Reworking of limestone (CaCO3) by magma is an important source of carbon in volcanic arc emissions. However, while it is broadly understood that CO2 is liberated during magma-limestone interaction, the degassing behaviour of calcite in silicate melts is less well constrained. In this study, we carried out microspectroscopic analysis of volatiles within fluid inclusions and glass (former melt) in the products of short-term experiments simulating limestone assimilation in mafic arc melt (T = 1200 degrees C, P = 0.5 GPa, runtimes of 0 to 300 s). The experimental products consist of partly to wholly assimilated limestone xenoliths enveloped by CaO-rich silicate glass (reacting melt) that grades into mafic glass (host melt). Micro-to milli-metric sized fluid-filled bubbles permeate the experimental products. This study reveals that limestone assimilation induces extremely fast apparent diffusivity of CO2 (DCO2 greater than or similar to 10-7 m2/s) through both the reacting melt and the host melt. Volatile saturation is thus quickly reached, triggering nucleation of bubbles mainly containing CO2 +/- CO, CH4, N2, H2, and H2O. Crucially, we find that the host melt contains dissolved CO2 from limestone, despite showing no other compositional evidence for limestone assimilation. Mafic melts in volcanic regions underlain by limestone may therefore mobilise and transport more carbon than previously thought, with implications for eruptive behaviour, volcanic CO2 inventories, and long-term climate warming.

Increased volcanic activity on inhabited volcanic islands requires a rapid volcanic-hazards assessment to reduce potential casualties and losses. Karangetang Volcano (2.78 degrees N, 125.406 E), located on Siau Island, poses significant threats to approximately 70,000 residents due to its increased activity over the last decade. In this study, we assessed volcanic hazards of Karangetang Volcano by integrating satellite imageries from Landsat 8 and 9, Sentinel-1 SLC, Sentinel-2, and Sentinel-5 TROPOMI. These satellites have been used to analyse land surface temperature (LST), thermal areas, volcano deformation, and SO2 over the last decade. The results highlighted reactivation of two craters (southern and northern) that influenced the directions of both lava and pyroclastic flows from the north, west, southwest, to the south flank area during the 2015, 2019, and 2023 eruption crises. The highest thermal anomalies were recorded in August 2015, November 2019, March 2023, and August 2023, with a maximum land surface temperature of-103.29 degrees C. Furthermore, cumulative displacement indicated that Karangetang experienced long-term deflation, possibly caused by the cooling of lava and the solidification of pyroclastic deposits on the western part of the Karangetang summit. Observations of SO2 emissions indicated that Karangetang experienced low degassing, with a maximum SO2 mass of 1.832 kt. Based on multi-parameter satellite observations, Karangetang has been highly active over the last decade, and mitigation preparedness should be conducted in the north, west, southwest, and south flank areas.

Anak Krakatau in the Sunda Strait is acknowledged as one of the most dangerous active volcanoes in Indonesia, not only because of its frequent explosive eruptive activities and its position in the middle of the ocean, but also due to its proximity to dense urbanization areas (e.g., Bandar Lampung, Banten). This study investigates the syn- and post-eruptive products of Anak Krakatau following the catastrophic sector collapse on 22nd December 2018 (which killed similar to 437 people) to better understand the magmatic origin, eruption types, syn-eruptive vesiculation history, and potential hazards associated with the recent activity of Anak Krakatau. The study focuses on the uppermost ten tephra layers (AKL 1-AKL 10) and employs multiple analytical techniques, including grain size distribution, componentry analysis, ash morphology, vesicle textures, and whole-rock and groundmass glass chemical compositions. Field observations and grain size distribution analysis indicate that AKL 3 represents a pyroclastic fall deposit dominated by scoria, whereas AKL 7 and AKL 9 correspond to pyroclastic density currents primarily composed of lithic fragments. The grain morphology of AKL 3, 7, and 9 suggests a predominantly magmatic origin, while AKL 2 exhibits characteristics of a wet phreatic eruption. The identified magmatic eruption styles range from violent Strombolian to Vulcanian, with estimated decompression rates (obtained from vesicle number density) of 0.07-0.41 MPa/s. Whole-rock geochemical analysis of AKL 2-9 reveals a SiO2 content of 55.3-55.8 wt%, with volcanic glass compositions ranging from 60.6 to 61.7 wt%, consistent with the differentiation trends of historical eruptions from the Krakatau volcanic complex. AKL 1, a thin white surface coating found at the summit, consists primarily of Na and Cl, indicative of magma interaction with saline water. The characteristics of AKL 1 and AKL 2 suggest an influence of seawater interaction within Anak Krakatau's magmatic system. These findings provide critical insights for understanding the dynamics and possible future eruption styles at Anak Krakatau, which are crucial for advancing the monitoring system and hazard assessment of this volcano.

