Robust Satellite Techniques Research Articles

Validation of Ash/Dust Detections from SEVIRI Data Using ACTRIS/EARLINET Ground-Based LIDAR Measurements

Two tailored configurations of the Robust Satellite Technique (RST) multi-temporal approach, for airborne volcanic ash and desert dust detection, have been tested in the framework of the European Natural Airborne Disaster Information and Coordination System for Aviation (EUNADICS-AV) project. The two algorithms, running on Spinning Enhanced Visible Infra-Red Imager (SEVIRI) data, were previously assessed over wide areas by comparison with independent satellite-based aerosol products. In this study, we present results of a first validation analysis of the above mentioned satellite-based ash/dust products using independent, ground-based observations coming from the European Aerosol Research Lidar Network (EARLINET). The aim is to assess the capabilities of RST-based ash/dust products in providing useful information even at local scale and to verify their applicability as a “trigger” to timely activate EARLINET measurements during airborne hazards. The intense Saharan dust event of May 18–23 2008—which affected both the Mediterranean Basin and Continental Europe—and the strong explosive eruptions of Eyjafjallajökull (Iceland) volcano of April–May 2010, were analyzed as test cases. Our results show that both RST-based algorithms were capable of providing reliable information about the investigated phenomena at specific sites of interest, successfully detecting airborne ash/dust in different geographic regions using both nighttime and daytime SEVIRI data. However, the validation analysis also demonstrates that ash/dust layers remain undetected by satellite in the presence of overlying meteorological clouds and when they are tenuous (i.e., with an integrated backscatter coefficient less than ~0.001 sr−1 and with aerosol backscatter coefficient less than ~1 × 10−6 m−1sr−1). This preliminary analysis confirms that the continuity of satellite-based observations can be used to timely “trigger” ground-based LIDAR measurements in case of airborne hazard events. Finally, this work confirms that advanced satellite-based detection schemes may provide a relevant contribution to the monitoring of ash/dust phenomena and that the synergistic use of (satellite-based) large scale, continuous and timely records with (ground-based) accurate and quantitative measurements may represent an added value, especially in operational scenarios.

The VIIRS-Based RST-FLARE Configuration: The Val d’Agri Oil Center Gas Flaring Investigation in Between 2015–2019

The RST (Robust Satellite Techniques)-FLARE algorithm is a satellite-based method using a multitemporal statistical analysis of nighttime infrared signals strictly related to industrial hotspots, such as gas flares. The algorithm was designed for both identifying and characterizing gas flares in terms of radiant/emissive power. The Val d’Agri Oil Center (COVA) is a gas and oil pre-treatment plant operating for about two decades within an anthropized area of Basilicata region (southern Italy) where it represents a significant potential source of social and environmental impacts. RST-FLARE, developed to study and monitor the gas flaring activity of this site by means of MODIS (Moderate Resolution Imaging Spectroradiometer) data, has exported VIIRS (Visible Infrared Imaging Radiometer Suite) records by exploiting the improved spatial and spectral properties offered by this sensor. In this paper, the VIIRS-based configuration of RST-FLARE is presented and its application on the recent (2015-2019) gas flaring activity at COVA is analyzed and discussed. Its performance in gas flaring characterization is in good agreement with VIIRS Nightfire outputs to which RST-FLARE seems to provide some add-ons. The great consistency of radiant heat estimates computed with both RST-FLARE developed configurations allows proposing a multi-sensor RST-FLARE strategy for a more accurate multi-year analysis of gas flaring.

Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions

In recent years, the risk related to oil spill accidents has significantly increased due to a global growth in offshore extraction and oil maritime transport. To ensure sea safety, the implementation of a monitoring system able to provide real-time coverage of large areas and a timely alarm in case of accidents is of major importance. Satellite remote sensing, thanks to its inherent peculiarities, has become an essential component in such a system. Recently, the general Robust Satellite Technique (RST) approach has been successfully applied to oil spill detection (RST-OIL) using optical band satellite data. In this paper, an advanced configuration of RST-OIL is presented, and we aim to extend its applicability to a larger set of observation conditions, referring, in particular, to those in the presence of severe sun glint effects that generate some detection limits to the RST-OIL standard algorithm. To test such a configuration, the DeepWater Horizon platform accident from April 2010 was selected as a test case. We analyzed a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images that are usually significantly affected by sun glint in the Gulf of Mexico area. The accuracy of the achieved results was evaluated for comparison with a well-established satellite methodology based on microwave data, which confirms the potential of the proposed approach in identifying the oil presence on the scene with good accuracy and reliability, even in these severe conditions.

Tropospheric and Ionospheric Anomalies Induced by Volcanic and Saharan Dust Events as Part of Geosphere Interaction Phenomena

In this work, we assessed the possible relation of ionospheric perturbations observed by Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions (DEMETER), Global Positioning System total electron content (GPS TEC), National Oceanic and Atmospheric Administration (NOAA)-derived outgoing longwave-Earth radiation (OLR), and atmospheric chemical potential (ACP) measurements, with volcanic and Saharan dust events identified by ground and satellite-based medium infrared/thermal infrared (MIR/TIR) observations. The results indicated that the Mt. Etna (Italy) volcanic activity of 2006 was probably responsible for the ionospheric perturbations revealed by DEMETER on 4 November and 6 December and by GPS TEC observations on 4 November and 12 December. This activity also affected the OLR (on 26 October; 6 and 23 November; and 2, 6, and 14 December) and ACP (on 31 October–1 November) analyses. Similarly, two massive Saharan dust episodes, detected by Robust Satellite Techniques (RST) using Spinning Enhanced Visible and Infrared Imager (SEVIRI) optical data, probably caused the ionospheric anomalies recorded, based on DEMETER and GPS TEC observations, over the Mediterranean basin in May 2008. The study confirmed the perturbing effects of volcanic and dust events on tropospheric and ionospheric parameters. Further, it demonstrated the advantages of using independent satellite observations to investigate atmospheric phenomena, which may not always be well documented. The impact of this increased detection capacity in reducing false positives, in the framework of a short-term seismic hazard forecast based on the study of ionospheric and tropospheric anomalies, is also addressed.

A statistical analysis of TIR anomalies extracted by RSTs in relation to an earthquake in the Sichuan area using MODIS LST data

Abstract. Research in the field of earthquake prediction has a long history, but the inadequacies of traditional approaches to the study of seismic threats have become increasingly evident. Remote sensing and Earth observation technology, an emerging method that can rapidly capture information concerning anomalies associated with seismic activity across a wide geographic area, has for some time been believed to be the key to overcoming the bottleneck in earthquake prediction studies. However, a multi-parametric method appears to be the most promising approach for increasing the reliability and precision of short-term seismic hazard forecasting, and thermal infrared (TIR) anomalies are important earthquake precursors. While several studies have investigated the correlation among TIR anomalies identified by the robust satellite techniques (RSTs) methodology and single earthquakes, few studies have extracted TIR anomalies over a long period within a large study area. Moreover, statistical analyses are required to determine whether TIR anomalies are precursors to earthquakes. In this paper, RST data analysis and the Robust Estimator of TIR Anomalies (RETIRA) index were used to extract the TIR anomalies from 2002 to 2018 in the Sichuan region using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, while the earthquake catalog was used to ascertain the correlation between TIR anomalies and earthquake occurrences. Most TIR anomalies corresponded to earthquakes, and statistical methods were used to verify the correlation between the extracted TIR anomalies and earthquakes. This is the first time that the ability to predict earthquakes has been evaluated based on the positive predictive value (PPV), false discovery rate (FDR), true-positive rate (TPR), and false-negative rate (FNR). The statistical results indicate that the prediction potential of RSTs with use of MODIS is limited with regard to the Sichuan region.

