Search Results (101)

The Flexible Combined Imager (FCI) instrument aboard the Meteosat Third Generation (MTG-I) geostationary satellite, launched in December 2022 and operational since September 2024, by providing shortwave infrared (SWIR), medium infrared (MIR) and thermal infrared (TIR) data, with an image refreshing time of 10 min and a spatial resolution ranging between 500 m in the high-resolution (HR) and 1–2 km in the normal-resolution (NR) mode, may represent a very promising instrument for monitoring thermal volcanic activity from space, also in operational contexts. In this work, we assess this potential by investigating the recent Mount Etna (Italy, Sicily) eruption of February–March 2025 through the analysis of daytime and night-time SWIR observations in the NR mode. The time series of a normalized hotspot index retrieved over Mt. Etna indicates that the effusive eruption started on 8 February at 13:40 UTC (14:40 LT), i.e., before information from independent sources. This observation is corroborated by the analysis of the MIR signal performed using an adapted Robust Satellite Technique (RST) approach, also revealing the occurrence of less intense thermal activity over the Mt. Etna area a few hours before (10.50 UTC) the possible start of lava effusion. By analyzing changes in total SWIR radiance (TSR), calculated starting from hot pixels detected using the preliminary NHI algorithm configuration tailored to FCI data, we inferred information about variations in thermal volcanic activity. The results show that the Mt. Etna eruption was particularly intense during 17–19 February, when the radiative power was estimated to be around 1–3 GW from other sensors. These outcomes, which are consistent with Multispectral Instrument (MSI) and Operational Land Imager (OLI) observations at a higher spatial resolution, providing accurate information about areas inundated by the lava, demonstrate that the FCI may provide a relevant contribution to the near-real-time monitoring of Mt. Etna activity. The usage of FCI data, in the HR mode, may further improve the timely identification of high-temperature features in the framework of early warning contexts, devoted to mitigating the social, environmental and economic impacts of effusive eruptions, especially over less monitored volcanic areas. Full article

The Orbiting Carbon Observatory-3 (OCO-3) mission provides a new perspective for studying atmospheric carbon dioxide (CO₂). Here we assess the potentiality of OCO-3 satellite acquisitions to analyse and monitor the CO₂ emissions from Mt. Etna volcano. While OCO-3 data are well-suited for gas analysis on a regional spatial scale, they have not yet been widely utilised for studying volcanic carbon dioxide emissions. The Snapshot Area Map (SAM) acquisition mode enables the capture of targeted snapshots over volcanic regions, allowing for the measurement of CO₂ concentrations in the vicinity of volcanic structures. In this work, we analyse 62 OCO-3 images acquired between 2020 and 2023, focusing on measurements within a 20 km radius of Mt. Etna’s summit, where the main craters are located. Atmospheric CO₂ concentrations are examined as a function of distance from the summit, and assuming a linear decreasing trend, the angular coefficient is computed. Lower angular coefficient values may indicate a stronger volcanic CO₂ contribution. Considering both the number of sampled pixels in each OCO-3 snapshot and the associated uncertainties in the angular coefficient calculation, we identify five days with potentially significant CO₂ emissions from Mt. Etna, likely associated with specific volcanic activity phases. The eruptive activity on these five days is further investigated, revealing a possible correlation between elevated gas emissions and intense volcanic phenomena, such as lava fountains. This assessment is supported by thermal activity analyses using SEVIRI, MODIS, and VIIRS satellite data. Full article

