remotesensing-logo

Journal Browser

Journal Browser

Satellite Monitoring of Volcanoes in Near-Real Time

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 1462

Special Issue Editors


E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, 95125 Catania, CT, Italy
Interests: thermal remote sensing; data fusion; lava flow modelling; volcanic hazards and risk
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Physics, University of Calabria, 87036 Rende, CS, Italy
Interests: geophysics; remote sensing; environmental monitoring; emergency management

E-Mail Website
Guest Editor
School of Ocean and Earth Science and Technology, Hawai’i Institute of Geophysics and Planetology, Honolulu, HI 96822, USA
Interests: electrooptical remote sensing; volcanology; physical-chemical parameters of lavas

E-Mail Website
Guest Editor
Hawai’i Institute of Geophysics and Planetology, University of Hawai’i at Manoa, Honolulu, HI 96822, USA
Interests: remote sensing; hyperspectral imaging; CubeSats; volcanology

Special Issue Information

Dear Colleagues,

Modern Earth Observation satellites carry instruments capable of early detection and tracking of changes in the nature and intensity of volcanic activity, anywhere on Earth, at unprecedented temporal frequencies. Volcanic processes and hazards that can be quantified from orbit include the impact of lavas, pyroclastic flows, and volcanic ash and gases on the terrestrial, atmospheric, and marine environments.

Depending on platform orbits, payload characteristics, and the number of spacecraft in increasingly common satellite constellations, revisit rates for any of about 2,000 sub-aerial or shallow submarine Holocene volcanoes (those with at least one documented eruption during the last 12,000 years) can range from daily to less-than-hourly, with spatial sampling ranging from tens of kilometers to sub-meters, where  the volcanic process under investigation dictates which spatial, spectral, and temporal resolution is most appropriate.

Current satellite sensors allow the detection, measurement, and monitoring of many physical and chemical parameters of eruptions, including the concentrations and mass fluxes of erupted products and the dynamics of their spatial distribution changes, yielding better insight into how eruption intensity waxes and wanes. Rapidly converting Level 1 satellite data products into useful volcanological information calls for the development of unsupervised processing and of satellite data, so they can be ingested into predictive models to provide planners, emergency managers, and first responders with a continuously updated, quantitative picture of the volcano of interest—a picture which may or may not be complemented by a ground-based monitoring system.

This Special Issue will present papers that describe innovative satellite remote sensing datasets and the associated methods and techniques being developed for the study, investigation, and monitoring of volcanic phenomena in real and near-real time. It will discuss the new generation of passive optical and active systems expanding volcanologists’ abilities to detect, map, characterize, model, understand and interpret pre-eruptive and eruptive volcanic processes and products. We are seeking original articles on new applications and case studies based on innovative satellite observations, models, solutions, and services. Potential topics include (but are not limited to):

  • Synergistic use of data acquired by sensors carried on multiple spacecraft.
  • Radiometry of low- and high-temperature volcanic features.
  • Improvements in eruptive columns and volcanic ash detection and tracking.
  • Monitoring of volcanic sulphur and carbon gas emissions.
  • Satellite monitoring of submarine volcanoes.
  • Deformation monitoring and major changes in volcanic topography following, accompanying and/or preceding volcanic unrest.
  • AI/ML-based cognitive interpretation of multi-parameter volcano dynamics.
  • Automating the process by which remote sensing data are inverted to physical parameters and ingested into predictory models of the unrest.
  • Ancillary data in support to automated monitoring processes.

Dr. Annalisa Cappello
Prof. Fabrizio Ferrucci
Dr. Nikola Rogic
Dr. Robert Wright
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

  • spaceborne EO
  • volcanic unrest
  • volcanic eruption
  • volcanic hazards
  • volcanic risk
  • emergency planning
  • emergency response
  • artificial intelligence
  • machine learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

