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  • 1
    Online-Ressource
    Online-Ressource
    Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario
    Instituto Nazionale di Geofisica e Vulcanologia, INGV ; 2015
    In:  Annals of Geophysics Vol. 57 ( 2015-03-03)
    Details
    In: Annals of Geophysics, Instituto Nazionale di Geofisica e Vulcanologia, INGV, Vol. 57 ( 2015-03-03)
    Kurzfassung: 〈 div class="WordSection1" 〉 〈 div class="page" title="Page 1" 〉 〈 div class="layoutArea" 〉 〈 div class="column" 〉 〈 p 〉 〈 span 〉 This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010) and Grimsvötn (2011) volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjalla - jökull event, and equal to 74% for the Grimsvötn event.  〈 /span 〉 〈 /p 〉 〈 /div 〉 〈 /div 〉 〈 /div 〉 〈 p 〉 〈 em 〉 〈 br / 〉 〈 /em 〉 〈 /p 〉 〈 p 〉 〈 em 〉 〈 br / 〉 〈 /em 〉 〈 /p 〉 〈 /div 〉 〈 em 〉 〈 br clear="all" / 〉 〈 /em 〉
    Materialart: Online-Ressource
    ISSN: 2037-416X , 1593-5213
    URL: Article
    DOI: 10.4401/ag-6638
    Sprache: Englisch
    Verlag: Instituto Nazionale di Geofisica e Vulcanologia, INGV
    Publikationsdatum: 2015
    ZDB Id: 2410939-3
    SSG: 16,13
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  • 2
    Online-Ressource
    Online-Ressource
    Volcanic SO2 by UV-TIR satellite retrievals: validation by using ground-based network at Mt. Etna
    Instituto Nazionale di Geofisica e Vulcanologia, INGV ; 2015
    In:  Annals of Geophysics Vol. 57 ( 2015-03-03)
    Details
    In: Annals of Geophysics, Instituto Nazionale di Geofisica e Vulcanologia, INGV, Vol. 57 ( 2015-03-03)
    Kurzfassung: 〈 div class="page" title="Page 1" 〉 〈 div class="layoutArea" 〉 〈 div class="column" 〉 〈 p 〉 〈 span 〉 Mt. Etna volcano in Italy is one of the most active degassing volcanoes worldwide, emitting a mean of 1.7 Mt/year of Sulphur Dioxide (SO 〈 /span 〉 〈 span 〉 2 〈 /span 〉 〈 span 〉 ) in quiescent periods. In this work, SO 〈 /span 〉 〈 span 〉 2 〈 /span 〉 〈 span 〉 measurements retrieved by Moderate Resolution Imaging Spectroradiometer (MODIS), hyper-spectral Infrared Atmospheric Sounding Interferometer (IASI) and the second Global Ozone Monitoring Experiment (GOME-2) data are compared with the ground-based data from the FLux Automatic MEasurement monitoring network (FLAME). Among the eighteen lava fountain episodes occurring at Mt. Etna in 2011, the 10 April paroxysmal event has been selected as a case-study for the simultaneous observation of the SO 〈 /span 〉 〈 span 〉 2 〈 /span 〉 〈 span 〉 cloud by satellite and ground-based sensors. For each data-set two retrieval techniques were adopted and the measurements of SO 〈 /span 〉 〈 span 〉 2 〈 /span 〉 〈 span 〉 mass and flux with their respective uncertainty were obtained. With respect to the FLAME SO 〈 /span 〉 〈 span 〉 2 〈 /span 〉 〈 span 〉 mass of 4.5 Gg, MODIS, IASI and GOME-2 differ by about 10%, 15% and 30%, respectively. The SO 〈 /span 〉 〈 span 〉 2 〈 /span 〉 〈 span 〉 flux correlation coefficient between MODIS and FLAME is 0.84. All the retrievals within the respective errors are in agreement with the ground-based measurements supporting the validity of these space measurements.  〈 /span 〉 〈 /p 〉 〈 /div 〉 〈 /div 〉 〈 /div 〉
    Materialart: Online-Ressource
    ISSN: 2037-416X , 1593-5213
    URL: Article
    DOI: 10.4401/ag-6641
    Sprache: Englisch
    Verlag: Instituto Nazionale di Geofisica e Vulcanologia, INGV
    Publikationsdatum: 2015
    ZDB Id: 2410939-3
    SSG: 16,13
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
    Online-Ressource
    Online-Ressource
    Volcanic Ash Cloud Properties: Comparison Between MODIS Satellite Retrievals and FALL3D Transport Model
    Institute of Electrical and Electronics Engineers (IEEE) ; 2011
    In:  IEEE Geoscience and Remote Sensing Letters Vol. 8, No. 2 ( 2011-03), p. 248-252
    Details
    In: IEEE Geoscience and Remote Sensing Letters, Institute of Electrical and Electronics Engineers (IEEE), Vol. 8, No. 2 ( 2011-03), p. 248-252
    Materialart: Online-Ressource
    ISSN: 1545-598X , 1558-0571