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

Sulfur dioxide (SO₂) is sourced by degassing magma in the shallow crust; hence its monitoring provides information on the rates of magma ascent in the feeding conduit and the style and intensity of eruption, ultimately contributing to volcano monitoring and hazard assessment. Here, we present a new algorithm to extract SO₂ data from the TROPOMI imaging spectrometer aboard the Sentinel-5 Precursor satellite, which delivers atmospheric column measurements of sulfur dioxide and other gases with an unprecedented spatial resolution and daily revisit time. Specifically, we automatically extract the volcanic clouds by introducing a two-step approach. Firstly, we used the Simple Non-Iterative Clustering segmentation method, which is an object-based image analysis approach; secondly, the K-means unsupervised machine learning technique is applied to the segmented images, allowing a further and better clustering to distinguish the SO₂. We implemented this algorithm in the open-source Google Earth Engine computing platform, which provides TROPOMI imagery collection adjusted in terms of quality parameters. As case studies, we chose three volcanoes: Mount Etna (Italy), Taal (Philippines) and Sangay (Ecuador); we calculated sulfur dioxide mass values from 2018 to date, focusing on a few paroxysmal events. Our results are compared with data available in the literature and with Level 2 TROPOMI imagery, where a mask is provided to identify SO₂, finding an optimal agreement. This work paves the way to the release of SO₂ flux time series with reduced delay and improved calculation time, hence contributing to a rapid response to volcanic unrest/eruption at volcanoes worldwide. Full article

Sulfur dioxide (SO₂) can be of natural and anthropogenic origin and is one of the sulfur compounds present in the atmosphere. Among natural sources, volcanoes contribute with relevant annual outputs, and major eruptions lead to spikes in these outputs. In the case of anthropogenic pollution, SO₂ emissions are mostly correlated with the sulfur content of fuels, which has been the focus of specific emission mitigation policies for decades. Following other examples of cyclic and multi-year evaluations, an analysis of SO₂ at the Lamezia Terme (code: LMT) WMO/GAW (World Meteorological Organization—Global Atmosphere Watch) station in Calabria, Southern Italy, was performed. The coastal site is characterized by wind circulation patterns that result in the detection of air masses with low or enhanced anthropic influences. The presence of the Aeolian Arc of active, quiescent, and extinct volcanoes, as well as Mount Etna in Sicily, may influence LMT observations with diffused SO₂ emissions. For the first time in the history of the LMT, a multi-year analysis of a parameter has been integrated with TROPOMI data gathered by Sentinel-5P and used to test total tropospheric column densities at the LMT itself and select coordinates in the Tyrrhenian and Ionian seas. Surface and satellite data indicate that SO₂ peaks at the LMT are generally linked to winds from the western–seaside wind corridor, a pattern that is compatible with active volcanism in the Tyrrhenian Sea and maritime shipping to and from the Gioia Tauro port located in the same region. The findings of this research provide the basis for enhanced source apportionment, which could further differentiate anthropogenic sources in the area from natural outputs. Full article

Paroxysmal explosive activity at Etna volcano (Italy) has become quite frequent over the last three decades, raising concerns with the civil protection authorities due to its significant impact on the local population, infrastructures, viability and air traffic. Between 4 July and 15 August 2024, during the tourist season peak when the local population doubles, Etna volcano gave rise to a sequence of six paroxysmal explosive events from the summit crater named Voragine. This is the oldest and largest of Etna’s four summit craters and normally only produces degassing, with the previous explosive sequences occurring in December 2015 and May 2016. In this paper, we use thermal images recorded by the monitoring system maintained by the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV–OE), and an automatic procedure previously tested in order to automatically define the eruptive parameters of the six lava fountain episodes. These data allowed us to infer the eruptive processes and gain some insights on the evolution of the explosive sequences that are useful for hazard assessment. Specifically, our results lead to the hypothesis that the Voragine shallow storage has a capacity of ~12–15 Mm³, which was not completely emptied with the last two paroxysmal events. It is thus possible that one or two additional explosive paroxysmal events could occur in the future. It is noteworthy that an additional paroxysmal episode occurred at Voragine on 10 November 2024, after the submission of this paper, thus confirming our hypothesis. Full article

