Search Results (109)

Stromboli’s volcanic activity fluctuates in intensity and style, and periods of heightened activity can trigger hazardous events such as crater collapses and lava overflows. This study investigates the volcano’s explosive behavior surrounding the 19 May 2021 crater-rim failure, which primarily affected the N2 crater and partially involved N1, by integrating high-frequency thermal imaging and high-resolution unmanned aerial system (UAS) surveys to quantify eruption parameters and vent morphology. Typically, eruptive periods preceding vent instability are characterized by evident changes in geophysical parameters and by intensified explosive activity. This is quantitatively monitored mainly through explosion frequency, while other eruption parameters are assessed qualitatively and sporadically. Our results show that, in addition to explosion rate, the spattering rate, the predominance of bomb- and gas-rich explosions, and the number of active vents increased prior to the collapse, reflecting near-surface magma pressurization. UAS surveys revealed that the pre-collapse configuration of the northern craters contributed to structural vulnerability, while post-collapse vent realignment reflected magma’s adaptation to evolving stress conditions. The May 2021 events were likely influenced by morphological changes induced by the 2019 paroxysms, which increased collapse frequency and amplified the 2021 failure. These findings highlight the importance of integrating quantitative time series of multiple eruption parameters and high-frequency morphological surveys into monitoring frameworks to improve early detection of system disequilibrium and enhance hazard assessment at Stromboli and similar volcanic systems. Full article

During January–February 2025, the Santorini volcanic complex experienced intense seismic activity, increasing interest and concern regarding the possible reactivation of the magmatic system. This study investigates the spatial and magnitude distribution of seismic events with the aim of distinguishing between tectonic and volcanic earthquakes and understanding the underlying processes governing seismicity in the region. The analysis is based on data from the national and local seismic network, including epicenter and focus determination, local magnitude (M_L) calculation, depth analysis, statistical processing, and the application of machine learning methods for event classification. The results show that tectonic earthquakes are mainly located at depths, D > 8 km along active faults, while volcanic earthquakes are concentrated at shallower levels (D < 5 km) below the volcanic center. The analysis of b values suggests the differentiation of the focal mechanism, with higher values for volcanic events, which is related to fluid and magmatic pressure processes. The spatiotemporal evolution of seismicity demonstrates seismic swarm characteristics, without a main earthquake, which are attributed to processes within the subvolcanic system. The study contributes to improving the understanding of the current seismovolcanic crisis of Santorini and enhances the ability to identify magmatic instability processes in a timely manner, critical for hazard assessment and monitoring of the South Aegean volcanic arc. Full article

Visible cameras are widely adopted low-cost instruments for volcano monitoring. Images can be used to characterize volcanic activity of variable intensity and style and to estimate key eruption source parameters that are essential for assessing volcanic hazards. Nevertheless, the analysis of images from visible cameras is subject to significant sources of uncertainty and operational limitations. In addition to visibility issues caused by meteorological phenomena and variable illumination, assigning the pixel position of an object of interest (e.g., volcanic plumes, ballistic projectiles) to a specific geographic location and elevation is not straightforward, introducing substantial uncertainty in the estimation of eruption parameters. We present PhixCam, a Python tool that allows the user to georeference in the 3D space the visual field of surveillance cameras from minimal input data: a DEM, the camera position, and a reference image where the framed relief can be outlined. The software includes functions to construct conversion matrices that can be adopted to translate the position of pixels into elevation above sea level when different emission directions of volcanic products are considered, thereby allowing users to assess the confidence of the results. This code was tested on a series of cameras of the Chilean Volcanic Surveillance Network, showing its operative potential in volcanic observatories, and on historical pictures, allowing us to estimate data of interest in volcanology for poorly monitored volcanic events. Full article