    URL: Article
    DOI: 10.1109/LGRS.2010.2064156
    Sprache: Unbekannt
    Verlag: Institute of Electrical and Electronics Engineers (IEEE)
    Publikationsdatum: 2011
    ZDB Id: 2138738-2
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 4
    Online-Ressource
    Online-Ressource
    Multisensor Satellite Monitoring of the 2011 Puyehue-Cordon Caulle Eruption
    Institute of Electrical and Electronics Engineers (IEEE) ; 2014
    In:  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7, No. 7 ( 2014-7), p. 2786-2796
    Details
    In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Institute of Electrical and Electronics Engineers (IEEE), Vol. 7, No. 7 ( 2014-7), p. 2786-2796
    Materialart: Online-Ressource
    ISSN: 1939-1404 , 2151-1535
    URL: Journal
    URL: Article
    DOI: 10.1109/JSTARS.4609443
    DOI: 10.1109/JSTARS.2014.2320638
    Sprache: Unbekannt
    Verlag: Institute of Electrical and Electronics Engineers (IEEE)
    Publikationsdatum: 2014
    ZDB Id: 2457423-5
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 5
    Online-Ressource
    Online-Ressource
    A Multi-Sensor Approach for Volcanic Ash Cloud Retrieval and Eruption Characterization: The 23 November 2013 Etna Lava Fountain
    MDPI AG ; 2016
    In:  Remote Sensing Vol. 8, No. 1 ( 2016-01-12), p. 58-
    Details
    In: Remote Sensing, MDPI AG, Vol. 8, No. 1 ( 2016-01-12), p. 58-
    Materialart: Online-Ressource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs8010058
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2016
    ZDB Id: 2513863-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 6
    Online-Ressource
    Online-Ressource
    Volcanic Clouds Characterization of the 2020–2022 Sequence of Mt. Etna Lava Fountains Using MSG-SEVIRI and Products’ Cross-Comparison
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 8 ( 2023-04-13), p. 2055-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 8 ( 2023-04-13), p. 2055-
    Kurzfassung: From December 2020 to February 2022, 66 lava fountains (LF) occurred at Etna volcano (Italy). Despite their short duration (an average of about two hours), they produced a strong impact on human life, environment, and air traffic. In this work, the measurements collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board Meteosat Second Generation (MSG) geostationary satellite, are processed every 15 min to characterize the volcanic clouds produced during the activities. In particular, a quantitative estimation of volcanic cloud top height (VCTH) and ash/ice/SO2 masses’ time series are obtained. VCTHs are computed by integrating three different retrieval approaches based on coldest pixel detection, plume tracking, and HYSPLIT models, while particles and gas retrievals are realized simultaneously by exploiting the Volcanic Plume Retrieval (VPR) real-time procedure. The discrimination between ashy and icy pixels is carried out by applying the Brightness Temperature Difference (BTD) method with thresholds obtained by making specific Radiative Transfer Model simulations. Results indicate a VCTH variation during the entire period between 4 and 13 km, while the SO2, ash, and ice total masses reach maximum values of about 50, 100, and 300 Gg, respectively. The cumulative ash, ice, and SO2 emitted from all the 2020–2022 LFs in the atmosphere are about 750, 2300, and 670 Gg, respectively. All the retrievals indicate that the overall activity can be grouped into 3 main periods in which it passes from high (December 2020 to March 2021), low (March to June 2021), and medium/high (June 2021 to February 2022). The different products have been validated by using TROPOspheric Monitoring Instrument (TROPOMI) polar satellite sensor, Volcano Observatory Notices for Aviation (VONA) bulletins, and by processing the SEVIRI data considering a different and more accurate retrieval approach. The products’ cross-comparison shows a generally good agreement, except for the SO2 total mass in case of high ash/ice content in the volcanic cloud.
