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Open AccessArticle

Towards Global Volcano Monitoring Using Multisensor Sentinel Missions and Artificial Intelligence: The MOUNTS Monitoring System

1
Dep. Computer Vision & Remote Sensing, Technische Universität Berlin, 10587 Berlin, Germany
2
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
3
Dipartimento di Scienze della Terra, University of Torino, Via Valperga Caluso 35, 10125 Torino, Italy
4
Dipartimento di Scienze della Terra, University of Firenze, Via La Pira 4, 50121 Firenze, Italy
5
Geography Department, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1528; https://doi.org/10.3390/rs11131528
Received: 30 April 2019 / Revised: 6 June 2019 / Accepted: 18 June 2019 / Published: 27 June 2019
(This article belongs to the Special Issue Remote Sensing of Volcanic Processes and Risk)
Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO2 gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards. View Full-Text
Keywords: volcano monitoring; Sentinel missions; Convolutional Neural Network (CNN); Synthetic Aperture Radar (SAR) imaging; InSAR processing; infrared remote sensing; SO2 gas emission volcano monitoring; Sentinel missions; Convolutional Neural Network (CNN); Synthetic Aperture Radar (SAR) imaging; InSAR processing; infrared remote sensing; SO2 gas emission
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MDPI and ACS Style

Valade, S.; Ley, A.; Massimetti, F.; D’Hondt, O.; Laiolo, M.; Coppola, D.; Loibl, D.; Hellwich, O.; Walter, T.R. Towards Global Volcano Monitoring Using Multisensor Sentinel Missions and Artificial Intelligence: The MOUNTS Monitoring System. Remote Sens. 2019, 11, 1528.

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