Search Results (104)

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

The Abu Farayed Granite (AFG), located in the southeastern desert of Egypt, was intruded during the early to late stages of Pan-African orogeny that prevailed within the Arabian–Nubian Shield. The AFG intrudes an association of gneisses, island arc volcano–sedimentary rocks, and serpentinite masses. Field observations, supported by remote sensing and geochemical data, reveal a composite granitic intrusion that is differentiated into two magmatic phases. The early granitic phase comprises weakly deformed subduction-related calc–alkaline rocks ranging from diorite to tonalite, while the later encloses undeformed granodiorite and granite. Landsat-8 (OLI) remote sensing data have shown to be highly effective in discriminating among the different varieties of granites present in the area. Furthermore, the data have provided important insights into the structural characteristics of the AFG region. Specifically, the data indicate the presence of major tectonic trends with ENE–WSW and NW–SE directions transecting the AFG area. Geochemically, the AFG generally has a calc–alkaline metaluminous affinity with relatively high values of Cs, Rb, K, Sr, Nd, and Hf but low contents of Nb, Ta, P, and Y. The early magmatic phase has lower alkalis and REEs, while the later phases have higher alkalis and REEs with distinctly negative Eu anomalies. The AFG is structurally controlled, forming a N–S arch, which may be due to the influence of the wadi Hodein major shear zone. The diorite and tonalite are believed to have been originally derived from subduction-related magmatism during regional compression. This began with the dehydration of the descending oceanic crust with differential melting of the metasomatized mantle wedge. Magma ascent was long enough to react with the thickened crust and therefore suffered fractional crystallization and assimilation (AFC) to produce the calc–alkaline diorite–tonalite association. The granodiorite and granites were produced due to partial melting, assimilation, and fractionation of lower crustal rocks (mainly diorite–tonalite of the early stage) after subduction and arc volcanism during a late orogenic relaxation–rebound event associated with uplift transitioning to extension. Full article

The EN280 Leba Road is a mountain road that runs along the western slope of Serra da Leba (Humpata Plateau) and its outstanding escarpments, connecting the hinterland areas of the Province of Huila to the coastal Atlantic Province of Namibe, in Southwest Angola. In the Serra da Leba ranges, as in Humpata Plateau, a volcano-sedimentary succession of Paleo-Mesoproterozoic age known as the Chela Group outcrops extensively. This main unit records a pile of sediments with a thickness over 600 m, overlying a cratonic basement with Eburnean and pre-Eburnean granitoids. This sequence is overlain in unconformity by the Leba Formation, which consists of weakly deformed cherty dolostones rich in stromatolites. Along the EN280 Leba Road, in the downward direction, were inventoried and characterized eight sites that, by their exceptional geological content and the singularity of their geoforms, are worth being defined and formalized as geosites: (1) traditional mining clay pit in the Humpata Plateau (post-Eburnean Paleo-Mesoproterozoic claystones); (2) old lime oven of Leba (post-Eburnean Meso-Neoproterozoic cherty dolostones with stromatolites); (3) viewpoint of the Serra da Leba (post-Eburnean Paleo-Mesoproterozoic volcano-sedimentary formations and Eburnean Paleoproterozoic granitoids); (4) vertical beds at the beginning of the descent (post-Eburnean Paleo-Mesoproterozoic volcano-sedimentary formations); (5) slope of the fault propagation fold (post-eburnean Paleo-Mesoproterozoic volcano-sedimentary formations); (6) reverse fault in granitoid rocks (Eburnean Paleoproterozoic granitoids); (7) Dolerite Curve (Eburnean Paleoproterozoic granitoids and dolerites); (8) ductile simple shear zone (Eburnean Paleoproterozoic granitoids and mylonites). These sites were primarily selected using the results of fieldwork (observations, measurements, reproduction of representations, and creation of models), interpretation of remote sensing data, and data from previously published bibliographies and cartography. A quantitative assessment of the selected sites to be preserved through their classification as geosites (integration in a geoconservation strategy) was proposed. The first position in the numerical assessment is occupied by the landscape dimension geosite “Viewpoint of the Serra da Leba”. This position is conferred, mainly, by its high geological, use, and Management values, being therefore considered the place with the highest geoheritage value in the studied area. Based on the previous characterization and evaluation, several field activities were proposed to be included in a guidebook, highlighting aspects such as landscapes, outcrops, rocks, structures, fossils, and georesources. The high scientific, didactic, and aesthetic values of these geological contexts and their high degree of geodiversity justify their integration into a geoeducational transect, contributing to the appreciation and awareness of the geological heritage of Serra da Leba, as well as to its promotion and scientific and educational dissemination. 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