    Materialart: Online-Ressource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15082055
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2023
    ZDB Id: 2513863-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 7
    Online-Ressource
    Online-Ressource
    Multi-Sensor Analysis of a Weak and Long-Lasting Volcanic Plume Emission
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 23 ( 2020-11-25), p. 3866-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 23 ( 2020-11-25), p. 3866-
    Kurzfassung: Volcanic emissions are a well-known hazard that can have serious impacts on local populations and aviation operations. Whereas several remote sensing observations detect high-intensity explosive eruptions, few studies focus on low intensity and long-lasting volcanic emissions. In this work, we have managed to fully characterize those events by analyzing the volcanic plume produced on the last day of the 2018 Christmas eruption at Mt. Etna, in Italy. We combined data from a visible calibrated camera, a multi-wavelength elastic/Raman Lidar system, from SEVIRI (EUMETSAT-MSG) and MODIS (NASA-Terra/Aqua) satellites and, for the first time, data from an automatic sun-photometer of the aerosol robotic network (AERONET). Results show that the volcanic plume height, ranging between 4.5 and 6 km at the source, decreased by about 0.5 km after 25 km. Moreover, the volcanic plume was detectable by the satellites up to a distance of about 400 km and contained very fine particles with a mean effective radius of about 7 µm. In some time intervals, volcanic ash mass concentration values were around the aviation safety thresholds of 2 × 10−3 g m−3. Of note, Lidar observations show two main stratifications of about 0.25 km, which were not observed at the volcanic source. The presence of the double stratification could have important implications on satellite retrievals, which usually consider only one plume layer. This work gives new details on the main features of volcanic plumes produced during low intensity and long-lasting volcanic plume emissions.
    Materialart: Online-Ressource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12233866
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2020
    ZDB Id: 2513863-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 8
    Online-Ressource
    Online-Ressource
    Near-Real-Time Tephra Fallout Assessment at Mt. Etna, Italy
    MDPI AG ; 2019
    In:  Remote Sensing Vol. 11, No. 24 ( 2019-12-12), p. 2987-
    Details
    In: Remote Sensing, MDPI AG, Vol. 11, No. 24 ( 2019-12-12), p. 2987-
    Kurzfassung: During explosive eruptions, emergency responders and government agencies need to make fast decisions that should be based on an accurate forecast of tephra dispersal and assessment of the expected impact. Here, we propose a new operational tephra fallout monitoring and forecasting system based on quantitative volcanological observations and modelling. The new system runs at the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV-OE) and is able to provide a reliable hazard assessment to the National Department of Civil Protection (DPC) during explosive eruptions. The new operational system combines data from low-cost calibrated visible cameras and satellite images to estimate the variation of column height with time and model volcanic plume and fallout in near-real-time (NRT). The new system has three main objectives: (i) to determine column height in NRT using multiple sensors (calibrated cameras and satellite images); (ii) to compute isomass and isopleth maps of tephra deposits in NRT; (iii) to help the DPC to best select the eruption scenarios run daily by INGV-OE every three hours. A particular novel feature of the new system is the computation of an isopleth map, which helps to identify the region of sedimentation of large clasts (≥5 cm) that could cause injuries to tourists, hikers, guides, and scientists, as well as damage buildings in the proximity of the summit craters. The proposed system could be easily adapted to other volcano observatories worldwide.
