About 1500 volcanoes are considered active worldwide [1
], with about 50–85 erupting volcanoes each year [2
]. Unfortunately, due to the cost and difficulty to maintain instrumentation in volcanic environments, less than half of the potentially active volcanoes are monitored with ground-based sensors, and even less are considered well-monitored [3
]. Numerous volcanic crises have sadly illustrated that this lack of monitoring capabilities can have dramatic consequences [4
], as it was the case during the recent 2018 eruptions at Fuego (Guatemala) and Anak Krakatau (Indonesia), which were both responsible for over 430 dead and missing persons according to authorities [5
]. Moreover, volcanoes considered dormant (no recent eruption) or extinct (no eruption for >10,000 years) are commonly not instrumentally monitored, but may experience large and unexpected eruptions, as it was the case for Chaiten (Chile) in 2008 which erupted after 8000 years of inactivity [7
]. In this regard, satellite remote sensing can provide crucial observations when ground-based monitoring is limited or lacking, due to remote environments and/or limited resources. In turn, continuous long-term observations (i.e., monitoring) from space is key to identify background levels of activity, that will allow to better recognize signs of unrest when the activity is deviating from these baselines. Provided that precursor signals can be recorded, pre-eruptive monitoring can lead to better eruption early warnings, and syn-eruptive monitoring can provide critical information for hazard mitigation [8
Indeed, eruptions are often (but not always) preceded by precursory signals which may last a few hours to a few years, indicating a state of unrest. These signals include changes in seismicity, ground deformation, gas emissions, and/or thermal anomalies [9
]. Apart from seismicity, all of these can be monitored from space by exploiting various wavelengths across the electromagnetic spectrum: Synthetic Aperture Radar (SAR) is widely used for quantification of surface deformation [12
], infrared (IR) for quantification of heat radiation [14
], and ultraviolet (UV) for quantification of SO2
]. During an eruption on the other hand, these space-borne sensors can provide additional support to track eruptive products. In particular, IR spectroscopy is used to estimate lava flow discharge rates [17
], UV and IR spectroscopy to detect volcanic gas and ash clouds [19
], whereas SAR processing allows for detection of areas covered by lava flows [21
] and pyroclastic deposits [23
Efforts to analyze these multi-sensor datasets on global and multi-decadal scales have provided insights into volcanic eruption dynamics and precursors [25
], and are contributing to the development of strategies for global volcano monitoring. Operational implementations of such multi-sensor approaches into monitoring platforms, aiming at providing near-real-time (NRT) access to gas, thermal, and deformation data from satellites have been initiated by a number of transnational projects. Among the most prominent are the GlobVolcano project (2007–2010, [30
]), the European Volcano Observatory Space Services (EVOSS, 2010–2013, [31
]), and more recently the Disaster Risk Management (DRM) volcano pilot project led by the Committee on Earth Observation Satellites CEOS (2014–2017, [28
]). In parallel, data and tools are shared on research infrastructures such as the European Plate Observing System (EPOS) or the World Organization of Volcano Observatories (WOVO, [32
]) to stimulate communication and cooperation. Nevertheless, operational monitoring systems providing integrated multi-sensor analysis for volcano surveillance on global scale is still lacking.