On the Potential of RST-FLOOD on Visible Infrared Imaging Radiometer Suite Data for Flooded Areas Detection

Timely and continuous information about flood spatiotemporal evolution are fundamental to ensure an effective implementation of the relief and rescue operations in case of inundation events. In this framework, satellite remote sensing may provide a valuable contribution provided that robust data analysis methods are implemented and suitable data, in terms of spatial, spectral and temporal resolutions, are employed. In this paper, the Robust Satellite Techniques (RST) approach, a satellite-based differential approach, already applied at detecting flooded areas (and therefore christened RST-FLOOD) with good results on different polar orbiting optical sensors (i.e., Advanced Very High Resolution Radiometer – AVHRR – and Moderate Resolution Imaging Spectroradiometer – MODIS), has been fully implemented on time series of Suomi National Polar-orbiting Partnership (Suomi-NPP-SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data. The flooding event affecting the Metaponto Plain in Basilicata and Puglia regions (southern Italy) in December 2013 was selected as a case study and investigated by analysing five years (only December month) of VIIRS Imagery bands at 375 m spatial resolution. The achieved results clearly indicate the potential of the proposed approach, especially when compared with a satellite-based high resolution map of flooded area, as well as with the official flood hazard map of the area and the outputs of a recent published VIIRS-based method. Both flood extent and dynamics have been recognized with good reliability during the investigated period, with only a residual 11.5% of possible false positives over an inundated area extent of about 73 km2. In addition, a flooded area of about 18 km2 was found outside the hazard map, suggesting it requires updating to better manage flood risk and prevent future damages. Finally, the achieved results indicate that medium-resolution optical data, if analysed with robust methodologies like RST-FLOOD, can be suitable for detecting and monitoring floods also in case of small hydrological basins.

Using Robust Satellite Technique (RST) to determine thermal anomalies before a strong earthquake: A case study of the Saravan earthquake (April 16th, 2013, MW = 7.8, Iran)

Employing an efficient and robust prediction system along with early warnings is useful to reduce the effects of devastating earthquakes. Thermal anomaly, as an earthquake precursory, is widely confirmed. In this study, to characterize the thermal anomalies within a 10-year specific observation interval of Land Surface Temperature (LST), satellite data products obtained from MODIS-Aqua (March 16th to May 16th, every year from 2004 to 2013) were analyzed using Robust Satellite Technique (RST) method. The Saravan earthquake (April 16th, 2013, MW = 7.8, Iran) were used for validation phase, and the year 2012, as a relatively seismically unperturbed year (no earthquakes with Mw > 3.5 regarding target time and region), was taken for confutation phase. For the first time, to reduce the effects of the topography with respect to vegetation cover, using unsupervised classification, the study area was divided into the low and high elevation regions. The results of the present study showed the Significant Sequences of LST Anomalies (SSLAs) from several days before and immediately after the Saravan earthquake appeared in the north, south, and center of the study area. However, no anomalies were observed in a seismically unperturbed year (2012). Therefore, the results confirmed the existence of space-time thermal anomalies related to destructive earthquakes and revealed that the RST method is a reliable approach to detect disturbances of the Earth’s thermal emission.

ТЕПЛОВЫЕ АНОМАЛИИ БАРАБИНСКОЙ НИЗМЕННОСТИ. ПЕРВЫЕ РЕЗУЛЬТАТЫ

We present the results of the development of RST (Robust Satellite Technique) method to detect statistical deviations in spatial-time series of satellite land surface temperature data under the Baraba.

The Contribution of Multi-Sensor Infrared Satellite Observations to Monitor Mt. Etna (Italy) Activity during May to August 2016