Magma-limestone interaction is thought to be an important source of carbon in volcanic arc emissions (1). To better understand the production of volatiles and their behaviour in silicate melts during magma-limestone interaction, we performed Raman and FTIR spectroscopic analysis of bubbles and glasses in the products of a time-series of high pressure-temperature experiments (2). The experiments were designed to simulate entrainment and assimilation of limestone (CaCO3) xenoliths in mafic magma using starting materials from an iconic example of a limestone-hosted arc volcano (Mt. Merapi, Sunda arc, Indonesia) (3). The experimental conditions were T = 1200 °C and P = 0.5 GPa, with run-times ranging from t = 0 s to t = 300 s. Our shortest run-time experiment (t = 0 s) reveals formation of CO2-rich bubbles (± C, CO, N2, H2, H2O, CH4) in and around the magma-limestone reaction site and fast diffusion of CO32- and CO2 molecules throughout the host melt (qualitatively faster than Ca diffusion). Longer run-time experiments (up to t = 300 s) show that bubbles evolved to become larger and richer in CO2 close to the reaction site and that they grew by extracting CO2 from the surrounding melt. Magma-limestone interaction thus rapidly mobilizes CO32- andCO2 and promotes formation of compositionally evolving CO2-rich fluids, which could migrate along fractures, faults, or other fluid escape pathways to contribute to atmospheric fluxes of CO2 at volcanic arcs. References(1) Mason E., Edmonds M., Turchyn AV (2017) Remobilization of crustal carbon may dominate volcanic arc emissions. Science 357, 290-294, doi: 10.1126/science.aan5049(2) Deegan FM, Troll VR, Freda C, Misiti V, Chadwick JP, McLeod CL, Davidson JP (2010) Magma-carbonate interaction processes and associated CO2 release at Merapi volcano, Indonesia: Insights from experimental petrology. Journal of Petrology 51, 1027-1051, doi:10.1093/petrology/egq010(3) Deegan FM, Troll VR, Gertisser R, Freda C (2023) Magma-carbonate interaction at Merapi volcano. In: Gertisser R., Troll VR, Walter T, Agung Nandaka IGM, Ratdomopurbo A (Eds.) Merapi volcano: Geology, eruptive activity, and monitoring of a high-risk volcano (Volcanoes of the World Book Series). Springer Verlag, Berlin, Heidelberg, New York. Chapter 10, 291-321, doi:10.1007/978-3-031-15040-1_

Catastrophic lava dome collapse is considered an unpredictable volcanic hazard because the physical properties, stress conditions, and internal structure of lava domes are not well understood. To better explain the locations of recent dome instability events at Merapi volcano, Indonesia (1), we combined geochemical and mineralogical analyses, rock physical property measurements, drone-based photogrammetry, and numerical modelling. We show that a linear fissure and a horseshoe-shaped alteration zone that formed in 2014 was buried by lava extrusion in 2018. The linear fissure controlled the location of the new lava dome, while the horseshoe shaped zone influenced subsequent instability. Geomechanical, mineralogical, and geochemical data suggest that such alteration zones are characterised by mechanically weak, hydrothermally altered materials, and we show that the new lava dome is collapsing along this now-hidden horseshoe shaped and comparatively weak alteration zone (2). To derive an improved general understanding of this phenomenon, we then combined recent laboratory data for the mechanical behaviour of dome rocks with discrete element method models to show that the presence of weak zones within lava domes increases instability, which is exacerbated when the size of the zone increases or when the zone is positioned off-centre (3). Our results highlight that improved understanding of dome architecture and compositional variations due to hydrothermal alteration within domes is essential for assessing hazards associated with dome and edifice failure at volcanoes worldwide.