Today, a growing number of new Earth Observation (EO) satellites is providing freely available imagery, with global coverage at unprecedented spatial and temporal resolutions, which is a game changer for volcano monitoring. In particular, the Copernicus Sentinel missions, launched by the European Space Agency (ESA), operate a range of instruments [33
] which provide the potential for a comprehensive monitoring of volcanic unrest and eruptive dynamics, opening pathways to global, multisensor volcano monitoring. In parallel, due to the sharply-increasing satellite data volume, novel ways of data reduction and analysis by artificial intelligence (AI) are gaining relevance [34
We here present the first operational volcano monitoring platform, incorporating multisensor satellite-based information (Sentinel-1 SAR, Sentinel-2 SWIR, Sentinel-5P TROPOMI), ground-based earthquakes information (Global Earthquake Catalogues GEOFON and USGS), and AI-assisted monitoring. The system currently monitors 17 volcanoes, located in various climatic and geologic settings across the globe: subduction zones (Colima, Popocatépetl, Fuego, Pacaya, Sangay, Sabancaya, Bezymianny, Klyuchevskoy, Udina, Krakatau, Ambrym, Etna), oceanic hotspots (Piton de la Fournaise, Kilauea), and continental rift zones (Nyiragongo, Nyamulagira, Erta Ale). The system is however designed to easily incorporate new targets to monitor, thanks in particular to the global-coverage and free-access of the data. We use freely-available processing toolboxes to perform routine data processing, and allow the user to investigate the multi-parametric results related to a given volcano through an open-access website (www.mounts-project.com
). We demonstrate the utility of such an interdisciplinary approach through a number of recent eruptions, describing a range of volcanic processes which can be tracked, from subsurface magma migration, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and aerosol propagation into the atmosphere. In addition, we show how artificial intelligence can play a key role in monitoring tasks. We show how a pre-trained Convolutional Neural Network (CNN) can be incorporated into the processing pipeline to detect large deformation in InSAR interferograms (e.g., related to dyke intrusions), in an automated, timely, and robust fashion.
We hereafter show how the products derived from S1, S2, and S5P analysis can help monitor various aspects of volcanic activity. The usefulness of each of these parameters varies from one volcano to another, due to both varying volcanic activity (i.e., effusive vs. explosive, open-vent vs. close-vent) and climatic setting (i.e., desert vs. tropical environment). For this reason, we here take various eruptive case examples, where the utility of each of these parameters is best illustrated. Below we successively describe processes related to magma migration towards the surface, effusive and explosive eruptive deposit emplacement, as well as SO2 gas plume propagation in the atmosphere.
4.1. Detection of Surface Deformation (DInSAR, AI)
A number of processes can cause ground deformation at volcanoes: dyke intrusions, reservoir pressurization, caldera subsidence, cooling of eruptive deposits (lava/pyroclastic flows), landslides, etc. These can to some extent be distinguished based on distinctive patterns of ground displacement imaged in the interferograms [75
]. As described previously, MOUNTS focuses on the automatic detection of processes generating large ground deformations in short time intervals (i.e., between two successive S1 acquisitions, usually 6 to 12 days apart), which typically imprint on interferograms as many colored fringes. In particular, dyke intrusions (whereby magma intrudes into a fissure and pushes the surrounding rock aside), are commonly characterized by two lobes with opposite displacements directions, that imprint as a “butterfly” shape. If the dyke reaches the surface, it will result in eruptive fissures/eruptive vents from which lava flows are emplaced.
illustrates the efficiency of this automated deformation detection system, taking the case of Piton de la Fournaise (Réunion Island) as an example. The volcano experienced 5 intrusive episodes in the past year (April 2018–April 2019), all of which reached the surface and resulted in lava flows that lasted between 0.7–47 days (episode 1 and 4 respectively). The system successfully detected the interferograms where ground deformation is recorded (Figure 5
a–c), illustrating its robustness against various artifacts in the geocoded interferogram (related to strong topography gradients, strong atmospheric phase noise, and decorrelation in vegetated areas). Furthermore, thermal anomalies detected by S2 SWIR analysis (i.e., number of hot pixels) are compared with the Volcanic Radiative Power (VRP) provided by the MIROVA system (Figure 5
d) to testify the good correspondence between the active flow area and the heat radiated by the flow surface.
The systematic analysis of both ground deformation (amplitude, pattern, orientation) related to the intrusion of magma in the shallow portions of the edifice, and lava effusion rate once the magma breaches the surface, should help better understand the mechanisms controlling effusive eruptions in closed-vent volcano systems such as Piton de la Fournaise.
4.2. Detection of Eruptive Deposits (SWIR, DInSAR, SAR)
Volcanic eruptions can generate various types of eruptive deposits, including lava flows, pyroclastic flows, mud flows, pyroclastic deposits, ash fall, etc. The best-suited parameter to detect these from space will depend on both the volcanic product type and the surface on which it is emplaced (i.e., arid surface, vegetated surface). We here describe two extreme eruptive case scenarios: the Erta Ale eruption (Ethiopia, ongoing since January 2017), characterized by an effusive activity where lava flows are emplaced in a desert environment, and the Fuego eruption (Guatemala, June 2018), characterized by a violent explosive activity which generated pyroclastic flows (i.e., avalanche of hot blocks and ash which flowed down the surrounding valleys in a densely vegetated environment).