22 pages, 17083 KiB  
Article
Volcanic Activity Classification Through Semi-Supervised Learning Applied to Satellite Radiance Time Series
by Francesco Spina, Giuseppe Bilotta, Annalisa Cappello, Marco Spina, Francesco Zuccarello and Gaetana Ganci
Remote Sens. 2025, 17(10), 1679; https://doi.org/10.3390/rs17101679 - 10 May 2025
Viewed by 372
Abstract
Satellite imagery provides a rich source of information that serves as a comprehensive and synoptic tool for the continuous monitoring of active volcanoes, including those in remote and inaccessible areas. The huge influx of such data requires the development of automated systems for [...] Read more.
Satellite imagery provides a rich source of information that serves as a comprehensive and synoptic tool for the continuous monitoring of active volcanoes, including those in remote and inaccessible areas. The huge influx of such data requires the development of automated systems for efficient processing and interpretation. Early warning systems, designed to process satellite imagery to identify signs of impending eruptions and monitor eruptive activity in near real-time, are essential for hazard assessment and risk mitigation. Here, we propose a machine learning approach for the automatic classification of pixels in SEVIRI images to detect and characterize the eruptive activity of a volcano. In particular, we exploit a semi-supervised GAN (SGAN) model that retrieves the presence of thermal anomalies, volcanic ash plumes, and meteorological clouds in each SEVIRI pixel, allowing time series plots to be obtained showing the evolution of volcanic activity. The SGAN model was trained and tested using the huge amount of data available on Mount Etna (Italy). Then, it was applied to other volcanoes, specifically, Stromboli (Italy), Tajogaite (Spain), and Nyiragongo (Democratic Republic of the Congo), to assess the model’s ability to generalize. The validation of the model was performed through a visual comparison between the classification results and the corresponding SEVIRI images. Moreover, we evaluate the model performance by calculating three different metrics, namely the precision (correctness of positive predictions), the recall (ability to find all the positive instances), and the F1-score (general model’s accuracy), finding an average accuracy of 0.9. Our approach can be extended to other geostationary satellite data and applied worldwide to characterize volcanic activity, allowing the monitoring of even remote volcanoes that are difficult to reach from the ground. Full article
(This article belongs to the Special Issue Satellite Monitoring of Volcanoes in Near-Real Time)
Show Figures

Graphical abstract

28 pages, 8684 KiB  
Article
Rapid Response to Effusive Eruptions Using Satellite Infrared Data: The March 2024 Eruption of Fernandina (Galápagos)
by Diego Coppola, Simone Aveni, Adele Campus, Marco Laiolo, Francesco Massimetti and Benjamin Bernard
Remote Sens. 2025, 17(7), 1191; https://doi.org/10.3390/rs17071191 - 27 Mar 2025
Viewed by 432
Abstract
On 3 March 2024, a new effusive eruption began from a sub-circular fissure on the southeast upper flank of the Fernandina volcano (Galápagos archipelago, Ecuador). Although the eruption posed no threat to people, as the island is uninhabited, it provided an opportunity to [...] Read more.
On 3 March 2024, a new effusive eruption began from a sub-circular fissure on the southeast upper flank of the Fernandina volcano (Galápagos archipelago, Ecuador). Although the eruption posed no threat to people, as the island is uninhabited, it provided an opportunity to test a rapid response system for effusive eruptions, based on satellite infrared (IR) data. In this work, we illustrate how the analysis of data from multiple IR sensors allowed us to monitor the eruption in near real-time (NRT), providing recurrent updates on key parameters, such as (i) lava discharge rate and trend, (ii) erupted lava volume, (iii) lava field area, (iv) active flow front position (v) flow velocity, (vi) location of active vents and breakouts, and (vii) emplacement style. Overall, the eruption lasted 68 days, during which 58.5 ± 29.2 Mm3 of lava was erupted and an area of 14.9 ± 0.5 km2 was invaded. The eruption was characterized by a peak effusion rate of 206 ± 103 m3/s, an initial velocity of ~2.3 km/h, and by an almost exponential decline in the effusion rate, accompanied by a transition from channel- to tube-fed emplacement style. The advance of the lava flow was characterized by three lengthening phases that allowed the front to reach the coast (~12.5 km from the vent) after 36 days (at an average velocity of ~0.015 km/h). The results demonstrate the efficiency of satellite thermal data in responding to effusive eruptions and maintaining situational awareness at remote volcanoes where ground-based data are limited or completely unavailable. The requirements, limitations, and future perspectives for applying this rapid response protocol on a global scale are finally discussed. Full article
(This article belongs to the Special Issue Satellite Monitoring of Volcanoes in Near-Real Time)
Show Figures

Figure 1

Back to TopTop