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: 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
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
Prof. 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 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 1800 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 (2 papers)

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Research

Open AccessArticle
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://doi.org/10.3390/rs11192318 - 05 Oct 2019
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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Open AccessArticle
Mapping Recent Lava Flows at Mount Etna Using Multispectral Sentinel-2 Images and Machine Learning Techniques
Remote Sens. 2019, 11(16), 1916; https://doi.org/10.3390/rs11161916 - 16 Aug 2019
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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