4.2.1. Using DInSAR Coherence and SWIR
Detecting lava flows in non-vegetated regions is particularly efficient using the interferometric coherence [76
]. Active lava flows will appear as highly incoherent areas (coherence values close to zero), contrasting with the coherent surface on which they are emplaced (coherence values typically >0.5). Moreover, because the summits of active volcanoes are usually vegetation-free, the coherence can also be utilized to monitor summital volcanic activity, as eruptive deposits will appear incoherent.
Both applications are illustrated in Figure 6
, which depicts the ongoing Erta Ale eruption and its precursory activity. Indeed, in the months preceding the eruption onset, intense summit activity is indicated from the coherence map analyzed in a 2 × 2 km box around the summit (Figure 6
a black curve). In particular, from June 2016 onwards the northwestern lake becomes active (in addition to the permanently active southeastern lake), and in the following months the activity at both lakes progressively intensifies, leading to multiple lava overflows (e.g., small overflow on 20 August 2016 visible on coherence image in Figure 6
(i), and large overflow on 19 January 2017 visible on Sentinel-2 image). On 28 January 2017, the S1 interferogram shows a strong deformation signal with a distinctive pattern characteristic of a dyke intrusion (Figure 6
c and Figure 6
(ii)), which marks the onset of a >2-year eruption which is still ongoing today [77
]. The eruptive fissure opened multiple new vents located SE of the summit lava lakes (clearly identifiable from S1 coherence and amplitude images), which progressively focused on a single new eruptive vent from which lava flowed during the following months. During this time, the activity at the summit lava lakes significantly decreases (i.e., coherence analysis on 2 × 2 km extent, Figure 6
a black curve), suggesting a possible drainage of the summit lava lakes. Both the coherence and the SWIR analyses computed on a large >20 km spatial scale (Figure 6
a blue curve and Figure 6
b respectively), depict the lava flow emplacement. The good agreement between the two highlights the fact that coherence can be used to track the active flow front, even when S2 images may not be usable due to cloud coverage. From this basic analysis, more elaborate parameters can be extracted offline, such as the lava flow front position through time, or the lava effusion rate, both key for hazard mitigation issues.
The coherence threshold can be adapted to each volcano, in order to account for specific environments (i.e., how incoherent is the surrounding land surface). More elaborate strategies can also be implemented to enhance the decorrelation sensibility to volcano-related processes, such as using NDVI masks (computed from S2 images) to exclude vegetated areas from the analysis. Nevertheless, in certain climatic settings coherence-based detection is simply not the best suited, and intensity-based detection can prove more useful.
4.2.2. Using SAR Intensity
The interferometric coherence is hardly exploitable in regions with very dense vegetation, or where the surface is likely to change rapidly due to various environmental factors (e.g., snow or sand). In such context, changes in the intensity of SAR images can help identify eruptive deposits. Figure 7
illustrates this, taking as example the 2018 eruption of Fuego (Guatemala) which killed over 200 persons due to pyroclastic flows [5
]. The interferometric coherence image (Figure 7
c) computed between SAR image 1 and 2, respectively acquired before and after the eruption, is entirely incoherent and therefore unusable. However, computing the log ratio between the two intensity images (Figure 7
a) reveals substantial changes: areas in blue are those where the intensity has decreased, whereas areas in red are those where the intensity has increased. The latter are mainly confined in the valleys, and likely correspond to the rougher block-and-ash deposit [24
]. Conversely, areas where intensity decreases are concentrated around the summit vent, which could be associated to the deposition of ash.