    Materialart: Online-Ressource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs11242987
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2019
    ZDB Id: 2513863-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 9
    Online-Ressource
    Online-Ressource
    Near Real-Time Monitoring of the Christmas 2018 Etna Eruption Using SEVIRI and Products Validation
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 8 ( 2020-04-23), p. 1336-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 8 ( 2020-04-23), p. 1336-
    Kurzfassung: On the morning of 24 December 2018, an eruptive event occurred at Etna, which was followed the next day by a strong sequence of shallow earthquakes. The eruptive episode lasted until 30 December, ranging from moderate strombolian to lava fountain activity coupled with vigorous ash/gas emissions and a lava flow effusion toward the eastern volcano flank of Valle del Bove. In this work, the data collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instruments on board the Meteosat Second Generation (MSG) geostationary satellite are used to characterize the Etna activity by estimating the proximal and distal eruption parameters in near real time. The inversion of data indicates the onset of eruption on 24 December at 11:15 UTC, a maximum Time Average Discharge Rate (TADR) of 8.3 m3/s, a cumulative lava volume emitted of 0.5 Mm3, and a Volcanic Plume Top Height (VPTH) that reached a maximum altitude of 8 km above sea level (asl). The volcanic cloud ash and SO2 result totally collocated, with an ash amount generally lower than SO2 except on 24 December during the climax phase. A total amount of about 100 and 35 kt of SO2 and ash respectively was emitted during the entire eruptive period, while the SO2 fluxes reached peaks of more than 600 kg/s, with a mean value of about 185 kg/s. The SEVIRI VPTH, ash/SO2 masses, and flux time series have been compared with the results obtained from the ground-based visible (VIS) cameras and FLux Automatic MEasurements (FLAME) networks, and the satellite images collected by the MODerate resolution Imaging Spectroradiometer (MODIS) instruments on board the Terra and Aqua- polar satellites. The analysis indicates good agreement between SEVIRI, VIS camera, and MODIS retrievals with VPTH, ash, and SO2 estimations all within measurement errors. The SEVIRI and FLAME SO2 flux retrievals show significant discrepancies due to the presence of volcanic ash and a gap of data on the FLAME network. The results obtained in this study show the ability of geostationary satellite systems to characterize eruptive events from the source to the atmosphere in near real time during the day and night, thus offering a powerful tool to mitigate volcanic risk on both local population and airspace and to give insight on volcanic processes.
    Materialart: Online-Ressource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12081336
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2020
    ZDB Id: 2513863-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 10
    Online-Ressource
    Online-Ressource
    Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case
    Copernicus GmbH ; 2022
    In:  Atmospheric Measurement Techniques Vol. 15, No. 24 ( 2022-12-14), p. 7195-7210
    Details
    In: Atmospheric Measurement Techniques, Copernicus GmbH, Vol. 15, No. 24 ( 2022-12-14), p. 7195-7210
    Kurzfassung: Abstract. Accurate automatic volcanic cloud detection by means of satellite data is a challenging task and is of great concern for both the scientific community and aviation stakeholders due to well-known issues generated by strong eruption events in relation to aviation safety and health impacts. In this context, machine learning techniques applied to satellite data acquired from recent spaceborne sensors have shown promising results in the last few years. This work focuses on the application of a neural-network-based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. A classification of meteorological clouds and of other surfaces comprising the scene is also carried out. The neural network has been trained with MODIS (Moderate Resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallajökull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the comparable latitudes of the eruptions permit an extension of the approach to SLSTR, thereby overcoming the lack in Sentinel-3 products collected in previous mid- to high-latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared to RGB visual inspection and BTD (brightness temperature difference) procedures. Moreover, the comparison between the ash cloud obtained by the neural network (NN) and a plume mask manually generated for the specific SLSTR images considered shows significant agreement, with an F-measure of around 0.7. Thus, the proposed approach allows for an automatic image classification during eruption events, and it is also considerably faster than time-consuming manual algorithms. Furthermore, the whole image classification indicates the overall reliability of the algorithm, particularly for recognition and discrimination between volcanic clouds and other objects.
    Materialart: Online-Ressource
    ISSN: 1867-8548
    URL: Article
    DOI: 10.5194/amt-15-7195-2022
    Sprache: Englisch
    Verlag: Copernicus GmbH
    Publikationsdatum: 2022
    ZDB Id: 2505596-3
    Standort Signatur Einschränkungen Verfügbarkeit
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