Between October 2021 and July 2024, radon measurements in air and soil were carried out in the South and East flanks of Etna volcano to check the possible correlation between radon emissions and active faults/eruptive fissures and to obtain preliminary data on any negative impacts on human health. Fifteen continuous indoor radon monitors were installed in homes, some of which are inhabited by patients suffering from Multiple Sclerosis and Amyotrophic Lateral Sclerosis. In all sites, the limit of 300 Bq/m³ indicated by the Euratom Directive 2013/59 was exceeded, even if slightly and for short periods. The highest values were recorded closest to active fault zones and during winters. Furthermore, 27 discrete indoor radon measurements were carried out using a passive method by means of activated charcoal canisters that were exposed for 48 h. Most of the values (>70%) were <100 Bq/m³; six canisters gave values >100 Bq/m³ and one >200 Bq/m³. Measurements of radon in soils were carried out using a Durridge RAD7 in the gardens of the homes in which the indoor radon measurements were made. The background radon values in soils were <5000 Bq/m³; the highest values (12,500 Bq/m³) were measured near the Aci Catena fault. The role of Etna’s faults in draining the deeper radon towards the surface and, therefore, into nearby homes is evident, with a consequent increase in the health risk caused by indoor radon pollution. Full article

Volcanic hazard assessment is generally based on past eruptive behavior, assuming that previous activity is representative of future activity. Hazard assessment can be supported by Artificial Intelligence (AI) techniques, such as machine learning, which are used for data exploration to identify features of interest in the data. Here, we applied a machine learning technique to automate the analysis of these datasets, handling intricate patterns that are not easily captured by explicit commands. Using the k-means clustering algorithm, we classified effusive eruptions of Mount Etna over the past 400 years based on key parameters, including lava volume, Mean Output Rate (MOR), and eruption duration. Our analysis identified six distinct eruption clusters, each characterized by unique eruption dynamics. Furthermore, spatial analysis revealed significant sectoral variations in eruption activity across Etna’s flanks. These findings, derived through unsupervised learning, offer new insights into Etna’s eruptive behavior and contribute to the development of hazard maps that are essential for long-term spatial planning and risk mitigation. Full article

The 16th century AD St. Barbara’s Church in Paternò, a town located at the SW foot of Mt. Etna volcano (Sicily, Italy), has since 2009 showed evident signs of structural instability and collapse. This is causing great concern among the local population and poses a growing hazard to the attendees to the masses. After precautionary closure of the church, we carried out geological, seismic, geophysical and geochemical surveys in order to shed light on the possible causes of the phenomenon. From the results of all surveys above, the presence of a hidden fault was hypothesized. The fault would prove to cross the west side of the church, parallel to its front portal, and continue both to the north and to the south of the edifice. It is part of a more complex system of faults that crosses the whole town of Paternò and is likely a result of the complex dynamics of Mt. Etna. This fault seems to also be a pathway for the upward flow of saline hydrothermal fluids, similar in composition to those emitted in nearby areas and whose corrosive action possibly contributed to the weakening of the rocks beneath the church. Temporal monitoring of several hydrological parameters (water temperature, water level and CO₂ content) in some sites in and around the church allowed a better understanding both of the fault dynamics and of the extent of hydrothermal influence in the studied area. Full article

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity. Full article

On 21 May 2023, a hidden eruption occurred at the Southeast Crater (SEC) of Etna (Italy); indeed, bad weather prevented its direct and remote observation. Tephra fell toward the southwest, and two lava flows propagated along the SEC’s southern and eastern flanks. The monitoring system of the Istituto Nazionale di Geofisica e Vulcanologia testified to its occurrence. We analyzed the seismic and infrasound signals to constrain the temporal evolution of the fountain, which lasted about 5 h. We finally reached Etna’s summit two weeks later and found an unexpected pyroclastic density current (PDC) deposit covering the southern lava flow at its middle portion. We performed unoccupied aerial system and field surveys to reconstruct in 3D the SEC, lava flows, and PDC deposits and to collect some samples. The data allowed for detailed mapping, quantification, and characterization of the products. The resulting lava flows and PDC deposit volumes were (1.54 ± 0.47) × 10⁶ m³ and (1.30 ± 0.26) × 10⁵ m³, respectively. We also analyzed ground-radar and satellite data to evaluate that the plume height ranges between 10 and 15 km. This work is a comprehensive analysis of the fieldwork, UAS, volcanic tremor, infrasound, radar, and satellite data. Our results increase awareness of the volcanic activity and potential dangers for visitors to Etna’s summit area. Full article