Long-term deformation is observed at many volcanoes worldwide, providing valuable insights into sub-volcanic processes. Deformation also informs on volcano flank instability, which presents a major hazard in the event of a complete or partial collapse of the edifice, which may further trigger a tsunami if the volcano is located near the sea. We explore InSAR datasets to investigate surface deformation of 20 potentially hazardous coastal volcanoes in Southeast Asia. We find that over 90% of them exhibit signs of persistent or episodic surface deformation. Most volcanoes experience line-of-sight (LOS) increase with displacement rates spanning a broad range and reaching up to 29 cm/yr at Ruang. These are either steady, or experience distinct acceleration periods, lasting for several months to years following increased volcanic activity measured as the volcanic radiative power (VRP) and reported periods of unrest or eruptions. We attribute the majority of observed deformation to gravity-driven processes and cooling of young surface deposits. Analysis of displacement components shows subsidence for all cases of LOS increase, coupled with varying horizontal displacements showing either (i) convergence, representing inwards displacements of the flanks due to volume loss by gravitational compaction and cooling contraction, (ii) divergence, representing outwards spreading due to instability of the volcano flanks via surficial downslope flank creep or fault sliding or (iii) near-unilateral horizontal displacements across most of the edifice, representing sliding via a deep detachment fault. We suggest that the horizontal component of InSAR deformation on volcanic edifices may be used to quickly assess the dominant deformation patterns. Applying this concept, we identify potential flank instability at four volcanoes (Anak Krakatau, Lewotobi, Sirung and Ulawun), which may pose future collapse hazards. This work offers new insights into the types and rates of volcano flank deformation, demonstrates a direct link between increased volcanic activity levels and deformation rates, and provides an improved comparative basis to other volcanic regions worldwide.

Landslides are geological disasters characterized by the movement of soil or rock masses down a slope due to geotechnical instability and environmental factors. They significantly impact infrastructure, life safety, ecosystems, and local economies. Therefore, accurate and reliable predictions are essential in supporting disaster risk mitigation. This research integrates the Geoscientific Data Mining approach with hyperparameter optimization on the Gradient Boosting Machine (GBM) algorithm to improve the accuracy and stability of landslide hazard prediction. Thirteen geospatial parameters are used as predictor variables, including aspect, elevation, slope, curvature, plan curvature, profile curvature, precipitation, lithology, NDVI, NDWI, flow accumulation, and earthquake. GBM is selected for its ability to model non-linear relationships and complex interactions between features, even when there is no explicit linear correlation between causal variables. Through a decision tree-based ensemble learning approach, GBM builds predictive models incrementally, which enables learning of hidden patterns in heterogeneous geoscience data. The optimization process is performed using Random, Manual and Grid Search Cross-Validation to obtain the best combination of parameters, focusing on the balance between the accuracy and stability of the predictive model. The research results improved the accuracy from 55% to 80%. Evaluation using precision, recall, and f1-score indicated more accurate and stable model performance. In addition, the confusion matrix provides good classification information, while the ROC Curve provides a high AUC value. The cross-validation score plotting shows the stability and generalization ability of the prediction model. This research proves that integrating geospatial data mining and optimized GBM can be an effective approach to supporting mitigation systems and development planning in landslide-vulnerable areas.

Series of hydrothermal events and a large phreatomagmatic which shortly followed by an effusive eruption has changed the morphology of Anak Krakatau between 2019 and 2020. The large phreatomagmatic and effusive events that occurred on April, 10th 2020, produced tephra and lava flow deposit that enlarged the west flank area. Here, we investigated the morphological changes of Anak Krakatau between August 2019 and May 2020 using UAV SfM photogrammetry, Sentinel and Pleiades satellite imageries, and fieldworks photographs data. Our UAV orthomosaic image captured the morphology of Anak Krakatau in August 2019, while temporal Sentinel 2 and High-resolution Pleiades satellite images observed the morphology of Anak Krakatau between September 2019 and May 2020. We manually digitized the edifice of Anak Krakatau from UAV, sentinel 2, and Pleiades images to investigate morphological changes of Anak Krakatau. The high-resolution Pleiades satellite image was processed using supervised classification to automatically delineate the deposit of lava flow, altered rocks and tephra. Result shows volcaniclastic deposit due to the hydrothermal and/or phreatomagmatic eruptions that covered 0.08 km2 around an active crater lake at Anak Krakatau between January and February 2020. The large phreatomagmatic and effusive eruptions produced tephra that covered 0.815 km2 at the north–north west flanks of Anak Krakatau and lava flow that emplaced 0.2 km2 and elongated around 742 m from the pre-existing crater lake to the west shoreline of Anak Krakatau. The lava flow has blocky surface and highly fractured that possibly formed due to compression—extension stresses during lava flow emplacement and has widened the Anak Krakatau volcanic island from 2.99 km2 to 3.027 km2. We infer that Anak Krakatau is currently on reconstruction phase after the destructive flank collapse on December 22th, 2018.

