Special Issue "Quantitative Volcanic Hazard Assessment and Uncertainty Analysis in Satellite Remote Sensing and Modeling"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editors

Dr. Ciro Del Negro
E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Catania, Osservatorio Etneo, Piazza Roma 2, 95125 Catania, Italy
Interests: physical volcanology; volcanic hazard modeling; satellite remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Michael S. Ramsey
E-Mail Website
Guest Editor
Department of Geology and Environmental Science, University of Pittsburgh, 4107 O'Hara Street, Pittsburgh, PA 15260, USA
Interests: satellite remote sensing; physical and thermodynamic properties of volcanic products
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alexis Hérault
E-Mail Website
Guest Editor
Conservatoire des Arts et Métiers, Laboratoire M2N, 2 rue Conté, 75003 Paris, France
Interests: volcanic hazard modeling; fluid dynamics; uncertainty analysis
Dr. Gaetana Ganci
E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Catania, Osservatorio Etneo, Piazza Roma 2, 95125 Catania, Italy
Interests: satellite remote sensing; data assimilation; volcanic hazard modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Volcanic eruptions can be both effusive, through the outpouring of lava onto the ground, and explosive, through the dispersion of ash in the atmosphere. Each type of eruptive process can produce its associated hazards, from lava flows that can impact local populations to dispersing ash clouds that can lead to aviation impacts. To deal effectively with these crises, a strategy based on the integration of field data, satellite observations and physical models is emerging to monitor volcanic hazards in near real-time. By monitoring, we mean here both following the manifestations of the eruption once it has started, as well as forecasting the areas potentially threatened by volcanic products in an eruptive scenario. The need for integrated and efficient monitoring systems, operating on a global scale, and including tools for producing different scenarios as eruptive conditions change, is a primary challenge for volcanic hazard modeling. Understanding and quantifying uncertainties surrounding the modeling inputs, processing and outputs is thus central to make the modeling of volcanic hazards effective. Characterizing uncertainties will allow more confidence in the interpretation of final model simulations and the application of model results for improved decision support systems.

 

This Special Issue covers original research and studies related to the above-mentioned topics, including but not limited to:

(i) describing field and remote sensing data provisions and their sources of uncertainty;

(ii) evaluating model robustness through validation against real case studies;

(iii) model comparison between numerical simulations, analytical solutions and laboratory experiments;

(iv) quantification of uncertainty propagation through both forward (sensitivity analyses) and inverse (optimization/calibration) modelling in all components of volcanic hazard modelling.

Dr. Ciro Del Negro
Prof. Michael S. Ramsey
Prof. Alexis Hérault
Dr. Gaetana Ganci
Guest Editor

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 papers will be 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 2400 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

  • volcanic eruptions
  • satellite remote sensing of volcanoes
  • volcanic hazard modeling
  • experimental petrology
  • fluid dynamics
  • data assimilation
  • uncertainty analysis

Published Papers (10 papers)