In May 2016, three powerful paroxysmal events, mild Strombolian activity, and lava emissions took place at the summit crater area of Mt. Etna (Sicily, Italy). During, and immediately after the eruption, part of the North-East crater (NEC) collapsed, while extensive subsidence affected the Voragine crater (VOR). Since the end of the May eruptions, a diffuse fumarolic activity occurred from a fracture system that cuts the entire summit area. Starting from 7 August, a small vent (of ~20–30 m in diameter) opened up within the VOR crater, emitting high-temperature gases and producing volcanic glow which was visible at night. We investigated those volcanic phenomena from space, exploiting the information provided by the satellite-based system developed at the Institute of Methodologies for Environmental Analysis (IMAA), which monitors Italian volcanoes in near-real time by means of the RSTVOLC (Robust Satellite Techniques–volcanoes) algorithm. Results, achieved integrating Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) observations, showed that, despite some issues (e.g., in some cases, clouds masking the underlying hot surfaces), RSTVOLC provided additional information regarding Mt. Etna activity. In particular, results indicated that the Strombolian eruption of 21 May lasted longer than reported by field observations or that a short-lived event occurred in the late afternoon of the same day. Moreover, the outcomes of this study showed that the intensity of fumarolic emissions changed before 7 August, as a possible preparatory phase of the hot degassing activity occurring at VOR. In particular, the radiant flux retrieved from MODIS data decreased from 30 MW on 4 July to an average value of about 7.5 MW in the following weeks, increasing up to 18 MW a few days before the opening of a new degassing vent. These outcomes, in accordance with information provided by Sentinel-2 MSI (Multispectral Instrument) and Landsat 8-OLI (Operational Land Imager) data, confirm that satellite observations may also contribute greatly to the monitoring of active volcanoes in areas where efficient traditional surveillance systems exist.

Analyzing the December 2013 Metaponto Plain (Southern Italy) Flood Event by Integrating Optical Sensors Satellite Data

Timely and continuous information about flood dynamics are fundamental to ensure an effective implementation of the relief and rescue operations. Satellite data provided by optical sensors onboard meteorological satellites could have great potential in this framework, offering an adequate trade-off between spatial and temporal resolution. The latest would benefit from the integration of observations coming from different satellite systems, also helping to increase the probability of finding cloud free images over the investigated region. The Robust Satellite Techniques for detecting flooded areas (RST-FLOOD) is a sensor-independent multi-temporal approach aimed at detecting flooded areas which has already been applied with good results on different polar orbiting optical sensors. In this work, it has been implemented on both the 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375 m Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The flooding event affecting the Basilicata and Puglia regions (southern Italy) in December 2013 has been selected as a test case. The achieved results confirm the RST-FLOOD potential in reliably detecting, in case of small basins, flooded areas regardless of the sensor used. Flooded areas have indeed been detected with similar performance by the two sensors, allowing for their continuous and near-real time monitoring.

Investigating the chlorophyll-a variability in the Gulf of Taranto (North-western Ionian Sea) by a multi-temporal analysis of MODIS-Aqua Level 3/Level 2 data

The analysis of chlorophyll-a (chl-a) variability on a long-term basis could allow detecting possible issues in the whole marine ecosystem functioning. The Gulf of Taranto (Southern Italy), in the North-western Ionian Sea (Mediterranean Sea), has been affected by several environmental threats in the last decade, thus deserving the implementation of an adequate monitoring system able to provide accurate indications about the variability of the most relevant bio-optical parameters.In this context, the main objectives of this study are to investigate the long-term chl-a variability in the Gulf of Taranto and identify the occurrence of any past spatiotemporal anomalies by implementing the multi-temporal Robust Satellite Technique (RST) on a 12-year (2003–2015) period of MODIS/AQUA Level 3/Level 2 chlorophyll-a data. The achieved results show well-clustered near-surface positive chl-a anomalies during the January–February 2011 period. This detected offshore phytoplankton bloom may be related to sub-basin processes, such as the inflow of the Western Adriatic Coastal Current (WACC), probably fostered by the cyclonic reversal of the Bimodal Oscillating System (BiOS) mechanism. Therefore, the RST approach proved successful in detecting chl-a anomalous variations with a high level of confidence regardless of the absolute value measured, thus suggesting its exportability in other areas with different site-setting conditions.