The Santorini volcano in the South Aegean Volcanic Arc is of great scientific importance. Knowledge of historical eruptions is valuable for better understanding the volcanic cycle and for improved hazard assessments. One of the little-known historical eruptions occurred either in 1570 or in 1573 or from 1570 to 1573 CE. We bring to light a very little-known but reliable Greek manuscript dated in 1588 CE which improves our knowledge about this eruption. The manuscript documents that the eruption occurred in 1572 and took place within the sea caldera between Santorini and Palaia Kameni. It makes it clear that “fire, smoke, and stones” were coming out between the two islands and a new volcanic island named Mikri Kameni was born. This landscape has been verified by independent maps of the 17th and 18th centuries. The floating pumice was transported by the sea as far as to Thessaloniki and Constantinople. Also, we learn a lot about the consequences of the eruption: (1) smoke and heat destroyed the vineyards and the planting season on Santorini, i.e., spring–summer, (2) it is likely that sulfurous gases were released, and (3) the residents of Santorini were forced to move to nearby islands. The duration of the eruption was ~1 year, but the fire and smoke disappeared suddenly. The Volcanic Explosivity Index of the eruption was estimated to be as high as 3. Full article

The Bilbao parish, located on the slopes of the Tungurahua volcano (Ecuador), was heavily impacted by ashfall during eruptions between 1999 and 2016. Volcanic ash may contain toxic metals such as Pb, Cd, Hg, As, and Se, which are linked to neurological, renal, skeletal, pulmonary, and dermatological disorders. This study evaluated metal concentrations in soil (40–50 cm depth, corresponding to the rooting zone of forage grasses), forage (English ryegrass and Kikuyu grass), and raw milk to assess potential risks to livestock and human health. Sixteen georeferenced sites were selected using a simple random probabilistic sampling method considering geological variability, vegetation cover, accessibility, and cattle presence. Samples were digested and analyzed with a SpectrAA 220 atomic absorption spectrophotometer (Varian Inc., Victoria, Australia). Soils (Andisols) contained Hg (1.82 mg/kg), Cd (0.36 mg/kg), As (1.36 mg/kg), Pb (1.62 mg/kg), and Se (1.39 mg/kg); all were below the Ecuadorian limits, except for Hg and Se. Forage exceeded FAO thresholds for Pb, Cd, As, Hg, and Se. Milk contained Pb, Cd, and Hg below detection limits, while Se averaged 0.047 mg/kg, exceeding water safety guidelines. Findings suggest soils act as sources with significant bioaccumulation in forage but limited transfer to milk. Although immediate consumer risk is low, forage contamination highlights long-term hazards, emphasizing the need for monitoring, soil management, and farmer guidance. Full article

The 2021 eruption of the Tajogaite volcano (La Palma, Canary Islands) provided a unique opportunity to investigate the early post-eruptive magnetic structure of a newly formed volcanic edifice. Understanding these structures is essential for improving hazard assessment and risk mitigation strategies. In this study, we present the first high-resolution, drone-based aeromagnetic dataset over the Tajogaite volcano, aimed at clarifying its still-uncertain geodynamic framework at shallow depths. We describe the data acquisition and processing workflows for surveying volcanic terrains, providing insights into the challenges encountered and the methodologies applied. The magnetic dataset was analyzed and used to construct a 3D magnetic susceptibility model of the volcanic edifice and its surroundings. Our results revealed very low magnetic susceptibility values at very shallow depths (~50 m below the surface) over the main volcanic edifice, suggesting the presence of a likely vertical, dyke-like structure feeding the eruption. These findings indicate that these materials remain above their Curie temperature around two years after the eruption. Moreover, the magnetic anomalies display patterns that correlate with the previously inferred two-fault systems, which likely played a critical role in channelling magma toward the eruptive vents. An elongated zone of slightly low magnetic susceptibility was identified following the NE-SW Mazo fault orientation, extending toward the eruptive fissure. This feature was associated with a single, fault-controlled magma pathway that remained at high temperatures at the time of the survey, in agreement with studies in other volcanic environments. This study highlights the value of aeromagnetic surveys, particularly those conducted with drones, as effective tools for advancing our understanding of young and dynamic volcanic systems, especially regarding their shallow structures. Full article