The geochemical monitoring of volcanic activity today relies largely on remote sensing, but the combination of this approach together with soil gas monitoring, using the appropriate parameters, is still not widely used. The main purpose of this study was to correlate data from crater gas emissions with flank emissions of soil gases at Mt. Etna volcano from June 2006 to December 2020. Crater SO₂ fluxes were measured from fixed stations around the volcano using the DOAS technique and applying a modeled clear-sky spectrum. The SO₂/HCl ratio in the crater plume was measured with the OP-FTIR technique from a transportable instrument, using the sun as an IR source. Soil CO₂ efflux coupled with the ²²⁰Rn/²²²Rn activity ratio in soil gases (named SGDI) were measured at a fixed monitoring site on the east flank of Etna. All signals acquired were subject both to spectral analysis and to filtering of the periodic signals discovered. All filtered signals revealed changes that were nicely correlated both with other geophysical signals and with volcanic eruptions during the study period. Time lags between parameters were explained in terms of different modes of magma migration and storage inside the volcano before eruptions. A comprehensive dynamic degassing model is presented that allows for a better understanding of magma dynamics in an open-conduit volcano. Full article

Ground-based infrared cameras can be used effectively and safely to provide quantitative information about small to moderate-sized volcanic eruptions. This study describes an infrared camera that has been used to measure emissions from the Mt. Etna and Stromboli (Sicily, Italy) volcanoes. The camera provides calibrated brightness temperature images in a broadband (8–14 µm) channel that is used to determine height, plume ascent rate and volcanic cloud/plume temperature and emissivity at temporal sampling rates of up to 1 Hz. The camera can be operated in the field using a portable battery and includes a microprocessor, data storage and WiFi. The processing and analyses of the data are described with examples from the field experiments. The updraft speeds of the small eruptions at Stromboli are found to decay with a timescale of ∼10 min and the volcanic plumes reach thermal equilibrium within ∼2 min. A strong eruption of Mt. Etna on 1 April 2021 was found to reach ∼9 km, with ascent speeds of 10–20 ms⁻¹. The plume, mostly composed of the gases CO₂, water vapour and SO₂, became bent over by the prevailing winds at high levels, demonstrating the need for multiple cameras to accurately infer plume heights. Full article

This paper describes the successes and failures after 4 years of continuous operation of a network of sensors, communicating nodes, and gateways deployed on the Etna Volcano in Sicily since 2019, including a period of Etna intense volcanic activity that occurred in 2021 and resulted in over 60 paroxysms. It documents how the installation of gateways at medium altitude allowed for data collection from sensors up to the summit craters. Most of the sensors left on the volcanic edifice during winters and during this period of intense volcanic activity were destroyed, but the whole gateway infrastructure remained fully operational, allowing for a very fruitful new field campaign two years later, in August 2023. Our experience has shown that the best strategy for IoT deployment on very active and/or high-altitude volcanoes like Etna is to permanently install gateways in areas where they are protected both from meteorological and volcanic hazards, that is mainly at the foot of the volcanic edifice, and to deploy temporary sensors and communicating nodes in the more exposed areas during field trips or in the summer season. Full article

During explosive eruptions, tephra fallout represents one of the main volcanic hazards and can be extremely dangerous for air traffic, infrastructures, and human health. Here, we present a new technique aimed at identifying the area covered by tephra after an explosive event, based on processing PlanetScope imagery. We estimate the mean reflectance values of the visible (RGB) and near infrared (NIR) bands, analyzing pre- and post-eruptive data in specific areas and introducing a new index, which we call the ‘Tephra Fallout Index (TFI)’. We use the Google Earth Engine computing platform and define a threshold for the TFI of different eruptive events to distinguish the areas affected by the tephra fallout and quantify the surface coverage density. We apply our technique to the eruptive events occurring in 2021 at Mt. Etna (Italy), which mainly involved the eastern flank of the volcano, sometimes two or three times within a day, making field surveys difficult. Whenever possible, we compare our results with field data and find an optimal match. This work could have important implications for the identification and quantification of short-term volcanic hazard assessments in near real-time during a volcanic eruption, but also for the mapping of other hazardous events worldwide. Full article