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Research

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Article
A Novel Approach to Estimating Time-Averaged Volcanic SO2 Fluxes from Infrared Satellite Measurements
Remote Sens. 2021, 13(5), 966; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050966 - 04 Mar 2021
Cited by 1 | Viewed by 431
Abstract
Long-term continuous time series of SO2 emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged SO2 plume from which to [...] Read more.
Long-term continuous time series of SO2 emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged SO2 plume from which to estimate the SO2 source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional SO2 datasets from ultraviolet (UV) sensors lack altitude information, which must be assumed, to correctly calibrate the SO2 data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded SO2 data. We then fit a solution to the general time-averaged dispersion of SO2 from a point source. We demonstrate these techniques using SO2 data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 satellites. This SO2 data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR SO2 data can fill an important data gap in the UV-based record: estimating SO2 emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used. Full article
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Article
Pyroclastic Density Current Hazard Assessment and Modeling Uncertainties for Fuego Volcano, Guatemala
Remote Sens. 2020, 12(17), 2790; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172790 - 27 Aug 2020
Cited by 2 | Viewed by 1196
Abstract
On 3 June 2018, Fuego volcano experienced a VEI = 3 eruption, which produced a pyroclastic density current (PDC) that devastated the La Réunion resort and the community of Los Lotes, resulting in over 100 deaths. To evaluate the potential hazard to the [...] Read more.
On 3 June 2018, Fuego volcano experienced a VEI = 3 eruption, which produced a pyroclastic density current (PDC) that devastated the La Réunion resort and the community of Los Lotes, resulting in over 100 deaths. To evaluate the potential hazard to the population centers surrounding Fuego associated with future PDC emplacement, we used an integrated remote sensing and flow modeling-based approach. The predominate PDC travel direction over the past 15 years was investigated using thermal infrared (TIR) data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument validated with ground reports from the National Institute of Seismology, Volcanology, Meteorology, and Hydrology (INSIVUMEH), the government agency responsible for monitoring. Two different ASTER-derived digital elevation model (DEM) products with varying levels of noise were also used to assess the uncertainty in the VolcFlow model results. Our findings indicate that the recent historical PDC travel direction is dominantly toward the south and southwest. Population centers in this region of Fuego that are within ~2 km of one of the volcano’s radial barrancas are at the highest risk during future large eruptions that produce PDCs. The ASTER global DEM (GDEM) product has the least random noise and where used with the VolcFlow model, had a significant improvement on its accuracy. Results produced longer flow runout distances and therefore better conveys a more accurate perception of risk. Different PDC volumes were then modeled using the GDEM and VolcFlow to determine potential inundation areas in relation to local communities. Full article
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Article
Recognizing Eruptions of Mount Etna through Machine Learning Using Multiperspective Infrared Images
Remote Sens. 2020, 12(6), 970; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12060970 - 17 Mar 2020
Cited by 5 | Viewed by 1066
Abstract
Detecting, locating and characterizing volcanic eruptions at an early stage provides the best means to plan and mitigate against potential hazards. Here, we present an automatic system which is able to recognize and classify the main types of eruptive activity occurring at Mount [...] Read more.
Detecting, locating and characterizing volcanic eruptions at an early stage provides the best means to plan and mitigate against potential hazards. Here, we present an automatic system which is able to recognize and classify the main types of eruptive activity occurring at Mount Etna by exploiting infrared images acquired using thermal cameras installed around the volcano. The system employs a machine learning approach based on a Decision Tree tool and a Bag of Words-based classifier. The Decision Tree provides information on the visibility level of the monitored area, while the Bag of Words-based classifier detects the onset of eruptive activity and recognizes the eruption type as either explosion and/or lava flow or plume degassing/ash. Applied in real-time to each image of each of the thermal cameras placed around Etna, the proposed system provides two outputs, namely, visibility level and recognized eruptive activity status. By merging these outcomes, the monitored phenomena can be fully described from different perspectives to acquire more in-depth information in real time and in an automatic way. Full article
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Article
The VEI 2 Christmas 2018 Etna Eruption: A Small But Intense Eruptive Event or the Starting Phase of a Larger One?
Remote Sens. 2020, 12(6), 905; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12060905 - 12 Mar 2020
Cited by 18 | Viewed by 1307
Abstract
The Etna flank eruption that started on 24 December 2018 lasted a few days and involved the opening of an eruptive fissure, accompanied by a seismic swarm and shallow earthquakes, significant SO2 flux release, and by large and widespread ground deformation, especially [...] Read more.
The Etna flank eruption that started on 24 December 2018 lasted a few days and involved the opening of an eruptive fissure, accompanied by a seismic swarm and shallow earthquakes, significant SO2 flux release, and by large and widespread ground deformation, especially on the eastern flank of the volcano. Lava fountains and ash plumes from the uppermost eruptive fissure accompanied the opening stage, causing disruption to Catania International Airport, and were followed by a quiet lava effusion within the barren Valle del Bove depression until 27 December. This was the first flank eruption to occur at Etna in the last decade, during which eruptive activity was confined to the summit craters and resulted in lava fountains and lava flow output from the crater rims. In this paper, we used ground and satellite remote sensing techniques to describe the sequence of events, quantify the erupted volumes of lava, gas, and tephra, and assess volcanic hazards. Full article