Global geological conditions are complex and variable, characterized by frequent plate movements, earthquakes, and volcanic eruptions. Coupled with significant climate differences, various factors interact to trigger frequent landslide disasters, resulting in substantial losses of life and property. Therefore, landslide monitoring is crucial. Traditional monitoring technologies face limitations when dealing with complex terrains and meeting the demands for high timeliness, while unmanned aerial vehicles (UAVs), with their maneuverability, high resolution, and ability to operate in hazardous environments, have been widely applied in landslide monitoring. This paper provides a comprehensive review of UAV-based 3D reconstruction for landslides, detailing the characteristics and application cases of UAVs, explaining the functions and limitations of sensors such as optical sensors and light detection and ranging (LiDAR), and exploring 3D reconstruction methods based on UAV imagery, LiDAR, and hybrid approaches. It analyzes the applications of UAV 3D reconstruction in landslide emergency investigation, monitoring, and disaster assessment. The paper identifies the technical challenges faced in these applications and proposes corresponding solutions. In addition, UAV-based 3D reconstruction technology—with its centimeter-level spatial resolution—enables the precise delineation of landslide extent and hazard potential, thereby enhancing monitoring accuracy and improving the efficiency of emergency investigations. This technology provides strong technical support for landslide research and prevention, with significant implications for reducing landslide disaster losses. Full article

Nieuwerkerk Volcano, located in the Banda Sea, Indonesia, is a submarine volcano whose entire edifice lies beneath sea level. Its proximity to several inhabited islands raises significant concerns regarding potential impacts from future volcanic hazards. Despite historical unrest recorded in 1925 and 1927, a comprehensive geological and geophysical understanding of Nieuwerkerk remains notably limited, with the last research expedition being in 1930. This study seeks to advance our understanding of the geomorphological structure and subsurface characteristics of the region, contributing to a preliminary hazard assessment and delineating key directions for future geoscientific investigation. The data were obtained during our most recent expedition conducted in 2022. High-resolution multibeam bathymetry data were analyzed to delineate the volcano’s morphology, while marine magnetic survey data were processed to interpret magnetic anomalies associated with its structure beneath volcano. Our updated morphological analysis reveals the following: (1) Nieuwerkerk Volcano is among the largest submarine volcanic edifices in the Banda Sea (length = 80 km, width = 30 km, height = 3460 m); (2) there is the presence of twin peaks (depth~300m); (3) there are indications of sector collapse (diameter = 10–12 km); (4) there are significant fault lineaments; and (5) there are landslide deposits, suggesting a complex volcanic edifice shaped by various constructive and destructive processes. The magnetic data show a low magnetic anomaly beneath the surface, where one of the indications is the presence of active magma. These findings significantly enhance our understanding of Nieuwerkerk’s current condition and volcanic evolution for an initial assessment of potential hazards, including future eruptions, edifice collapse, and landslides, which could subsequently trigger tsunamis. Further investigation, including comprehensive geophysical surveys covering the entire Nieuwerkerk area, rock sample analysis, visual seafloor observation, and seawater characterization, is crucial for a comprehensive understanding of its magmatic system and a more robust hazard assessment. This research highlights the critical need for detailed investigations of active submarine volcanoes, particularly those with sparse historical records and close proximity to populated areas, within tectonically complex settings such as the Banda Sea. Full article

The Tokara Islands, a volcanic archipelago located south of Japan’s main islands, experienced earthquake swarm activity in 2025. Public concern has emerged regarding the potential triggering of the anticipated Nankai Trough earthquake, which the Japan Meteorological Agency has dismissed; however, the underlying mechanisms of this seismic activity remain inadequately explained. This study employs Exploratory Data Analysis (EDA) to characterise the statistical properties of the swarm and compare them with historical patterns. Earthquake intervals followed exponential distributions, but swarm events exhibited distinctive short intervals that clearly distinguished them from background seismicity. Similarly, whilst earthquake magnitudes conformed to normal distributions, swarm events demonstrated low mean values and reduced variability, characteristics markedly different from regional background activity. The frequency and magnitude distributions of the 2025 swarm demonstrate remarkable similarity to two previous swarms that occurred in 2021. All the episodes coincided with volcanic activity at Suwanose Island, located approximately 10 km from the epicentral region, suggesting a causal relationship between magmatic processes and seismic activity. Statistical analysis reveals that the earthquake swarm exhibits exceptionally low magnitude scale, characteristics consistent with magma-driven seismicity rather than tectonic stress accumulation. The parameter contrasted markedly with pre-seismic conditions observed before the 2011 Tohoku earthquake, where it was substantially elevated. Our findings indicate that the current seismic activity represents localised volcanic-related processes rather than precursory behaviour associated with major tectonic earthquakes. These results demonstrate the utility of statistical seismology in distinguishing between volcanic and tectonic seismic processes for hazard assessment purposes. Full article

