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Assessment and Prediction of Volcano Hazard Using Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 25657

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Dr. Gaetana Ganci

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Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, 95125 Catania, Italy
Interests: remote sensing application; topographic data; satellite images analysis
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Dr. Társilo Girona

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Geophysical Institute, Alaska Volcano Observatory, University of Alaska Fairbanks, 2156 N Koyukuk Dr, Fairbanks, AK 99775, USA
Interests: natural hazards; volcanoes; earthquakes; machine-learning; physics-based modeling; spectroscopy
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Dr. Simona Scollo

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Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, 95125 Catania, Italy
Interests: remote sensing application; analysis of the explosive activity and eruption dynamics
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Dr. Nicolas Theys

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Royal Belgian Institute for Space Aeronomy (BIRA-IASB), 3 Avenue Circulaire, 1180 Brussels, Belgium
Interests: satellite; remote sensing; UV-visible; thermal infrared; volcanoes; emissions; sulfur dioxide; aerosols; aviation hazards
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Special Issue Information

Dear Colleagues,

The accurate forecasting and characterization of volcanic activity, and the assessment of their potential impact on land and population, is an open challenge given the inhomogeneity of monitoring networks around active volcanoes and the wide range of eruptive styles and volcanic phenomena. For the last forty years, satellite- and ground-based remote sensing techniques have been extensively used to monitor volcanoes worldwide. Satellite measurements are, for example, a very useful tool for estimating volcano deformation, thermal activity, SO₂/ash extension and amount, and lava flow mapping. In addition, ground-based remote sensing systems, ranging from low cost cameras and drones to more expensive instruments (e.g., radars and lidars, FTIR), are able to estimate important eruption source parameters in a similar way to satellite measurements but with different spatiotemporal resolution and sensitivity. All those measurements are fundamental to effectively track the evolution of volcanoes and enhance physics-based dynamic models that link those spatial and temporal observations with volcanic phenomena. In this context, ensemble-based data assimilation approaches have been successfully implemented to model time-varying ground deformation observations from interferometric synthetic-aperture radar (InSAR), or to forecast volcanic ash and SO₂ dispersal in the atmosphere. Integrating remote sensing observations and models is fundamental to forecasting volcanic activity and impact in near-real time.

We invite papers dealing with the integration of satellite- and ground-based remote sensing observations into modelling with the aim to nowcast and possibly forecast volcanic hazards and their impact. Contributions on novel methodologies and applications are welcome.

You may choose our Joint Special Issue in Forecasting.

Dr. Gaetana Ganci
Dr. Társilo Girona
Dr. Simona Scollo
Dr. Nicolas Theys
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

modeling of volcanic processes
forecasting volcanic hazards
satellite data
ground-based remote sensing techniques
data assimilation

Published Papers (10 papers)

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Research

28 pages, 40628 KiB

Open AccessArticle

Characterizing and Mapping Volcanic Flow Deposits on Mount St. Helens via Dual-Band SAR Imagery

by Nikola Rogic, Sylvain J. Charbonnier, Franco Garin, Guy W. Dayhoff II, Eric Gagliano, Mel Rodgers, Charles B. Connor, Sameer Varma and David Shean

Remote Sens. 2023, 15(11), 2791; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112791 - 27 May 2023

Cited by 2 | Viewed by 1501

Abstract

Mapping volcanic flow deposits can be achieved by considering backscattering characteristics as a metric of surface roughness. In this study, we developed an approach to extract a measure of surface roughness from dual-band airborne Synthetic Aperture Radar (ASAR) backscattering data to characterize and map various volcanic flow deposits—namely, debris avalanches, lahars, lava flows, and pyroclastic density currents. We employed ASAR and Indian Space Research Organization (ISRO) airborne SAR datasets, from a joint project (ASAR-ISRO), acquired in December 2019 at 2 m spatial resolution, to assess the role and importance of incorporating dual-band data, i.e., L-band and S-band, into surface roughness models. Additionally, we derived and analyzed surface roughness from a digital surface model (DSM) generated from unoccupied aircraft systems (UAS) acquisitions using Structure from Motion (SfM) photogrammetry techniques. These UAS-derived surface roughness outputs served as meter-scale calibration products to validate the radar roughness data over targeted areas. Herein, we applied our method to a region in the United States over the Mount St. Helens volcano in the Cascade Range of Washington state. Our results showed that dual-band systems can be utilized to characterize different types of volcanic deposits and range of terrain roughness. Importantly, we found that a combination of radar wavelengths (i.e., 9 and 24 cm), in tandem with high-spatial-resolution backscatter measurements, yields improved surface roughness maps, compared to single-band, satellite-based approaches at coarser resolution. The L-band (24 cm) can effectively differentiate small, medium, and large-scale structures, namely, blocks/boulders from fine-grained lahar deposits and hummocks from debris avalanche deposits. Additionally, variation in the roughness estimates of lahar and debris avalanche deposits can be identified and quantified individually. In contrast, the S-band (9 cm) can distinguish different soil moisture conditions across variable terrain; for example, identify wet active channels. In principle, this dual-band approach can also be employed with time series of various other SAR data of higher coherence (such as satellite SAR), using different wavelengths and polarizations, encompassing a wider range of surface roughness, and ultimately enabling additional applications at other volcanoes worldwide and even beyond volcanology. Full article

