in order to assess the technique performances, also a comparison with well-established MODIS fire algorithm is carried out.

INVESTIGATING THE TEMPORAL FLUCTUATIONS IN SATELLITE ADVANCED VERY HIGH RESOLUTION RADIOMETER THERMAL SIGNALS MEASURED IN THE VOLCANIC AREA OF ETNA (ITALY)

The time dynamics of long-term time series of satellite thermal signal, measured at Mount Etna, has been investigated. The signal has been analyzed by means of a recently proposed multi-temporal and robust technique (RST), which has already shown to be better capable to detect and monitor volcanic hotspots, compared to traditional satellite approaches. The temporal fluctuations of the thermal signal detected by RST over a long series (1995-2005) of Advanced Very High Resolution Radiometer (AVHRR) satellite data, have been characterized by means of the correlation function and the power spectrum analysis, which have shown the presence of correlation structures in the thermal time series recorded in the crater area.

A Robust Multitemporal Satellite Technique for Volcanic Activity Monitoring: Possible Impacts on Volcanic Hazard Mitigation

Among the natural hazards, volcanoes represent one of major risk for both population and surrounding infrastructures, causing every year significant economical and environmental damages. Satellite remote sensing, thanks to multispectral data, high observational frequencies and global coverage, represents an important tool for volcanic activity monitoring, especially in remote areas where traditional techniques are generally inadequately applied. A new multitemporal satellite approach named RST (Robust Satellite Techniques) applied to several recent eruptions of Mount Etna and Stromboli volcanoes has shown to be suitable to correctly identifying and tracking volcanic ash plumes as well as to successfully detecting and monitoring volcanic thermal anomalies strongly reducing false alarm occurrences. In this paper, some recent RST results, confirming the high reliability and sensitivity of the proposed approach in volcanic activity monitoring together with its full exportability on different satellite platforms and geographic locations will be shown and discussed.

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.

Thermal Monitoring of Eyjafjöll Volcano Eruptions by Means of Infrared MODIS Data

In the evening of 20 March 2010, after about two centuries of quiescence, an effusive eruption took place at Eyjafjöll (Iceland) volcano, from a small vent localized on the northeast flank (Fimmvörduháls 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 Eyjafjallajökull 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 Eyjafjöll volcano during 1 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 (MODIS) data, show that both effusive and explosive eruptions of the Eyjafjöll 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.

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.

Volcanic ash cloud detection from space: a comparison between the RSTASH technique and the water vapour corrected BTD procedure

Volcanic eruptions can inject large amounts (Tg) of gas and particles into the troposphere and, sometimes, into the stratosphere. Besides the main gases (H2O, CO2, SO2 and HCl), volcanic clouds contain a mix of silicate ash particles in the size range from 0.1 μm to 1 mm or larger. The interest in volcanic ash detection is high, particularly because it represents a serious hazard for air traffic. Particles with dimensions of several millimetres can damage the aircraft structure (windows, wings, ailerons), while particles less than 10 μm may be extremely dangerous for the jet engines and are undetectable by the pilots during night or in low visibility conditions. Furthermore, ash detection represents a critical step towards quantitative retrievals of plume parameters. In this paper two different satellite techniques for volcanic cloud detection and tracking are compared, namely a water vapour corrected version of the brightness temperature difference (BTD-WVC) procedure and an implementation of the robust satellite technique, specifically configured for volcanic ash (RSTASH). The BTD method identifies volcanic ash clouds on the basis of the brightness temperature difference measured in two infrared spectral bands at around 11 and 12 μm. To account for the atmospheric water vapour differential absorption in the 11–12 μm spectral range, which tends to reduce (and in some cases completely mask) the BTD signal, a water vapour correction procedure has been developed (BTD-WVC), based on measured or synthetic atmospheric profiles. RSTASH instead, is based on the analysis of a time series of satellite records, aimed at identifying signal anomalies through an automatic unsupervised change detection step. To assess the performance of the BTD-WVC and RSTASH methods in detecting volcanic ash clouds, some eruptive events of Mt Etna, observed by the Advanced Very High Resolution Radiometer (AVHRR) sensor, have been analysed. The obtained results show a good agreement between the BTD-WVC and RSTASH techniques for all the considered images, in terms of pixels detected as ‘ash affected’ (i.e. the ash cloud area). In particular, compared to the traditional BTD procedure, the BTD-WVC and RSTASH techniques significantly improve volcanic ash cloud detection, both in daytime and night-time data, especially in the case of low ash loading.

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.