Geohazard investigation in volcanic fields is essential for understanding and mitigating risks associated with volcanic activity. Volcanic vents are often concealed by processes such as faulting, subsidence, or uplift, which complicates their detection and hampers hazard assessment. To address this challenge, we developed a predictive framework that integrates high-resolution gravity data with multiple machine learning algorithms. Logistic Regression, Gradient Boosting Machine (GBM), Decision Tree, Support Vector Machine (SVM), and Random Forest models were applied to analyze the gravitational characteristics of known volcanic vents and predict the likelihood of undiscovered vents at other locations. The problem was formulated as a binary classification task, and model performance was assessed using accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The Random Forest algorithm yielded optimal outcomes: 95% classification accuracy, AUC-ROC score of 0.99, 75% geographic correspondence between real and modeled vent sites, and a 95% certainty degree. Spatial density analysis showed that the distribution patterns of predicted and actual vents are highly similar, underscoring the model’s reliability in identifying vent-prone areas. The proposed method offers a valuable tool for geoscientists and disaster management authorities to improve volcanic hazard evaluation and implement effective mitigation strategies. These results represent a significant step forward in our ability to model volcanic dynamics and enhance predictive capabilities for volcanic hazard assessment. Full article

Accurate estimation of erupted lava volumes is essential for understanding volcanic processes, interpreting eruptive cycles, and assessing volcanic hazards. Traditional methods based on Mid-Infrared (MIR) satellite imagery require clear-sky conditions during eruptions and are prone to sensor saturation, limiting data availability. Here, we present an alternative approach based on the post-eruptive Thermal InfraRed (TIR) signal, using the recently proposed VRP_TIR method to quantify radiative energy loss during lava flow cooling. We identify thermally anomalous pixels in VIIRS I5 scenes (11.45 µm, 375 m resolution) using the TIRVolcH algorithm, this allowing the detection of subtle thermal anomalies throughout the cooling phase, and retrieve lava flow area by fitting theoretical cooling curves to observed VRP_TIR time series. Collating a dataset of 191 mafic eruptions that occurred between 2010 and 2025 at (i) Etna and Stromboli (Italy); (ii) Piton de la Fournaise (France); (iii) Bárðarbunga, Fagradalsfjall, and Sundhnúkagígar (Iceland); (iv) Kīlauea and Mauna Loa (United States); (v) Wolf, Fernandina, and Sierra Negra (Ecuador); (vi) Nyamuragira and Nyiragongo (DRC); (vii) Fogo (Cape Verde); and (viii) La Palma (Spain), we derive a new power-law equation describing mafic lava flow thickening as a function of time across five orders of magnitude (from 0.02 Mm³ to 5.5 km³). Finally, from knowledge of areas and episode durations, we estimate erupted volumes. The method is validated against 68 eruptions with known volumes, yielding high agreement (R² = 0.947; ρ = 0.96; MAPE = 28.60%), a negligible bias (MPE = −0.85%), and uncertainties within ±50%. Application to the February-March 2025 Etna eruption further corroborates the robustness of our workflow, from which we estimate a bulk erupted volume of 4.23 ± 2.12 × 10⁶ m³, in close agreement with preliminary estimates from independent data. Beyond volume estimation, we show that VRP_TIR cooling curves follow a consistent decay pattern that aligns with established theoretical thermal models, indicating a stable conductive regime during the cooling stage. This scale-invariant pattern suggests that crustal insulation and heat transfer across a solidifying boundary govern the thermal evolution of cooling basaltic flows. Full article