The interaction between the Indo-Australian plate and the Eurasian plate exerts significant influence on seismic activities within the southern seas of Java Island, with potential repercussions extending to the triggering of tsunamis. Given the densely populated nature of this area, especially along the southern region of Yogyakarta Province, the coast of Samas Beach and its surroundings, mitigation efforts are needed to reduce the potential loss of life caused by tsunamis. One of the mitigation efforts is making a tsunami model which can be done using the help of DEMNAS and DEM topographical data from unmanned aerial vehicle (UAV) photogrammetry. The COMCOT software is a tool used in modeling tsunamis based on a numerical model of the shallow water equation that processes tsunami generator parameters and DEM data into an accurate tsunami model. The modeling results show that the tsunami waves will reach the Samas coast in the 38th minute after the occurrence of the earthquake. The maximum height of the tsunami inundation obtained using DEMNAS data was 21.72 m while using the UAV-DEM it was obtained 23.34 m. Comparison of modeling using DEMNAS and UAV-DEM data shows that image data collection using UAV has good resolution and has high accuracy so that it is able to produce a tsunami model that better shows the propagation of a tsunami in the actual field. The location used as a temporary/final evacuation site is Tegalsari Elementary School because of its strategic location and in tsunami modeling, this location is in the very low risk zone.

Anak Krakatau coastal area was used as a research location because it has unique characteristics, i.e., a small volcanic island with an active volcano. This study aims to analyze the grain size and sedimentation process in the Anak Krakatau coastal area. A total of 24 samples, each consisting of 8 sediment samples on land, beaches, and seabed surfaces, were collected purposively. Granulometric analysis was performed using statistical methods with Gradistat. Descriptive analysis, Linear Discriminant Analysis (LDA), and Stewart and Passega's diagrams were conducted to compare differences in grain size of sediments on land, beach, and seabed surfaces, as well as the factors that can influence them. Our results demonstrated that the mean value indicated a predominance of very coarse particles, suggesting high-energy conditions during sediment deposition. Samples from the land exhibited the largest mean size (Mz-3.627 or medium gravel), while samples from the seabed surface had the smallest mean size (Mz0.692 or coarse sand), implying greater energy on the land compared to the sea. LDA plots confirmed that sediments from the land and shore were deposited in a fluvial environment, whereas samples from the seabed represented shallow ocean deposits. Additionally, Stewart and Passega's diagrams show that seabed surface samples had been deposited through rolling action caused by waves.

Volcano slope stability analysis is a critical component of volcanic hazard assessments and monitoring. However, traditional methods for assessing rock strength require physical samples of rock which may be difficult to obtain or characterize in bulk. Here, visible to shortwave infrared (350–2500 nm; VNIR–SWIR) reflected light spectroscopy on laboratory-tested rock samples from Ruapehu, Ohakuri, Whakaari, and Banks Peninsula (New Zealand), Merapi (Indonesia), Chaos Crags (USA), Styrian Basin (Austria) and La Soufrière de Guadeloupe (Eastern Caribbean) volcanoes was used to design a novel rapid chemometric-based method to estimate uniaxial compressive strength (UCS) and porosity. Our Partial Least Squares Regression models return moderate accuracies for both UCS and porosity, with R2 of 0.43–0.49 and Mean Absolute Percentage Error (MAPE) of 0.2–0.4. When laboratory-measured porosity is included with spectral data, UCS prediction reaches an R2 of 0.82 and MAPE of 0.11. Our models highlight that the observed changes in the UCS are coupled with subtle mineralogical changes due to hydrothermal alteration at wavelengths of 360–438, 532–597, 1405–1455, 2179–2272, 2332–2386, and 2460–2490 nm. These mineralogical changes include mineral replacement, precipitation hydrothermal alteration processes which impact the strength of volcanic rocks, such as mineral replacement, precipitation, and/or silicification. Our approach highlights that spectroscopy can provide a first order assessment of rock strength and/or porosity or be used to complement laboratory porosity-based predictive models. VNIR-SWIR spectroscopy therefore provides an accurate non-destructive way of assessing rock strength and alteration mineralogy, even from remote sensing platforms.