4.3. Detection of Morphological Changes (SAR)
The intensity of SAR images is strongly dependent on the terrain slope, and is therefore useful to monitor morphological changes affecting the volcano. Because radar wavelengths penetrate through clouds, SAR intensity images provide crucial insights into the volcanic activity when optical imagery is obstructed by atmospheric and/or volcanic gas clouds. We here give two examples taken from very different volcanological settings: the summit crater collapse of Kīlauea (Hawai’i) during the 2018 effusive eruption (Figure 8
a), and the Anak Krakatau (Indonesia) island growth and destruction during the 2018 explosive eruption (Figure 8
Kilauea is well known for its persistent active lava lake. In 2018, it experienced its largest flank eruption and caldera collapse in the last 200 years [79
]. During spring 2018, the lava lake activity was high, which was clearly detected as a hotspot in the S2 SWIR images (Figure 8
a, 13 April 2018). On 30 April 2018, seismicity indicated the intrusion of a dyke along the East Rift Zone, which generated a ~38 km long deformation zone, and multiple eruptive fissures with lava flows rapidly reaching the sea (see Supplementary Material S5
for analysis of S1 and S2 over the entire rift zone). During this time the summit underwent significant changes: lava lake withdrawal (i.e., hotspot disappears in SWIR images, Figure 8
a), accompanied by summit subsidence (i.e., deflation detected in interferogram), progressively evolving in a ~3 km wide caldera collapse (see LIDAR digital elevation model in [80
]), as the shallow magma reservoir was being drained. The progression of the caldera collapse is clearly imaged with SAR intensity images, which reveal the progressive formation of fractures and the profound summit morphological changes accompanying the flank eruption (Figure 8
a and video in Supplementary Material S3
). Ash deposits following the eruption onset is also captured, identified by a decrease in the SAR backscattered intensity on the SE flank of the volcano, also visible in the SWIR images. This decrease can be explained by the fact that fresh ash is less reflective than bare rock owing to its loose structure and high porosity, and that ash deposits smooth the surface, resulting in a more specular reflector which backscatters less energy towards when the slope is facing away from the sensor [23
Krakatau is well-known for its volcano-induced tsunamis. Just over 135 years after the famous 1883 event, the volcano triggered on 22 December 2018 another deadly wave. Analysis of the SAR intensity images clearly shows the progressive island growth in the months preceding the tsunami, due to multiple lava flows reaching the sea and extending the island’s coast line (Figure 8
b and Supplementary Material video S4
). This likely increased the instability of the volcano’s flank, which on 22 December 2018 collapsed, generating a tsunami wave [81
]. Post flank sector collapse images first reveal an amphitheater-shaped scar opened to the sea (Figure 8
b, 31 December 2018 image), which was closed shortly after by an explosion tuff ring, resulting in a ~400 m wide water filled crater (Figure 8
b, 12 January and 15 February 2019 images).
4.4. Detection of SO2 Gas Flux (UV)
On 18 February 2019 a new eruption of the Piton de la Fournaise began. According to OVPF Reports, 14 ± 5 Mm3 of lava were erupted during the 18 days of activity, fed by several eruptive fissures located on the upper east flank of the volcanic cone. Following a phase of gradual increase in volcanic tremor and the intensification of surface activity, the eruption ended abruptly on March 10 (OVPF Reports).
shows the time series of SO2
mass burden recovered from S5P (Figure 9
b), complemented by thermal anomalies recorded from S2 SWIR data (Figure 9
c). The SO2
mass obtained from MOUNTS is compared with the SO2
mass computed by NASA as a measure of correlation between the two datasets. In addition, thermal anomalies detected by S2 are overlaid with the VRP data provided by MIROVA (Figure 9
c) to show correspondence between the active flow area and the heat radiated by the flow surface. Once suitably calibrated, the combination of SO2
and thermal data provides a synoptic view of the gas and magma fluxes during the course of the February–March 2019 eruption which can be used to track eruptive trends and patterns in real time [82
]. Notably for this specific case, the two datasets show consistent trends, indicating a gradual intensification of the effusive and degassing activity during the final phases of the eruption. Thermal anomalies recorded after 10 March and in the absence of gas emission are attributed to the cooling of the lava field. The last SO2
detection (10 March 2019 09:38 UTC) is attributed to the gas plume, no longer fed from the eruptive vent but still inside the 500 × 500 km AOI as it slowly drifts away from the island.