Understanding the dynamics of volcanic activity is crucial for volcano observatories in their efforts to forecast volcanic hazards. Satellite imager data hold promise in offering crucial insights into the thermal behavior of active volcanoes worldwide, facilitating the assessment of volcanic activity levels and identifying significant changes during periods of volcano unrest. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, aboard NASA’s Terra and Aqua satellites, provides invaluable data with high temporal and spectral resolution, enabling comprehensive thermal monitoring of eruptive activity. The accuracy of volcanic activity characterization depends on the quality of models used to relate the relationship between volcanic phenomena and target variables such as temperature. Under these circumstances, machine learning (ML) techniques such as decision trees can be employed to develop reliable models without necessarily offering any particular or explicit insights. Here, we present a ML approach for quantifying volcanic thermal activity levels in near real time using thermal infrared satellite data. We develop an unsupervised Isolation Forest machine learning algorithm, fully implemented in Google Colab using Google Earth Engine (GEE) which utilizes MODIS Land Surface Temperature (LST) data to automatically retrieve information on the thermal state of volcanoes. We evaluate the algorithm on various volcanoes worldwide characterized by different levels of volcanic activity. 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

Tsunami monitoring and early warning systems are mainly established to deal with seismogenic tsunamis generated by sudden seafloor fault displacement. However, a global tsunami triggered by the 2022 Tonga volcanic eruption promoted the need for tsunami early warning and hazard mitigation of non-seismogenic tsunamis in coastal countries. This paper studied the spatiotemporal distribution characteristics of historical volcanic tsunamis and summarized high-risk areas of volcanic tsunamis. The circum southwestern Pacific volcanic zone, including the Sunda volcanic belt and the Indo-Australian plate, is a concentrated area of active volcanoes and major volcanic tsunamis. In addition, the challenges associated with adapting seismogenic tsunami techniques for use in the context of volcanic tsunamis were elucidated. At the same time, based on historical records and post-disaster surveys, typical historical volcanic tsunami events and involved mechanisms were summarized. The results show that a majority of volcanic tsunamis may involve multiple generation mechanisms, and some mechanisms show geographical distribution characteristics. The complexity of volcanic tsunami mechanisms poses challenges to tsunami early warning by measuring tsunami sources to evaluate the possible extent of impact, or using numerical modeling to simulate the process of a tsunami. Therefore, a concise overview of the lessons learned and the current status of early warning systems for volcanic tsunamis was provided. Finally, a conceptual scheme of monitoring systems for volcanic tsunamis based on historical volcanoes, real-time volcanic eruption information and sea level data, as well as remote sensing images, was presented. Full article

On 19th September 2021, a protracted eruption of the Cumbre Vieja Volcano on the Canary Island of La Palma commenced and continued for a duration of 12 weeks. Lava flows starting from the rift zone at the mid-western flank of Cumbre Vieja advanced toward the western coast of the island. The eruption was monitored by different remote sensing satellites, including the Copernicus Sentinel missions and DESIS. The Sentinel-2 Copernicus satellites acquired multispectral data from 15th September onward. On September 30th, and with a difference of ∼2 h with respect to Sentinel-2 A, the DESIS hyperspectral sensor also acquired data from the volcano and then again on 15th October 2021. Typically, mid-infrared (around 3.8 $\mathsf{\mu }$m) data are used for the thermal analysis of active lava flows. However, neither Sentinel-2 nor DESIS possesses mid-infrared bands and the Sentinel-2 high-wavelengths bands (∼2 $\mathsf{\mu }$m) have some limitations. Nevertheless, the hyperspectral character of DESIS enables the analysis of active erupting volcanoes in near-infrared wavelengths. The results of this analysis find fluid lava temperatures of about 1100–1200 K but there are problems associated with the high-temperature lava spectral emissivity. Full article

Several satellite missions are currently available to provide thermal infrared data at different spatial resolutions and revisit time. Furthermore, new missions are planned thus enabling to keep a nearly continuous ‘eye’ on thermal volcanic activity around the world. This massive volume of data requires the development of artificial intelligence (AI) techniques for the automatic processing of satellite data in order to extract significant information about volcano conditions in a short time. Here, we propose a robust machine learning approach to accurately detect, recognize and quantify high-temperature volcanic features using Sentinel-2 MultiSpectral Instrument (S2-MSI) imagery. We use the entire archive of high spatial resolution satellite data containing more than 6000 S2-MSI scenes at ten different volcanoes around the world. Combining a ‘top-down’ cascading architecture, two different machine learning models, a scene classifier (SqueezeNet) and a pixel-based segmentation model (random forest), we achieved a very high accuracy, namely 95%. These results show that the cascading approach can be applied in near-real time to any available satellite image, providing a full description of the scene, with an important contribution to the monitoring, mapping and characterization of volcanic thermal features. Full article