Thermal infrared (TIR) ground observations are a well-established method for investigating surface temperature variations in thermally anomalous areas. However, commercially available technical solutions are currently limited, often offering proprietary products with minimal customization options for establishing a permanent TIR monitoring network. This work presents the comprehensive development of a thermal infrared monitoring network, detailing everything from the hardware schematics of the remote monitoring station (RMS) to the code for the final data processing software. The procedures implemented in the RMS for managing TIR sensor operations, acquiring environmental data, and transmitting data remotely are thoroughly discussed, along with the technical solutions adopted. The processing of TIR imagery is carried out using ASIRA (Automated System of InfraRed Analysis), a free software package, now developed for GNU Octave. ASIRA performs quality filtering and co-registration, and applies various seasonal correction methodologies to extract time series of deseasoned surface temperatures, estimate heat fluxes, and track variations in thermally anomalous areas. Processed outputs include binary, Excel, and CSV formats, with interactive HTML plots for visualization. The system’s effectiveness has been validated in active volcanic areas of southern Italy, demonstrating high reliability in detecting anomalous thermal behavior and distinguishing endogenous geophysical processes. The aim of this work is to enable readers to easily replicate and deploy this open-source, low-cost system for the continuous, automated thermal monitoring of active volcanic and geothermal areas and environmental pollution, thereby supporting hazard assessment and scientific research. Full article

Heavy metals are a major cause for concern in relation to water systems, due to their high toxicity at elevated levels. The metals can originate from both natural processes, including geological weathering and volcanic activity, as well as anthropogenic activi-ties such as industrial discharges, agricultural runoff, mining, and urbanization, which significantly contribute to water pollution and environmental degradation. The as-sessment of these risks is crucial for protecting public health, especially in populations reliant on contaminated water sources. Exposure to such contaminants can result in severe health consequences, including neurological impairments, organ deterioration, and an elevated risk of cancer. To conduct this assessment study, six surface water sampling sites were selected (i.e., S1 (Gobia), S2 (Kouamefla), S3 (Benkro), S4 (Dou-kouya), S5 (Doka), and S6 (Zengue)) due to their proximity to mining activities. We used the hazard quotient (HQ) and hazard index (HI) methods to estimate the levels of non-carcinogenic health risk associated with heavy metals. Then, the assessment of carcinogenic health risk was carried out using the Incremental Lifetime Cancer Risk (ILCR) methods. First, the highest ILCR total values were observed in the Doya locality (i.e., 0.4237 for the children and 0.5650 for the adults) and during the great dry season (i.e., 0.4333 for the children and 0.5743 for the adults). These findings highlight that populations in this locale experience heightened exposure during the period of the Great Rainy Season. The results indicated that the population exposed to Cd and Hg may experience health concerns irrespective of season and locality. For As and Pb, risks are present in both seasons (i.e., Short Dry Season (SDS) and Short Rainy Season (SRS)). On the other hand, the HIs are well above 1, indicating that the population may be exposed to non-carcinogenic diseases associated with the metals, regardless of the season or locality. To further explore the results, the assessment by ILCR was em-ployed, which demonstrated that for all the designated localities, the ILCRs of As and Cd are well above 10⁻⁴ for the entire population, indicating that the population con-suming this water may develop major carcinogenic risks. In addition, the highest ILCR values were obtained for Cd, regardless of the age group. It should be noted that sea-sonal variation had no significant effect on the trend in ILCRs determined for the en-tire population. Full article

In recent years, numerous satellite-based systems have been developed to monitor and study volcanic activity from space. This progress reflects the growing demand for accurate and timely monitoring to reduce volcanic risk. Observing volcanoes from a satellite perspective provides key advantages, enabling continuous data acquisition and near-real-time assessment of volcanic activity. Multispectral sensors operating across various regions of the electromagnetic spectrum can detect thermal anomalies associated with lava flows, pyroclastic flows, ash plumes, and volcanic gases. Traditional hotspot detection techniques based on fixed thresholds often miss subtle anomalies on a global scale. In contrast, advanced machine learning algorithms offer a data-driven alternative. We designed and implemented the V-STAR application (Volcanic Satellite Thermal Anomalies Recognition) on Google Earth Engine (GEE) to leverage cloud computing for processing large geospatial datasets in real time. It employs supervised machine learning, specifically Random Forests, to adapt to evolving volcanic conditions. This enhances the accuracy and responsiveness of volcanic monitoring, offering valuable insights into potential eruptive behavior. Here, we present V-STAR as a robust and accessible tool that integrates satellite data and advanced analytics. Through its intuitive interface, V-STAR provides a comprehensive visualization of key volcanic features. The resulting analyses reveal hidden patterns in thermal data, contributing to improved disaster risk reduction strategies associated with volcanic hazards. Full article