Remote Sensed Land Surface Temperature Anomalies for Earthquake Prediction

Thermal anomaly Detection prior to earthquake events has been widely confirmed by researchers over the past decade. In this paper, we use robust satellite technique approach (RST) on a collection of six years of MODIS satellite data, representing land surface temperature (LST) images to predict 21st May 2003 Boumerdès Algeria earthquake. The thermal anomalies results were compared with the ambient temperature variation measured in three meteorological stations of Algerian National Office of Meteorology (ONM) (DELLYS-AFIR, TIZI-OUZOU, and DAR-EL-BEIDA). The results confirm the importance of robust satellite technique as an approach highly effective for monitoring the earthquakes.

A MODIS-Based Robust Satellite Technique (RST) for Timely Detection of Oil Spilled Areas

Natural crude-oil seepages, together with the oil released into seawater as a consequence of oil exploration/production/transportation activities, and operational discharges from tankers (i.e., oil dumped during cleaning actions) represent the main sources of sea oil pollution. Satellite remote sensing can be a useful tool for the management of such types of marine hazards, namely oil spills, mainly owing to the synoptic view and the good trade-off between spatial and temporal resolution, depending on the specific platform/sensor system used. In this paper, an innovative satellite-based technique for oil spill detection, based on the general robust satellite technique (RST) approach, is presented. It exploits the multi-temporal analysis of data acquired in the visible channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite in order to automatically and quickly detect the presence of oil spills on the sea surface, with an attempt to minimize “false detections” caused by spurious effects associated with, for instance, cloud edges, sun/satellite geometries, sea currents, etc. The oil spill event that occurred in June 2007 off the south coast of Cyprus in the Mediterranean Sea has been considered as a test case. The resulting data, the reliability of which has been evaluated by both carrying out a confutation analysis and comparing them with those provided by the application of another independent MODIS-based method, showcase the potential of RST in identifying the presence of oil with a high level of accuracy.

On the Potential of Robust Satellite Techniques Approach for SPM Monitoring in Coastal Waters: Implementation and Application over the Basilicata Ionian Coastal Waters Using MODIS‐Aqua

Monitoring river plume dynamics and variations in complex coastal areas can provide useful information to prevent marine environmental damage. In this work, the Robust Satellite Techniques (RST) approach has been implemented and tested on historical series of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data to monitor, for the first time, Suspended Particulate Matter (SPM) anomalies associated to river plumes. To this aim, MODIS-Aqua Level 1A data were processed using an atmospheric correction adequate for coastal waters, and SPM daily maps were generated applying an algorithm adapted from literature. The RST approach was then applied to these maps to assess the anomalous presence of SPM. The study area involves the Basilicata region coastal waters (Ionian Sea, South of Italy). A long-time analysis (2003–2015) conducted for the month of December allows us to find that the maximum SPM concentration value was registered in December 2013, when an extreme hydrological event occurred. A short-time analysis was then carried out applying RST to monitor the dynamics of anomalous SPM concentrations. Finally, the most exposed areas, in terms of SPM concentration, were identified. The results obtained in this work showed the RST high potential when used in combination with standard SPM daily maps to better characterize and monitor coastal waters.

RST-FIRES, an exportable algorithm for early-fire detection and monitoring: description, implementation, and field validation in the case of the MSG-SEVIRI sensor