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Article
Uncertainty Analysis of Remotely-Acquired Thermal Infrared Data to Extract the Thermal Properties of Active Lava Surfaces
Remote Sens. 2020, 12(1), 193; https://doi.org/10.3390/rs12010193 - 05 Jan 2020
Cited by 2 | Viewed by 1188
Abstract
Using thermal infrared (TIR) data from multiple instruments and platforms for analysis of an entire active volcanic system is becoming more common with the increasing availability of new data. However, the accuracy and uncertainty associated with these combined datasets are poorly constrained over [...] Read more.
Using thermal infrared (TIR) data from multiple instruments and platforms for analysis of an entire active volcanic system is becoming more common with the increasing availability of new data. However, the accuracy and uncertainty associated with these combined datasets are poorly constrained over the full range of eruption temperatures and possible volcanic products. Here, four TIR datasets acquired over active lava surfaces are compared to quantify the uncertainty, accuracy, and variability in derived surface radiance, emissivity, and kinetic temperature. These data were acquired at Kīlauea volcano in Hawai’i, USA, in January/February 2017 and 2018. The analysis reveals that spatial resolution strongly limits the accuracy of the derived surface thermal properties, resulting in values that are significantly below the expected values for molten basaltic lava at its liquidus temperature. The surface radiance is ~2400% underestimated in the orbital data compared to only ~200% in ground-based data. As a result, the surface emissivity is overestimated and the kinetic temperature is underestimated by at least 30% and 200% in the airborne and orbital datasets, respectively. A thermal mixed pixel separation analysis is conducted to extract only the molten fraction within each pixel in an attempt to mitigate this complicating factor. This improved the orbital and airborne surface radiance values to within 15% of the expected values and the derived emissivity and kinetic temperature within 8% and 12%, respectively. It is, therefore, possible to use moderate spatial resolution TIR data to derive accurate and reliable emissivity and kinetic temperatures of a molten lava surface that are comparable to the higher resolution data from airborne and ground-based instruments. This approach, resulting in more accurate kinetic temperature and emissivity of the active surfaces, can improve estimates of flow hazards by greatly improving lava flow propagation models that rely on these data. Full article
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Article
Spaceborne EO and a Combination of Inverse and Forward Modelling for Monitoring Lava Flow Advance
Remote Sens. 2019, 11(24), 3032; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11243032 - 16 Dec 2019
Cited by 3 | Viewed by 1130
Abstract
We aim here to improve the understanding of the relationship between emissivity of the lava and temperature by carrying out a multi-stage experiment for the 2017 Mt Etna (Italy) eruption. We combine laboratory, spaceborne, and numerical modelling data, to quantify the emissivity–temperature relationship. [...] Read more.
We aim here to improve the understanding of the relationship between emissivity of the lava and temperature by carrying out a multi-stage experiment for the 2017 Mt Etna (Italy) eruption. We combine laboratory, spaceborne, and numerical modelling data, to quantify the emissivity–temperature relationship. Our laboratory-based Fourier-transform infrared (FTIR) results indicate that emissivity and temperature are inversely correlated, which supports the argument that emissivity of molten material is significantly lower than that of the same material in its solid state. Our forward-modelling tests using MAGFLOW Cellular Automata suggest that a 35% emissivity variation (0.95 to 0.60) can produce up to 46% overestimation (for constant emissivity 0.60) in simulated/forecasted lava flow lengths (compared to actual observed). In comparison, our simulation using a ‘two-component’ emissivity approach (i.e., different emissivity values for melt and cooled lava) and constant emissivity 0.95 compares well (≤10% overestimation) with the actual 2017 lava flow lengths. We evaluated the influence of variable emissivity on lava surface temperatures using spaceborne data by performing several parametrically controlled assessments, using both constant (‘uniform’) and a ‘two-component’ emissivity approach. Computed total radiant fluxes, using the same spaceborne scene (Landsat 8 Operational Land Imager (OLI)), differ ≤15% depending on emissivity endmembers (i.e., 0.95 and 0.60). These results further suggest that computed radiant flux using high-spatial resolution data is bordering at lower boundary (range) values of the moderate-to-high temporal resolution spaceborne data (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Spinning Enhanced Visible and Infrared Imager (SEVIRI)), acquired for the same target area (and the same time interval). These findings may have considerable impact on civil protection decisions made during volcanic crisis involving lava flows as they approach protected or populated areas. Nonetheless, the laboratory work, reported here, should be extended to include higher volcanic eruptive temperatures (up to 1350 K). Full article
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Article
A Multi-Channel Algorithm for Mapping Volcanic Thermal Anomalies by Means of Sentinel-2 MSI and Landsat-8 OLI Data
Remote Sens. 2019, 11(23), 2876; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11232876 - 03 Dec 2019
Cited by 17 | Viewed by 2106
Abstract
The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively onboard Sentinel-2A/2B and Landsat 8 satellites, thanks to their features especially in terms of spatial/spectral resolution, represents two important instruments for investigating thermal volcanic activity from space. In this study, we used [...] Read more.