4.5. Combining Ground-Based and Space-Based Sensors
Magma migration within the crust generates stresses, which can result in earthquakes as the surrounding rocks are displaced or fractured. This seismicity, commonly known as volcano-tectonic (VT) seismicity, is often recorded both prior and during volcanic eruptions, within and around the volcanic edifice [83
]. High magnitude VT earthquakes can be recorded and located by global seismological networks, even when the nearest seismic stations are installed several hundreds of kilometers away. In turn, their timing, location, magnitude, and sometimes focal mechanism, are stored in open access global earthquake databases, particularly GEOFON and USGS catalogues. MOUNTS facilitates the interrogation of such catalogs, recovering potential earthquakes recorded in a region centered around the monitored volcano. This data can support the analysis of the volcanic phenomena, especially when the volcano is not equipped with ground-based monitoring instrumentation.
shows the recent eruption of Ambrym (Vanuatu), and illustrates how combining ground-based and space-based sensors helps understand the eruptive dynamics of this volcano located in a very remote and cloud-prone region. On 15 December 2018 and in the days that followed, a swarm of volcano-tectonic earthquakes were recorded in the vicinity of the volcano, with magnitudes ranging between ~4.5 and 5.5 (Figure 10
d). The volcano was known until then for its persistent activity characterized by two active volcanic lakes (Figure 10
(b.1)), responsible for high heat and gas fluxes (Figure 10
b,c respectively), [84
]. Analysis of the SAR intensity images immediately before and after this swarm reveal profound morphological changes (Figure 10
(d.1,d.2)), in particular the collapse and enlargement of the summit crater. DInSAR analyses indicate very strong ground deformation during this period (Figure 10
a red curve, Figure 10
(a.1)), related to dyke intrusion and caldera subsidence [86
]. Simultaneously, the decorrelation in the coherence map increases (Figure 10
a blue curve), due to both the ground deformation and perhaps also pyroclastic deposits. Once stabilized, the coherence map reveals the presence of a new eruptive vent (Figure 10
(a.2)), from which lava was most likely emitted, as suggested by the SWIR image acquired on 15 December 2018 (Figure 10
(b.2)). Interestingly, following this event the volcano completely changed dynamics: the summit lava lakes were most likely drained, as suggested by the absence of thermal anomalies and the cessation of SO2
The key to detecting volcano unrest and understanding the underlying mechanisms is to be able to recognize when a volcano is deviating from its background level of activity. Once the eruption starts on the other hand, the key to decipher the eruptive dynamics and to mitigate the related hazards, is to integrate multiparametric dataset streaming from both space- and ground-based sensors, in order to provide the most comprehensive view of the eruptive phenomena. Both require “monitoring”, i.e., observing the volcanic activity over long periods of time, during both quiescent and eruptive phases. As such, the aim of monitoring platforms such as MOUNTS is twofold: (1) a scientific one, aiming at deepening our understanding of volcanic processes and patterns at stake at active volcanoes, by processing in a systematic way large amounts of data in an effort to construct global databases, and (2) a societal one, aiming at producing more successful eruption forecasts, and providing additional information to the operational community (e.g., local volcano observatories and civil protection) in order to mitigate the risks related to volcanic hazards.
The results presented in this paper intend to demonstrate how the monitoring platform MOUNTS can contribute to both scientific and operational aims. We here discuss the benefits, limitations, and future developments of the system.
5.1. Benefits of MOUNTS
The benefits of the developed system are the following:
Automated processing of free multisensor dataset which provide key parameters for volcano monitoring: surface deformation and reflectivity changes (Sentinel-1), heat anomalies (Sentinel-2), SO2 gas emission (Sentinel-5P), and seismic activity (USGS and GEOFON earthquake catalogues). This interdisciplinary approach allows for assessment of a variety of volcanic phenomena in various volcanological contexts. Moreover, exploiting multiple sensors spanning across the electromagnetic spectrum reduces the dependency to sensing conditions (e.g., night, clouds).
Flexible design allowing fast implementation of new targets to monitor, with freedom regarding the size and shape of the region of interest. This allows to rapidly respond to new eruptive crisis, and adapt to the specific scenarios (e.g., monitoring of summit activity on small ~1–2 km spatial extent, and/or monitoring of effusive activity on large >50 km extent). The system currently monitors 17 volcanoes in various volcanological and climatic settings across the globe, many of which recently experienced large eruptive crisis.