Abstract Wildfires are a worldwide phenomenon with local and global effects. Many satellite-based methods for fire detection and monitoring have been developed to exploit data acquired by sensors onboard polar orbiting platforms. Their relatively low temporal resolution (hours) is, however, decidedly not adequate for detecting short-living events or fires characterised by a marked diurnal cycle and rapid evolution times. Geostationary satellites have very high temporal resolution of 30 to 2.5 min and could, in principle, be more suitable for providing timely alerts and facilitating possible mitigation actions. However, such short revisit times are coupled with spatial resolutions of 3–5 km, which are coarser than those of the polar orbiting sensors generally having 1 km capability in the infrared region. This could represent a significant limitation for small fire detection and precise localization. However, unlike polar orbiting systems, the geostationary attitude assures very stable observational conditions at the pixel level, including fixed view angles, same time of day, and same ground resolution cell dimensions. This can be crucial for fire-detection methods based on multi-temporal analyses. This work describes in detail the Robust Satellite Techniques for FIRES detection and monitoring (RST-FIRES), a multi-temporal change-detection technique, and its application to the data of Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board the Meteosat Second Generation (MSG) platform. Its performance in terms of reliability and sensitivity was verified by > 20,000 SEVIRI images collected throughout the day during a four-year-collaboration with the regional Civil Protection Departments and other Local Authorities of two Italian regions which provided about 950 near real-time ground and aerial checks of the RST-FIRES detections. The results indicate a mean commission error rate of 18%, ranging from 3% to 30% according to the specific experimental campaign, with an average omission error of 44%, ranging from 3% to 66% depending on the campaign and the validation source used. This study fully demonstrates the added value of the RST-FIRES technique for the early warning of fire events. During the considered validation campaigns, RST-FIRES provided the sole fire alert in 348 cases; in further 227 cases, its warnings were given > 1 h before any other source of information was provided. Finally, this paper presents and discusses a comparison among RST-FIRES and other SEVIRI-based fire detection products. On the whole, RST-FIRES is shown to be 3 to 70 times more sensitive than all of the other considered SEVIRI-based products. This satisfactory result was not completely unexpected, considering the quite high false positive rate exhibited by RST-FIRES and the fact that other algorithms were designed to work in a wider area, from continental to the SEVIRI full disk.

On the potential of an RST-based analysis of the MODIS-derived chl-a product over Condor seamount and surrounding areas (Azores, NE Atlantic)

Oceanographic cruises have been conducted on the Condor seamount (SW Faial Island, Azores archipelago, NE Atlantic) since 2009 to collect in situ data and understand potential seamount effects on local biodiversity. Satellite data have been concurrently collected to infer the space-time upper-ocean optical property variability and the associated physical processes. The main limitation of this analysis is the persistent and significant cloud coverage above the region that, especially in some seasons, can significantly hinder satellite data availability. This study was meant to test the robust satellite technique (RST) over the Condor seamount, assess its capability to estimate multiyear trends and identify space-time anomalies. To this aim, 11-year MODIS/AQUA level 2-derived chlorophyll-a (chl-a) data were used. Results achieved for October 2010 show, within a large-scale analysis, the presence of well-defined areas of near-surface chl-a anomalies, highlighting the occurrence of a trapping effect due to flow-topography interaction processes. Regarding the Condor area, the chl-a anomalies detected along the eastern side of the seamount were linked to a strong vertical mixing that provided sufficient inorganic nutrients requested for productivity. The achieved results, whose accuracy was also tested through a comparison with in situ data, are consistent with those independently obtained by other authors who described the phytoplankton variability around the Condor seamount. This study shows the high potential of the RST approach to assess the chl-a variability in the space-time domain in oligotrophic regions such as the Azores, allowing the identification of the most important areas to be preserved and/or managed.

Long-Term RST Analysis of Anomalous TIR Sequences in Relation with Earthquakes Occurred in Greece in the Period 2004–2013

AbstractReal-time integration of multi-parametric observations is expected to accelerate the process toward improved, and operationally more effective, systems for time-Dependent Assessment of Seismic Hazard (t-DASH) and earthquake short-term (from days to weeks) forecast. However, a very preliminary step in this direction is the identification of those parameters (chemical, physical, biological, etc.) whose anomalous variations can be, to some extent, associated with the complex process of preparation for major earthquakes. In this paper one of these parameters (the Earth’s emitted radiation in the Thermal InfraRed spectral region) is considered for its possible correlation withM ≥ 4 earthquakes occurred in Greece in between 2004 and 2013. The Robust Satellite Technique (RST) data analysis approach and Robust Estimator of TIR Anomalies (RETIRA) index were used to preliminarily define, and then to identify, significant sequences of TIR anomalies (SSTAs) in 10 years (2004–2013) of daily TIR images acquired by the Spinning Enhanced Visible and Infrared Imager on board the Meteosat Second Generation satellite. Taking into account the physical models proposed for justifying the existence of a correlation among TIR anomalies and earthquake occurrences, specific validation rules (in line with the ones used by the Collaboratory for the Study of Earthquake Predictability—CSEP—Project) have been defined to drive a retrospective correlation analysis process. The analysis shows that more than 93 % of all identified SSTAs occur in the prefixed space–time window around (M ≥ 4) earthquake's time and location of occurrence with a false positive rate smaller than 7 %. Molchan error diagram analysis shows that such a correlation is far to be achievable by chance notwithstanding the huge amount of missed events due to frequent space/time data gaps produced by the presence of clouds over the scene. Achieved results, and particularly the very low rate of false positives registered on a so long testing period, seems already sufficient (at least) to qualify TIR anomalies (identified by RST approach and RETIRA index) among the parameters to be considered in the framework of a multi-parametric approach to t-DASH.