(This article belongs to the Special Issue Assessment and Prediction of Volcano Hazard Using Remote Sensing)

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22 pages, 7040 KiB

Open AccessArticle

Volcanic Clouds Characterization of the 2020–2022 Sequence of Mt. Etna Lava Fountains Using MSG-SEVIRI and Products’ Cross-Comparison

by Lorenzo Guerrieri, Stefano Corradini, Nicolas Theys, Dario Stelitano and Luca Merucci

Remote Sens. 2023, 15(8), 2055; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15082055 - 13 Apr 2023

Cited by 4 | Viewed by 1653

Abstract

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. Full article

(This article belongs to the Special Issue Assessment and Prediction of Volcano Hazard Using Remote Sensing)

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22 pages, 58822 KiB

Open AccessArticle

Volcanic Cloud Detection and Retrieval Using Satellite Multisensor Observations

by Francesco Romeo, Luigi Mereu, Simona Scollo, Mario Papa, Stefano Corradini, Luca Merucci and Frank Silvio Marzano

Remote Sens. 2023, 15(4), 888; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15040888 - 05 Feb 2023

Cited by 2 | Viewed by 1631

Abstract

Satellite microwave (MW) and millimetre-wave (MMW) passive sensors can be used to detect volcanic clouds because of their sensitivity to larger volcanic particles (i.e., size bigger than 20 µm). In this work, we combine the MW-MMW observations with thermal-infrared (TIR) radiometric data from the Low Earth Orbit (LEO) spectroradiometer to have a complete characterisation of volcanic plumes. We describe new physical-statistical methods, which combine machine learning techniques, aimed at detecting and retrieving volcanic clouds of two highly explosive eruptions: the 2014 Kelud and 2015 Calbuco test cases. For the detection procedure, we compare the well-known split-window methods with a machine learning algorithm named random forest (RF). Our work highlights how the machine learning method is suitable to detect volcanic clouds using different spectral signatures without fixing a threshold. Moreover, the RF model allows images to be automatically processed with promising results (90% of the area correctly identified). For the retrieval procedure of the mass of volcanic particles, we consider two methods, one based on the maximum likelihood estimation (MLE) and one using the neural network (NN) architecture. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands. Summing the MW-MMW and TIR estimates, we obtain the following masses: 1.11 ± 0.40 × 10¹¹ kg (MLE method) and 1.32 ± 0.47 × 10¹¹ kg (NN method) for Kelud; 4.48 ± 1.61 × 10¹⁰ kg (MLE method) and 4.32 ± 1.56 × 10¹⁰ kg (NN method) for Calbuco. This work shows how machine learning techniques can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near-source volcanic clouds. Full article

(This article belongs to the Special Issue Assessment and Prediction of Volcano Hazard Using Remote Sensing)

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17 pages, 12730 KiB

Open AccessArticle

Coupling Flank Collapse and Magma Dynamics on Stratovolcanoes: The Mt. Etna Example from InSAR and GNSS Observations

by Giuseppe Pezzo, Mimmo Palano, Lisa Beccaro, Cristiano Tolomei, Matteo Albano, Simone Atzori and Claudio Chiarabba

Remote Sens. 2023, 15(3), 847; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030847 - 02 Feb 2023