Visualization through an open-access website (www.mounts-project.com
) of both geocoded images (i.e., DInSAR interferograms wrapped/unwrapped, DInSAR coherence map, SAR VV intensity image, SWIR B12-B11-B8A image, and SO2
PBL concentration), and time series of parameters extracted from each image type (i.e., deformation score, number of decorrelated pixels, number of hot pixels, and SO2
mass, respectively). This allows to apprehend the evolution through time of the volcanic activity and eruptive products. Download of full resolution images and time series graphs (PNG format) is readily possible from the website; more specific data download based on user-defined queries is planned, but for the moment possible on-demand only.
Based on the free SNAP toolboxes, providing a unique framework to manipulate data from various satellites, with state-of-the-art processing algorithms (e.g., DInSAR). MOUNTS is open-source, with a Github repository (https://github.com/sebastienValade/mounts
) storing both the source code and a changelog informing on all the notable changes made to the system and website.
Modular architecture, allowing the implementation of new processing algorithms to extract relevant volcanological parameters, or solve specific tasks. As a matter of example, a pre-trained CNN was plugged to detect strong deformation in the interferograms generated by the system.
Automated email alert messaging to dedicated users when specific thresholds are overcome. Interaction with other monitoring systems such as MIROVA is achieved by facilitating access to volcano-dedicated webpages (Figure 3
). Strengthening the interactivity between the systems is planned, in particular by sharing database access in order to confront datasets more easily.
The operational community such as volcano observatories can use MOUNTS and contribute to its development in a number of ways. The IGEPN (Instituto Geofísico de la Escuela Politécnica Nacional) for example, responsible for volcano monitoring in Ecuador, suggested to add Sangay to the list of monitored volcanoes in order to contribute to the surveillance of this remote edifice. The data available on the platform was used freely, and a collaborative exchange was initiated upon request to provide more specific data processing. The resulting material was further analyzed by IGEPN staff according to their needs, and was used in the activity reports describing the ongoing crisis for public information [87
]. (Disclaimers on the data usage and appropriate acknowledgements can be found on the website). Scientific collaborations to investigate specific volcanic processes, or to develop specific methods (based on either the dataset available on the website, or on datasets resulting from more complex analysis) are also welcomed.
5.2. Limitations of MOUNTS and Future Developments
The quantity of data available for volcano monitoring is increasing exponentially, but so is the difficulty to transform it into knowledge. Indeed a number of limitations arise, related to the extraction of meaningful parameters (are we looking at the right variables?), resolution issues (is the sampling in time and space accurate enough?), and data handling issues (how do we deal with the growing mass of data?). MOUNTS is at this stage still a proof-of-concept, which has large potential for improvement. We hereafter discuss the main limitations and development directions.
Improve MOUNTS’ capability to recover parameters informing on the state of volcanic activity, eruptive precursors in particular. While IR and UV spectroradiometry is able to provide rather straightforward parameters (i.e., heat and gas flux respectively), recovering parameters from SAR in a robust and automated fashion is more challenging. In this paper, we show how trained neural networks can achieve complex tasks in a timely and reliable manner, and can be easily implemented in operational processing chains. In particular, strong deformation typically imprint on interferograms as many colored fringes, which are successfully detected. Further development however is needed to detect slow deformation mechanisms, which do not generate deformation patterns with numerous fringes. Future developments should also focus on designing and training neural networks to recover from SAR data other relevant parameters that can inform on volcanic activity. For example, efficient change detection able to exclude changes non related to volcanic activity (e.g., snow fall, vegetation growths, etc.) would prove extremely useful during both pre-eruptive and syn-eruptive phases.
Incorporate additional data types in the processing chain to provide further insights into the volcanic activity. A priority is to analyze Sentinel-3 (S3) TIR data routinely, crucial to monitor ground thermal anomalies at high sampling rate (sensor characteristics similar to MODIS), but also to detect ash plumes in the atmosphere. Ash detection is commonly achieved using the brightness temperature difference (BTD) procedure [88
], applied to two channels centered around 11 and 12 μm. This approach is easily applicable, but also prone to generate false alarms. A number of methods have been developed to overcome this issue [20
], including 3-band algorithms [48
], the BTD algorithm with water vapor correction (BTD-WVC), the Robust Satellite Technique (RST) specifically configured for volcanic ash, and shallow neural networks [89
]. Future developments should therefore implement automated S3 processing to monitor volcanic ash propagation in the atmosphere, which poses a major threat for air traffic in particular.
