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

Abstract

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.

Synergic Use of Multi-Sensor Satellite Data for Volcanic Hazards Monitoring: The Fogo (Cape Verde) 2014–2015 Effusive Eruption

“…For these reasons, SAR-based change detection (SAR-CD) was developed over many years to provide useful and reliable information on land surface changes that occur across different temporal and spatial scales (Bovolo and Bruzzone, 2005). SAR-CD usually finds different domain of application, and in particular for all concerns natural hazard related to floods , volcanoes (Bignami et al, 2013;Valade et al, 2019), earthquakes (Pierdicca et al, 2018), and tsunamis (Chini et al, 2008). SAR-CD algorithms typically generates the difference image and then classifies it, which consists of a binary classification problem, aiming at separating the change and the no change classes (hereafter CC and NCC) (Ajadi et al, 2016).…”

“…This quantity is mostly influenced by the phase difference between radar returns, a distinctive parameter measured by a coherent sensor such as SAR, and it is particularly related to the spatial arrangement of the scatterers within the pixel and thus to their possible random displacements. Its high sensitivity to surface changes is welldocumented and enables the detection of damages caused by catastrophic events such as volcano eruptions, earthquakes, and floods (e.g., Hoffmann, 2007;Chini et al, 2012;Valade et al, 2019). Compared to the SAR intensity, the coherence sensitivity to surface changes is much higher, because even a target rotation can create a temporal decorrelation, while to detect changes in the intensity, it is necessary that the roughness and the dielectric properties of surface change.…”

“…In this case, the seeds are those pixels depicted as change by the intensity and the rule to stop the growing is the standard deviation of seeds plus an epsilon. The rule to use the change maps from intensity as seed for the region growing on the coherence is motivated by the fact the coherence drop-off occurs also for reasons other than lava flow, e.g., vegetated areas (Valade et al, 2019). Indeed, looking at the coherence map in Figure 1B, the effect of the vegetation is quite evident in many areas (NNE zones of the volcano).…”

Thermal Remote Sensing for Global Volcano Monitoring: Experiences From the MIROVA System

“…The acquisition of satellite images and data for volcanological applications is continuously and rapidly growing (Ramsey and Harris, 2013;Furtney et al, 2018;Pritchard et al, unpublished), so that big data analysis techniques (i.e., artificial intelligence and machine learning) are progressively used for research purposes and for monitoring activity (Piscini and Lombardo, 2014;Anantrasirichai et al, 2018;Valade et al, 2019). In particular, with the advent of the new millenium, and with the development of internet, the dissemination and sharing of satellite data/products can be considered a pillar of open science in volcanology, also thanks to the growing availability of open data by space agencies (Delgado et al, 2019).…”

“…Recent scientific pilot projects, as the European Volcano Observatory Space Services, EVOSS (Tait and Ferrucci, 2013) and the Committee on Earth Observation Satellite (CEOS) Volcano Pilot Project (Delgado et al, 2019), demonstrated the potential of integrating these space-based data for forecasting eruptions (Furtney et al, 2018), stressing the need to develop a volcanic monitoring system to support volcano observatories (Pritchard et al, unpublished). The Monitoring Unrest From Space (MOUNTS) project 1 , although in an embryonic stage, can be considered a first prototype of such integrated system, since it includes near-real time multi-parametric analysis (UV, IR and microwaves) derived from the ESA Sentinel constellation, at several volcanoes (Valade et al, 2019). However, apart from this example, a comprehensive integration of space-based datasets into an operational system for global volcano monitoring is at this time only envisioned, with several distinct groups working on a single RSO (deformation, degassing, ash, thermal;Reath et al, 2019a).…”