The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively onboard Sentinel-2A/2B and Landsat 8 satellites, thanks to their features especially in terms of spatial/spectral resolution, represents two important instruments for investigating thermal volcanic activity from space. In this study, we used data from those sensors to test an original multichannel algorithm, which aims at mapping volcanic thermal anomalies at a global scale. The algorithm, named Normalized Hotspot Indices (NHI), combines two normalized indices, analyzing near infrared (NIR) and short wave infrared (SWIR) radiances, to identify hotspot pixels in daylight conditions. Results, achieved studying a number of active volcanoes located in different geographic areas and characterized by a different eruptive behavior, demonstrated the NHI capacity in mapping both subtle and more intense volcanic thermal anomalies despite some limitations (e.g., missed detections because of clouds/volcanic plumes). In addition, the study shows that the performance of NHI might be further increased using some additional spectral/spatial tests, in view of a possible usage of this algorithm within a known multi-temporal scheme of satellite data analysis. The low processing times and the straight forth exportability to data from other sensors make NHI, which is sensitive even to other high temperature sources, suited for mapping hot volcanic targets integrating information provided by current and well-established satellite-based volcanoes monitoring systems. Full article
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Article
On the Applicability of Laboratory Thermal Infrared Emissivity Spectra for Deconvolving Satellite Data of Opaque Volcanic Ash Plumes
Remote Sens. 2019, 11(19), 2318; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11192318 - 05 Oct 2019
Cited by 2 | Viewed by 977
Abstract
The ASTER Volcanic Ash Library (AVAL) is presented, developed using quantitative laboratory thermal infrared (TIR) emission spectroscopic methods, spanning the 2000–400 cm−1 (5–25 μm wavelength) range, including the Earth’s TIR atmospheric window (8–12 μm). Each spectral suite is unique owing to the [...] Read more.
The ASTER Volcanic Ash Library (AVAL) is presented, developed using quantitative laboratory thermal infrared (TIR) emission spectroscopic methods, spanning the 2000–400 cm−1 (5–25 μm wavelength) range, including the Earth’s TIR atmospheric window (8–12 μm). Each spectral suite is unique owing to the chemical composition and proportion of glass to crystals per sample and is divided into six size fractions. AVAL, used with an appropriate spectral mixture model applied to orbital multispectral TIR data, provides a unique ability to study active volcanic ash plumes. We present the first example of this application to an ash plume produced by the Sakurajima Volcano in Japan. The emissivity variations measured in ash plumes using an ever-expanding ash spectral library will provide future quantitative inputs for both atmospheric models, where the ash composition is unknown or estimated, as well as compositional probes into ongoing eruptions. Full article
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Article
Mapping Recent Lava Flows at Mount Etna Using Multispectral Sentinel-2 Images and Machine Learning Techniques
Remote Sens. 2019, 11(16), 1916; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161916 - 16 Aug 2019
Cited by 14 | Viewed by 2333
Abstract
Accurate mapping of recent lava flows can provide significant insight into the development of flow fields that may aid in predicting future flow behavior. The task is challenging, due to both intrinsic properties of the phenomenon (e.g., lava flow resurfacing processes) and technical [...] Read more.
Accurate mapping of recent lava flows can provide significant insight into the development of flow fields that may aid in predicting future flow behavior. The task is challenging, due to both intrinsic properties of the phenomenon (e.g., lava flow resurfacing processes) and technical issues (e.g., the difficulty to survey a spatially extended lava flow with either aerial or ground instruments while avoiding hazardous locations). The huge amount of moderate to high resolution multispectral satellite data currently provides new opportunities for monitoring of extreme thermal events, such as eruptive phenomena. While retrieving boundaries of an active lava flow is relatively straightforward, problems arise when discriminating a recently cooled lava flow from older lava flow fields. Here, we present a new supervised classifier based on machine learning techniques to discriminate recent lava imaged in the MultiSpectral Imager (MSI) onboard Sentinel-2 satellite. Automated classification evaluates each pixel in a scene and then groups the pixels with similar values (e.g., digital number, reflectance, radiance) into a specified number of classes. Bands at the spatial resolution of 10 m (bands 2, 3, 4, 8) are used as input to the classifier. The training phase is performed on a small number of pixels manually labeled as covered by fresh lava, while the testing characterizes the entire lava flow field. Compared with ground-based measurements and actual lava flows of Mount Etna emplaced in 2017 and 2018, our automatic procedure provides excellent results in terms of accuracy, precision, and sensitivity. Full article
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Review

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Review
Raman Spectroscopy from Laboratory and Proximal to Remote Sensing: A Tool for the Volcanological Sciences
Remote Sens. 2020, 12(5), 805; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050805 - 02 Mar 2020
Cited by 5 | Viewed by 1503
Abstract
Here we explore and review some of the latest ideas and applications of Raman spectroscopy to the volcanological sciences. Firstly, we provide a brief overview of how Raman spectral analysis works and how spectra from silicate glasses are interpreted. We then look at [...] Read more.
Here we explore and review some of the latest ideas and applications of Raman spectroscopy to the volcanological sciences. Firstly, we provide a brief overview of how Raman spectral analysis works and how spectra from silicate glasses are interpreted. We then look at specific applications of Raman spectral analysis to the volcanological sciences based on measurements on and studies of natural materials in the laboratory. We conclude by examining the potential for Raman spectral analysis to be used as a field based aid to volcano monitoring via in situ studies of proximal deposits and; perhaps; in remote sensing campaigns Full article
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