Cited by 9 | Viewed by 2393

Abstract

Volcano ground deformation is a tricky puzzle in which different phenomena contribute to the surface displacements with different spatial–temporal patterns. We documented some high variable deformation patterns in response to the different volcanic and seismic activities occurring at Mt. Etna through the January 2015–March 2021 period by exploiting an extensive dataset of GNSS and InSAR observations. The most spectacular pattern is the superfast seaward motion of the eastern flank. We also observed that rare flank motion reversal indicates that the short-term contraction of the volcano occasionally overcomes the gravity-controlled sliding of the eastern flank. Conversely, fast dike intrusion led to the acceleration of the sliding flank, which could potentially evolve into sudden collapses, fault creep, and seismic release, increasing the hazard. A better comprehension of these interactions can be of relevance for addressing short-term scenarios, yielding a tentative forecasting of the quantity of magma accumulating within the plumbing system. Full article

(This article belongs to the Special Issue Assessment and Prediction of Volcano Hazard Using Remote Sensing)

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22 pages, 20353 KiB

Open AccessArticle

Satellite Radar and Camera Time Series Reveal Transition from Aligned to Distributed Crater Arrangement during the 2021 Eruption of Cumbre Vieja, La Palma (Spain)

by Valeria Muñoz, Thomas R. Walter, Edgar U. Zorn, Alina V. Shevchenko, Pablo J. González, Diego Reale and Eugenio Sansosti

Remote Sens. 2022, 14(23), 6168; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236168 - 06 Dec 2022

Cited by 10 | Viewed by 3039

Abstract

Magma-filled dikes may feed erupting fissures that lead to alignments of craters developing at the surface, yet the details of activity and migrating eruptions at the crater row are difficult to monitor and are hardly understood. The 2021 Tajogaite eruption at the Cumbre Vieja, La Palma (Spain), lasted 85 days and developed a pronounced alignment of craters that may be related to changes within the volcano edifice. Here, we use COSMO-SkyMed satellite radar data and ground-based time-lapse photographs, offering a high-resolution dataset to explore the locations and characteristics of evolving craters. Our results show that the craters evolve both gradually and suddenly and can be divided into three main phases. Phase 1, lasting the first 6 weeks of the eruption, was characterized by a NW–SE linear evolution of up to seven craters emerging on the growing cone. Following two partial collapses of the cone to the northwest and a seismicity increase at depth, Phase 2 started and caused a propagation of the main activity toward the southeastern side, together with the presence of up to 11 craters along this main NW–SE trend. Associated with strong deep and shallow earthquakes, Phase 3 was initiated and continued for the final 2 weeks of the eruption, expressed by the development of up to 18 craters, which became dominant and clustered in the southeastern sector in early December 2021. In Phase 3, a second and oblique alignment and surface fracture was identified. Our findings that crater and eruption changes coincide together with an increase in seismic activity at depth point to a deep driver leading to crater and morphology changes at the surface. These also suggest that crater distributions might allow for improved monitoring of changes occurring at depth, and vice versa, such that strong seismicity changes at depth may herald the migration and new formation of craters, which have major implications for the assessment of tephra and lava flow hazards on volcanoes. Full article

(This article belongs to the Special Issue Assessment and Prediction of Volcano Hazard Using Remote Sensing)

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10 pages, 2213 KiB

Open AccessArticle

LiDAR and UAV SfM-MVS of Merapi Volcanic Dome and Crater Rim Change from 2012 to 2014

by Christopher Gomez, Muhammad Anggri Setiawan, Noviyanti Listyaningrum, Sandy Budi Wibowo, Danang Sri Hadmoko, Wiwit Suryanto, Herlan Darmawan, Balazs Bradak, Rikuto Daikai, Sunardi Sunardi, Yudo Prasetyo, Annisa Joviani Astari, Lukman Lukman, Idea Wening Nurani, Moh. Dede, Indranova Suhendro, Franck Lavigne and Mukhamad Ngainul Malawani

Remote Sens. 2022, 14(20), 5193; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205193 - 17 Oct 2022