Incorporate modeling tools to predict the propagation of volcanic eruptive products, using the recovered multiparametric data as input source terms for the models. Such strategies are not new, and are now, thanks to increasing computing power and data availability, becoming achievable in NRT to forecast the propagation of lava flows [91
] or the dispersion of ash plumes into the atmosphere (e.g., [92
]), as well as to predict the geometry and depth of magma bodies responsible for volcano deformation (e.g., [94
]). Monitoring platforms such as MOUNTS should not necessarily include such modeling routines, but should at least strive to provide parameters than can be fed to such models.
Migrate processing tasks on cloud platforms where data is archived. The development of MOUNTS was done by automatizing data download and processing, using free and open-source data and software. In doing so, we were able to keep the costs of this proof-of-concept platform very low (i.e., a single desktop computer manages data download, processing, and hosting of the web server). Nevertheless, this architecture limits the ability to process larger amounts of data, which would require the analysis of the entire available Sentinel dataset over hundreds of volcanoes worldwide. To achieve this, cloud computing strategies are preferable, whereby algorithms would run on a platform where the data is hosted, thereby preventing data download, and at the same time offering higher computing power. Commercial platforms offering such services exist: the Copernicus DIAS platforms (Data and Information Access Services, which include Sobloo, Onda, Creodias, Mundi, and Wekeo), the Amazon Web Services (AWS), or the Google Cloud Platform.
Analyze the recovered multiparametric volcanic time-trends. Monitoring efforts such as the one presented here, allow the development of consistent multiparametric databases on a variety of volcanic settings (i.e., various volcano types, tectonic settings and magma compositions). Such databases are crucial to decipher eruptive patterns, and potentially better estimate future activity [25
]. As a matter of example, decadal heat and gas emission time-trends help decrypt slow mechanisms of magma/gas accumulation and release at active volcanoes [97
]. Clustering of these trends in categorically similar patterns, together with time-series analysis and probabilistic approaches (e.g., [98
]), should be investigated to help decision making and potentially lead to better eruption forecasting. Moreover, incorporating standardized volcano alert level classifications (whereby color-codes help flag the activity of volcanoes [39
]), will help better communicate the level of volcanic unrest and eruption likelihood to local populations and governmental authorities [101
], and potentially lead to better early warning systems.
We present an operational volcano monitoring system, based on the automated download and processing of multisensor satellite-based data (Sentinel-1 SAR, Sentinel-2 SWIR, Sentinel-5P TROPOMI). The recovered data aim at providing key parameters able to inform on the state of volcanic activity, namely: surface deformation, surface reflectivity changes, surface heat anomalies, and SO2
gas emissions. The results are disseminated in NRT on a public website (www.mounts-project.com
), where both geocoded images and multi-parametric time series help understand the activity. Moreover, we demonstrate how artificial intelligence can be used in such monitoring system to solve complex tasks. In particular, we designed and trained a convolutional neural network to detect large deformation signals in wrapped interferograms with no atmospheric corrections. The training was done on synthetically generated interferograms, and evaluated on >1360 real interferograms produced by MOUNTS. Due to the very good performances of the network, it is now incorporated into the operational processing chain, which delivers automatic email alerts to dedicated users when strong deformation is recorded at the monitored volcanoes.
In addition to the set of parameters recovered from spaceborne sensors, we incorporate information available from global earthquake catalogues (GEOFON and USGS) to inform on the seismicity located in the vicinity of the volcano. The utility of integrating both satellite-based parameters (deformation, heat and gas) and ground-based parameters (seismicity) are demonstrated through a number of recent eruptions: Erta Ale 2017, Piton de la Fournaise 2018–2019, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, and Ambrym 2018. We show how this interdisciplinary approach allows for assessment of a variety of volcanic phenomena, ranging from subsurface magma migration, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and SO2 gas emission into the atmosphere. The data processed by MOUNTS is providing insights into the eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of such multiparametric datasets can help better monitor volcanic hazards.