A review of RST VOLC , 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 RST VOLC 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 RST VOLC 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 RST VOLC . 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.

Robust Satellite Techniques (RST) for monitoring earthquake prone areas by satellite TIR observations: The case of 1999 Chi-Chi earthquake (Taiwan)

For more than 13years a multi-temporal data-analysis method, named Robust Satellite Techniques (RST), has been being applied to satellite Thermal InfraRed (TIR) monitoring of seismically active regions. It gives a clear definition of a TIR anomaly within a validation/confutation scheme devoted to verify if detected anomalies can be associated or not to the time and location of the occurrence of major earthquakes. In this scheme, the confutation part (i.e. verifying if similar anomalies do not occur in the absence of a significant seismic activity) assumes a role even much important than the usual validation component devoted to verify the presence of anomalous signal transients before (or in association with) specific seismic events. Since 2001, RST approach has been being used to study tens of earthquakes with a wide range of magnitudes (from 4.0 to 7.9) occurred in different continents and in various geo-tectonic settings.In this paper such a long term experience is exploited in order to give a quantitative definition of a significant sequence of TIR anomalies (SSTA) in terms of the required space–time continuity constraints (persistence), identifying also the different typologies of known spurious sequences of TIR anomalies that have to be excluded from the following validation steps. On the same basis, taking also into account for the physical models proposed for justifying the existence of a correlation between TIR anomalies and earthquakes occurrence, specific validation rules (in line with the ones used by the Collaboratory for the Study of Earthquake Predictability – CSEP – Project) have been defined to drive the validation process. In this work, such an approach is applied for the first time to a long-term dataset of night-time GMS-5/VISSR (Geostationary Meteorological Satellite/Visible and Infrared Spin-Scan Radiometer) TIR measurements, comparing SSTAs and earthquakes with M>4 which occurred in a wide area around Taiwan, in the month of September of different years (from 1995 to 2002). In this dataset the Chi-Chi earthquake (MW=7.6) which occurred on September 20, 1999 represents the major, but not unique, event. The analysis shows that all identified SSTAs occur in the pre-fixed space–time window around (in terms of time and location) earthquakes with M>4. The false positive rate remains zero even if only earthquakes with M>4.5 are considered. In the case of the Chi-Chi earthquake, 3 SSTAs were identified (all within the established space–time correlation window), one of them appearing about 2weeks before and very close to the epicentre of the earthquake just along the associated tectonic lineaments. The wide considered space–time window, together with the high seismicity of the considered area, surely positively conditioned the achieved results, so that further analyses should be carried out by using longer datasets and different geographic areas. However, also considering the coincidence with other (possible) precursor phenomena, independently reported (particularly within the iSTEP project) at the time of the Chi Chi earthquake, achieved results seem already sufficient (at least) to qualify TIR anomalies (identified by RST) among the parameters to be considered in the framework of a multi-parametric approach to a time-Dependent Assessment of Seismic Hazard (t-DASH).

A satellite-based analysis of the Val d'Agri Oil Center (southern Italy) gas flaring emissions

Abstract. 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 (R2 of 0.83), emitted gas flaring volumes at COVA.