Cited by 10 | Viewed by 2209

Abstract

Spatial approaches, based on the deformation measurement of volcanic domes and crater rims, is key in evaluating the activity of a volcano, such as Merapi Volcano, where associated disaster risk regularly takes lives. Within this framework, this study aims to detect localized topographic change in the summit area that has occurred concomitantly with the dome growth and explosion reported. The methodology was focused on two sets of data, one LiDAR-based dataset from 2012 and one UAV dataset from 2014. The results show that during the period 2012–2014, the crater walls were 100–120 m above the crater floor at its maximum (from the north to the east–southeast sector), while the west and north sectors present a topographic range of 40–80 m. During the period 2012–2014, the evolution of the crater rim around the dome was generally stable (no large collapse). The opening of a new vent on the surface of the dome has displaced an equivalent volume of 2.04 × 10⁴ m³, corresponding to a maximum −9 m (+/−0.9 m) vertically. The exploded material has partly fallen within the crater, increasing the accumulated loose material while leaving “hollows” where the vents are located, although the potential presence of debris inside these vents made it difficult to determine the exact size of these openings. Despite a measure of the error from the two DEMs, adding a previously published dataset shows further discrepancies, suggesting that there is also a technical need to develop point-cloud technologies for active volcanic craters. Full article

(This article belongs to the Special Issue Assessment and Prediction of Volcano Hazard Using Remote Sensing)

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23 pages, 11695 KiB

Open AccessArticle

Numerical Modeling of the Ash Cloud Movement from the Catastrophic Eruption of the Sheveluch Volcano in November 1964

by Olga Girina, Sergey Malkovsky, Aleksei Sorokin, Evgeny Loupian and Sergey Korolev

Remote Sens. 2022, 14(14), 3449; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143449 - 18 Jul 2022

Cited by 1 | Viewed by 1981

Abstract

This paper reconstructs, for the first time, the motion dynamics of an eruptive cloud formed during the catastrophic eruption of the Sheveluch volcano in November 1964 (Volcanic Explosivity Index 4+). This became possible due to the public availability of atmospheric reanalysis data from the ERA-40 archive of the European Center for Medium-Range Weather Forecasts (ECMWF) and the development of numerical modeling of volcanic ash cloud propagation. The simulation of the eruptive cloud motion process, which was carried out using the FALL3D and PUFF models, made it possible to clarify the sequence of events of this eruption (destruction of extrusive domes in the crater and the formation of an eruptive column and pyroclastic flows), which lasted only 1 h 12 min. During the eruption, the ash cloud consisted of two parts: the main eruptive cloud that rose up to 15,000 m above sea level (a.s.l.), and the co-ignimbrite cloud that formed above the moving pyroclastic flows. The ashfall in Ust-Kamchatsk (Kamchatka) first occurred out of the eruptive cloud moving at a higher speed, then out of the co-ignimbrite cloud. In Nikolskoye (Bering Island, Commander Islands), ash fell only out of the co-ignimbrite cloud. Under the turbulent diffusion, the forefront of the main eruptive cloud rose slowly in the atmosphere and reached 16,500 m a.s.l. by 04:07 UTC on November 12. Three days after the eruption began, the eruptive cloud stretched for 3000 km over the territories of the countries of Russia, Canada, the USA, Mexico, and over both the Bering Sea and the Pacific Ocean. It is assumed that the well-known long-term decrease in the solar radiation intensity in the northern latitudes from 1963–1966, which was established according to the world remote sensing data, was associated with the spread of aerosol clouds formed not only by the Agung volcano, but those formed during the 1964 Sheveluch volcano catastrophic eruption. Full article

(This article belongs to the Special Issue Assessment and Prediction of Volcano Hazard Using Remote Sensing)

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21 pages, 40634 KiB

Open AccessArticle

The Impact of Dynamic Emissivity–Temperature Trends on Spaceborne Data: Applications to the 2001 Mount Etna Eruption

by Nikola Rogic, Giuseppe Bilotta, Gaetana Ganci, James O. Thompson, Annalisa Cappello, Hazel Rymer, Michael S. Ramsey and Fabrizio Ferrucci

Remote Sens. 2022, 14(7), 1641; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071641 - 29 Mar 2022