A persistent stratospheric aerosol layer first appeared during July 2019 above Thessaloniki, Greece (40.5°N, 22.9°E). It was initially at 12 km and, during August 2019, was even up to 20 km, with increased thickness and reduced attenuated backscatter levels till the end of the year. In this study, we analyze the geometrical and optical properties of this stratospheric layer, using ground-based Lidar measurements, CALIOP/CALIPSO & OMPS-LP space-borne observations, as well as CAMS/ECMWF assimilation experiments. The main aim of the paper is to present an overview of this atmospheric feature and to identify any temporal changes in the aerosol properties that would signify substantial changes in the composition of this long-lasting stratospheric plume over Thessaloniki. This aim is further enhanced by emphasizing the importance of the combined information based on active ground- and space-borne lidars, passive remote sensing, and models during the complex stratospheric aerosol conditions as those encountered during 2019. The layer’s origin is linked to the Raikoke volcanic eruption in the Kuril Islands in June 2019, yielding a particle linear depolarization ratio less than 0.05, while some indications exist that the intense forest fires at mid and high northern latitudes throughout the summer of 2019 also contributed to the persistent layer. We report that in July, mainly volcanic sulphate aerosol layers with a 1–3 km vertical extent were identified in the stratosphere at ~15 km over Thessaloniki, while after August and until the end of 2019, the plume heights showed a significant month-to-month variability and a broadening (with thickness greater than 3 km) towards lower altitudes. The aerosol optical thickness was found to be in the range between 0.004 and 0.125 (visible) and 0.001 and 0.095 (infrared) and the particle depolarization of the detected stratospheric plume was found to be 0.03 ± 0.04, indicative of spherical particles, such as sulphate aerosols. Full article

The powerful explosive eruptions with large volumes of volcanic ash pose a great danger to the population and jet aircraft. Global experience in monitoring volcanoes and observing changes in the parameters of their thermal anomalies is successfully used to analyze the activity of volcanoes and predict their danger to the population. The Kamchatka Peninsula in Russia, with its 30 active volcanoes, is one of the most volcanically active regions in the world. The article considers the thermal activity in 2015–2022 of the Klyuchevskoy, Sheveluch, Bezymianny, and Karymsky volcanoes, whose rock composition varies from basaltic andesite to dacite. This study is based on the analysis of the Value of Temperature Difference between the thermal Anomaly and the Background (the VTDAB), obtained by manual processing of the AVHRR, MODIS, VIIRS, and MSU-MR satellite data in the VolSatView information system. Based on the VTDAB data, the following “background activity of the volcanoes” was determined: 20 °C for Sheveluch and Bezymianny, 12 °C for Klyuchevskoy, and 13–15 °C for Karymsky. This study showed that the highest temperature of the thermal anomaly corresponds to the juvenile magmatic material that arrived on the earth’s surface. The highest VTDAB is different for each volcano; it depends on the composition of the eruptive products produced by the volcano and on the character of an eruption. A joint analysis of the dynamics of the eruption of each volcano and changes in its thermal activity made it possible to determine the range of the VTDAB for different phases of a volcanic eruption. Full article

Changes in the emission rate of volcanic sulphur dioxide (SO${}_2$) are crucial parameters for identifying volcanic unrest and forecasting the eruptive activity. Ground-based ultraviolet (UV) remote sensing provides a near continuous record of the SO${}_2$ emission rate, with Differential Optical Absorption Spectroscopy (DOAS) being the preferred method for quantifying SO${}_2$ absorption from recorded spectra. However, retrieving accurate column amounts of SO${}_2$ using DOAS requires a complex fitting procedure that relies on user expertise for selecting suitable fit parameters and visually inspecting the fit results. We explore an alternative approach that exploits the well-defined spatial frequencies present in sky-scattered UV spectra. We use wavelet coherence to compare UV spectra recorded with calibration cells of known SO${}_2$ concentration in the wavelength–spatial frequency plane. Our findings reveal that the Magnitude-Squared Wavelet Coherence (MSWC) is inversely proportional to the SO${}_2$ concentration, suggesting that this relationship could be used to quantify volcanic SO${}_2$ in natural spectra. To validate this approach, we analyze UV spectra recorded by scanning-DOAS instruments from the Network of Volcanic and Atmospheric Change (NOVAC) at Masaya volcano, Nicaragua, and Soufrière Hills volcano, Montserrat. We observe a favourable comparison between the MSWC values we calculate and the slant column densities (SCDs) of SO${}_2$ obtained using the DOAS and iFit algorithms, respectively. We demonstrate the MSWC to be a robust indicator of SO${}_2$ which may potentially serve as a proxy for differential SCDs of volcanic SO${}_2$. The straightforward computation of the wavelet coherence between spectra offers an efficient means to identify spectra which contain the signature of the volcanic plume and an objective approach to validate results obtained using traditional fitting routines. Full article