Volcano flank collapses have been documented at ocean islands worldwide and are capable of triggering devastating tsunamis, but little is known about the precursory processes and deformation changes prior to flank failure. This makes the 22 December 2018 flank collapse at Anak Krakatau in Indonesia a key event in geosciences. Here, we provide direct insight into the precursory processes of the final collapse. We analyzed interferometric synthetic aperture radar (InSAR) data from 2014 to 2018 and studied the link between the deformation trend and intrusion occurrence through analogue modeling. We found that the flank was already moving at least 4 yr prior to collapse, consistent with slow décollement slip. Movement rates averaged ~27 cm/yr, but they underwent two accelerations coinciding with distinct intrusion events in January/February 2017 and in June 2018. Analogue models suggest that these accelerations occurred by (re)activation of a décollement fault linked to a short episode of magma intrusion. During intrusion, we observed a change in the internal faults, where the outward-directed décollement accelerated while inward faults became partially blocked. These observations suggest that unstable oceanic flanks do not disintegrate abruptly, but their collapse is preceded by observable deformations that can be accelerated by new intrusions.

Sileri crater is one of the prominent craters in Dieng Volcanic Complex. It is one of the craters in the area with the highest activity of phreatic eruption. This type of eruption is characterized by a minimal to no precursor beforehand, making mitigation more challenging. Previously, there is minimum number of research which tries to understand the subsurface properties of the crater. Therefore, in this research, we integrate two methodologies to understand Sileri crater’s characteristics better. An Unmanned Aerial Vehicle (UAV) survey was conducted in parallel with the surface seismic survey. The UAV data was then processed, yielding a Digital Elevation Model (DEM) and orthophoto for surface analysis. The ground seismic survey on the east flank of the crater was processed to obtain the gradient with respect to the misfit function. The obtained gradient information can be used to interpret the tendency of the subsurface velocity model. Through this process, a vertical shape positive gradient anomaly is detected at the crater’s northeast flank, which may correspond to the hot spring activity. The constant positive gradient anomaly layer throughout the model, which corresponds to the alteration zone, is also detected. Another 10 m depth positive anomaly is also found in the model’s southeast part, which corresponds with the new potential geothermal activity. The location of the anomalies in our investigation indicating a continuous tendency in the east which correspond to the possible eastward expansion of the crater. This finding is important to better monitor and mitigate the future activities and eruption of the Sileri crater.

Episodic growth and collapse of the lava dome of Merapi volcano is accompanied by significant hazards associated with material redeposition processes. Some of these hazards are preceded by over-steepening of the flanks of the dome, its destabilisation, fracturing and gravitational collapse, producing lethal pyroclastic density currents. With the emergence of unoccupied aircraft systems (UAS), these changes occurring high up at Merapi can now be monitored at unprecedented levels of detail. Here we summarise the use of UAS at Merapi to better understand the evolution of the lava dome following the 2010 eruption. Systematic UAS overflights and photogrammetric surveys were carried out in 2012, 2015, 2017, 2018 and 2019, allowing identification of the progression of major structures and a three-stage morphological evolution of the dome. We first highlight the significant morphological changes associated with steam-driven explosions that occurred in the period 2012-2014. A large open fissure formed and split the dome into two parts. In the years 2014-2018, hydrothermal activity dominated and progressively altered the dome rock. Lastly, in May-June 2018, a series of steam-driven explosions occurred and was followed by dome extrusion in August 2018, initially refilling the formerly open fissure. This work demonstrates the importance of reactivating pre-existing structures, and summarises the unique contribution realised by high resolution photogrammetric UAS surveys.

Spatial approaches, based on the deformation measurement of volcanic domes and crater rims, is key in evaluating the activity of a volcano, such as Merapi Volcano, where associated disaster risk regularly takes lives. Within this framework, this study aims to detect localized topographic change in the summit area that has occurred concomitantly with the dome growth and explosion reported. The methodology was focused on two sets of data, one LiDAR-based dataset from 2012 and one UAV dataset from 2014. The results show that during the period 2012–2014, the crater walls were 100–120 m above the crater floor at its maximum (from the north to the east–southeast sector), while the west and north sectors present a topographic range of 40–80 m. During the period 2012–2014, the evolution of the crater rim around the dome was generally stable (no large collapse). The opening of a new vent on the surface of the dome has displaced an equivalent volume of 2.04 × 104 m3, corresponding to a maximum −9 m (+/−0.9 m) vertically. The exploded material has partly fallen within the crater, increasing the accumulated loose material while leaving “hollows” where the vents are located, although the potential presence of debris inside these vents made it difficult to determine the exact size of these openings. Despite a measure of the error from the two DEMs, adding a previously published dataset shows further discrepancies, suggesting that there is also a technical need to develop point-cloud technologies for active volcanic craters.