Cited by 7 | Viewed by 1852

Abstract

Spaceborne detection and measurements of high-temperature thermal anomalies enable monitoring and forecasts of lava flow propagation. The accuracy of such thermal estimates relies on the knowledge of input parameters, such as emissivity, which notably affects computation of temperature, radiant heat flux, and subsequent analyses (e.g., effusion rate and lava flow distance to run) that rely on the accuracy of observations. To address the deficit of field and laboratory-based emissivity data for inverse and forward modelling, we measured the emissivity of ‘a’a lava samples from the 2001 Mt. Etna eruption, over the wide range of temperatures (773 to 1373 K) and wavelengths (2.17 to 21.0 µm). The results show that emissivity is not only wavelength dependent, but it also increases non-linearly with cooling, revealing considerably lower values than those typically assumed for basalts. This new evidence showed the largest and smallest increase in average emissivity during cooling in the MIR and TIR regions (~30% and ~8% respectively), whereas the shorter wavelengths of the SWIR region showed a moderate increase (~15%). These results applied to spaceborne data confirm that the variable emissivity-derived radiant heat flux is greater than the constant emissivity assumption. For the differences between the radiant heat flux in the case of variable and constant emissivity, we found the median value is 0.06, whereas the 25th and the 75th percentiles are 0.014 and 0.161, respectively. This new evidence has significant impacts on the modelling of lava flow simulations, causing a dissimilarity between the two emissivity approaches of ~16% in the final area and ~7% in the maximum thickness. The multicomponent emissivity input provides means for ‘best practice’ scenario when accurate data required. The novel approach developed here can be used to test an improved version of existing multi-platform, multi-payload volcano monitoring systems. Full article

(This article belongs to the Special Issue Assessment and Prediction of Volcano Hazard Using Remote Sensing)

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16 pages, 6131 KiB

Open AccessArticle

Effusion Rates on Mt. Etna and Their Influence on Lava Flow Hazard Assessment

by Francesco Zuccarello, Giuseppe Bilotta, Annalisa Cappello and Gaetana Ganci

Remote Sens. 2022, 14(6), 1366; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061366 - 11 Mar 2022

Cited by 8 | Viewed by 2568

Abstract

The rate at which lava is discharged plays a key role in controlling the distance covered by lava flows from eruptive vents. We investigate the available time-averaged discharge rates (TADRs) estimated for recent flank eruptions at Mt. Etna volcano (Italy), in order to define a possible generalized effusion rate trend which is consistent with observed real data. Our analysis indicates a rapid waxing phase in which effusion rate peaks occur for between 0.5 and 29% of the total eruption time, followed by a progressive decrease in the waning phase. Three generalized curves are built by calculating the 25th, 50th and 75th percentiles values associated with the occurrence of effusion peaks, and with the slope variations of descending curves in the waning phase. The obtained curves are used as an input for the GPUFLOW model in order to perform numerical simulations of the lava flows paths on inclined planes, and are compared with those generated by using effusion rate curves with a bell-shaped time-distribution. Our tests show how these characteristic curves could impact single-vent scenarios, as well as short- and long-term hazard maps, with maximum variations of up to 40% for a specific category of eruptive events. Full article

(This article belongs to the Special Issue Assessment and Prediction of Volcano Hazard Using Remote Sensing)

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27 pages, 8768 KiB

Open AccessArticle

The 2019 Eruptive Activity at Stromboli Volcano: A Multidisciplinary Approach to Reveal Hidden Features of the “Unexpected” 3 July Paroxysm

by Mario Mattia, Bellina Di Lieto, Gaetana Ganci, Valentina Bruno, Pierdomenico Romano, Francesco Ciancitto, Prospero De Martino, Salvatore Gambino, Marco Aloisi, Mariangela Sciotto, Roberto Scarpa and Carmelo Ferlito

Remote Sens. 2021, 13(20), 4064; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204064 - 11 Oct 2021

Cited by 7 | Viewed by 4111

Abstract

In July and August 2019, Stromboli volcano underwent two dangerous paroxysms previously considered “unexpected” because of the absence of significant changes in usually monitored parameters. We applied a multidisciplinary approach to search for signals able to indicate the possibility of larger explosive activity and to devise a model to explain the observed variations. We analysed geodetic data, satellite thermal data, images from remote cameras and seismic data in a timespan crossing the eruptive period of 2019 to identify precursors of the two paroxysms on a medium-term time span (months) and to perform an in-depth analysis of the signals recorded on a short time scale (hours, minutes) before the paroxysm. We developed a model that explains the observations. We call the model “push and go” where the uppermost feeding system of Stromboli is made up of a lower section occupied by a low viscosity, low density magma that is largely composed of gases and a shallower section occupied by the accumulated melt. We hypothesize that the paroxysms are triggered when an overpressure in the lower section is built up; the explosion will occur at the very moment such overpressure overcomes the confining pressure of the highly viscous magma above it. Full article

(This article belongs to the Special Issue Assessment and Prediction of Volcano Hazard Using Remote Sensing)

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