The Wadi Samra–Wadi Kid district in southeastern Sinai, Egypt, has undergone extensive investigation involving remote sensing analysis, field geology studies, petrography, and geochemistry. The main aim of this study is the integration between remote sensing applications, fieldwork, and laboratory studies for accurate lithological mapping for future mineral exploration in the study region. The field relationships between these coincident rocks were studied in the study area. Landsat-8 (OLI) data that cover the investigated area were used in this paper. The different rock units in the study area were studied petrographically using a polarizing microscope, in addition to major and trace analysis using ICP-OES tools. The Operational Land Imager (OLI) images were used with several processing methods, such as false color composite (FCC), band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF) techniques for detecting the different types of rock units in the Wadi Kid district. This district mainly consists of a volcano-sedimentary sequence as well as diorite, gabbro, granite, and albitite. Geochemically, the metasediments are classified as pelitic graywackes derived from sedimentary origin (i.e., shales). The Al₂O₃ and CaO contents are medium–high, while the Fe₂O₃ and TiO₂ contents are very low. Alkaline minerals are relatively low–medium in content. All of the metasediment samples are characterized by high MgO contents and low SiO₂, Fe₂O₃, and CaO contents. The granitic rocks appear to have alkaline and subalkaline affinity, while the subalkaline granites are high-K calc-alkaline to shoshonite series. The alkaline rocks are classified as albitite, while the calc-alkaline series samples vary from monzodiorites to granites. The outcomes of this study can be used for prospecting metallic and industrial mineral exploration in the Wadi Kid district. Full article

Volcanic plume height is one the most important features of explosive activity; thus, it is a parameter of interest for volcanic monitoring that can be retrieved using different remote sensing techniques. Among them, calibrated visible cameras have demonstrated to be a promising alternative during daylight hours, mainly due to their low cost and low uncertainty in the results. However, currently these measurements are generally not fully automatic. In this paper, we present a new, interactive, open-source MATLAB tool, named ‘Plume Height Analyzer’ (PHA), which is able to analyze images and videos of explosive eruptions derived from visible cameras, with the objective of automatically identifying the temporal evolution of eruption columns. PHA is a self-customizing tool, i.e., before operational use, the user must perform an iterative calibration procedure based on the analysis of images of previous eruptions of the volcanic system of interest, under different eruptive, atmospheric and illumination conditions. The images used for the calibration step allow the computation of ad hoc expressions to set the model parameters used to recognize the volcanic plume in new images, which are controlled by their individual characteristics. Thereby, the number of frames used in the calibration procedure will control the goodness of the model to analyze new videos/images and the range of eruption, atmospheric, and illumination conditions for which the program will return reliable results. This also allows improvement of the performance of the program as new data become available for the calibration, for which PHA includes ad hoc routines. PHA has been tested on a wide set of videos from recent explosive activity at Mt. Etna, in Italy, and may represent a first approximation toward a real-time analysis of column height using visible cameras on erupting volcanoes. Full article

Satellite data provide crucial information to better understand volcanic processes and mitigate associated risks. In recent years, exploiting the growing number of spaceborne polar platforms, several automated volcanic monitoring systems have been developed. These, however, rely on good geometrical and meteorological conditions, as well as on the occurrence of thermally detectable activity at the time of acquisition. A multiplatform approach can thus increase the number of volcanological-suitable scenes, minimise the temporal gap between acquisitions, and provide crucial information on the onset, evolution, and conclusion of both transient and long-lasting volcanic episodes. In this work, we assessed the capabilities of the MEdium Resolution Spectral Imager-II (MERSI-II) sensor aboard the Fengyun-3D (FY-3D) platform to detect and quantify heat flux sourced from volcanic activity. Using the Middle Infrared Observation of Volcanic Activity (MIROVA) algorithm, we processed 3117 MERSI-II scenes of Mount Etna acquired between January 2020 and February 2023. We then compared the Volcanic Radiative Power (VRP, in Watt) timeseries against those obtained by MODIS and VIIRS sensors. The remarkable agreement between the timeseries, both in trends and magnitudes, was corroborated by correlation coefficients (ρ) between 0.93 and 0.95 and coefficients of determination (R²) ranging from 0.79 to 0.84. Integrating the datasets of the three sensors, we examined the effusive eruption of Mount Etna started on 27 November 2022, and estimated a total volume of erupted lava of 8.15 ± 2.44 × 10⁶ m³ with a Mean Output Rate (MOR) of 1.35 ± 0.40 m³ s⁻¹. The reduced temporal gaps between acquisitions revealed that rapid variations in cloud coverage as well as geometrically unfavourable conditions play a major role in thermal volcano monitoring. Evaluating the capabilities of MERSI-II, we also highlight how a multiplatform approach is essential to enhance the efficiency of satellite-based systems for volcanic surveillance. Full article