Tsunamis caused by large volcanic eruptions and flanks collapsing into the sea are major hazards for nearby coastal regions. They often occur with little precursory activity and are thus challenging to detect in a timely manner. This makes the pre-emptive identification of volcanoes prone to causing tsunamis particularly important, as it allows for better hazard assessment and denser monitoring in these areas. Here, we present a catalogue of potentially tsunamigenic volcanoes in Southeast Asia and rank these volcanoes by their tsunami hazard. The ranking is based on a multicriteria decision analysis (MCDA) composed of five individually weighted factors impacting flank stability and tsunami hazard. The data are sourced from geological databases, remote sensing data, historical volcano-induced tsunami records, and our topographic analyses, mainly considering the eruptive and tsunami history, elevation relative to the distance from the sea, flank steepness, hydrothermal alteration, and vegetation coverage. Out of 131 analysed volcanoes, we found 19 with particularly high tsunamigenic hazard potential in Indonesia (Anak Krakatau, Batu Tara, Iliwerung, Gamalama, Sangeang Api, Karangetang, Sirung, Wetar, Nila, Ruang, Serua) and Papua New Guinea (Kadovar, Ritter Island, Rabaul, Manam, Langila, Ulawun, Bam) but also in the Philippines (Didicas). While some of these volcanoes, such as Anak Krakatau, are well known for their deadly tsunamis, many others on this list are lesser known and monitored. We further performed tsunami travel time modelling on these high-hazard volcanoes, which indicates that future events could affect large coastal areas in a short time. This highlights the importance of individual tsunami hazard assessment for these volcanoes, the importance of dedicated volcanological monitoring, and the need for increased preparedness on the potentially affected coasts.

Catastrophic lava dome collapse is considered an unpredictable volcanic hazard because the physical properties, stress conditions, and internal structure of lava domes are not well understood and can change rapidly through time. To explain the locations of dome instabilities at Merapi volcano, Indonesia, we combined geochemical and mineralogical analyses, rock physical property measurements, drone-based photogrammetry, and geoinformatics. We show that a horseshoe-shaped alteration zone that formed in 2014 was subsequently buried by renewed lava extrusion in 2018. Drone data, as well as geomechanical, mineralogical, and oxygen isotope data suggest that this zone is characterized by high-porosity hydrothermally altered materials that are mechanically weak. We additionally show that the new lava dome is currently collapsing along this now-hidden weak alteration zone, highlighting that a detailed understanding of dome architecture, made possible using the monitoring techniques employed here, is essential for assessing hazards associated with dome and edifice failure at volcanoes worldwide.

Volcanoes are subject to intense fluid circulation, altering primary rock properties. The rock alteration can be due to “cold” water-driven weathering or “hot” hydrothermal fluids. Such alteration is facilitated by the efficient circulation of fluids through fractures and the connected pore-network. Fluid-driven alteration can manifest as mineral dissolution and precipitation, ultimately changing the properties of the host rock, such as strength and elasticity. Alteration-induced geomechanical changes are complex due to the large range of protolith porosity (e.g. 0.01-0.8) and permeability (e.g. 10-10 to ≤10-18 m2). For example, fresh pyroclastic rocks can initially have low strength, which can be ‘reinforced’ by mineral precipitation. In contrast, dense lava rocks can decrease their strength due to pore space enlargement and development of secondary clays, oxides, sulfides and sulfates. This study analysed lab-tested samples from Ruapehu, Merapi, Whakaari, Chaos Crags, Ohakuri, Styrian Basin and La Soufrière de Guadeloupe volcanoes to provide new insights into the controls on geomechanical properties due to weathering and hydrothermal alteration. The volcaniclastic and lava rocks range from basaltic to rhyolitic in composition and encompass surface weathering and intermediate and advanced argillic alteration styles. The physical, geomechanical, Visible-Near Infrared (VNIR; 350-1000 nm), and Shortwave Infrared (SWIR; 1000-2500 nm) properties of the rocks were measured on core samples. Porosity, P-wave velocity, and uniaxial compressive strength ranged from 0.02-0.67, 88-5800 m/s, and 0.1-312 MPa, respectively. Partial Least Squares Regression (PLSR) was employed to successfully predict physical and mechanical properties using VNIR-SWIR spectroscopy data. The PLSR-based prediction models highlighted a handful of spectral bands around 400-600 nm, 1400 nm and 2200-2300 nm, indicating that hydrated secondary minerals were responsible for the observed geomechanical changes. The proposed method using VNIR-SWIR spectroscopy can lead to a new way of mapping physical and geomechanical properties at outcrop-scale using field spectrometers, and at volcano-scale using airborne and satellite remote sensing.