The characteristics and extent of forest damage and subsequent patterns of recovery reflect the intensity of an explosive volcanic eruption and have the potential to be a novel proxy for eruption magnitude and impact. Using vegetation damage and recovery patterns following the 2015 explosive eruption of Calbuco, Chile, we develop a satellite-based approach to assess impact on surrounding temperate forests. The Calbuco eruption resulted in tephra deposition over 100skm2, pyroclastic flows extending 6km and lahars extending 15km. We explore NDVI derived from optical imagery (June 2013 - May 2023) as well as radar backscatter and phase coherence (October 2014 - June 2023) through time series analysis, clustering and estimation of recovery timescales to find patterns in forest disturbance and recovery. We find that forest damage and recovery correspond primarily with deposit type, thickness and dispersal directions. The thickest tephra deposits (> 40cm) correlate with the most vegetation loss, so our vegetation impact maps allow us to refine the spatial mapping of tephra fall-deposit isopachs to give a revised eruption volume of 0.28km3. Vegetation recovery rates relate to initial impact intensity, but also local topography, aspect and altitude. Our results demonstrate the potential of vegetation disturbance as a novel proxy that can be used to determine eruption extents and magnitudes, especially in remote and densely vegetated environments, and to refine field-based analyses in inaccessible or intensely damaged zones.

There are now more interferograms being generated from global satellite radar datasets than can be assessed by hand. The reliable, automatic detection of true displacement from these data is therefore critical, both for monitoring deformation related to geohazards and understanding solid earth processes. We discuss improvements to an unsupervised, event agnostic method for automatically detecting deformation in unwrapped interferograms. We use an anomaly detection framework that recognises any deformation as “anomalies” by learning the ‘typical’ spatio-temporal pattern of atmospheric and other noise in sequences of interferograms. Here, we present developments to our prototype model, ALADDIn (Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR) using (1) a self-attention training technique to exploit redundancy in interferogram networks to capture the temporal structure of signals and (2) the addition of synthetic data for training. We evaluate the impact of these developments using two geophysical scenarios: (1) the detection of the same M_w 5.7 earthquake used to test our original model (20.03.2019, south-west Turkey), (2) the persistent uplift of Domoyu volcano (17.05.2017 to 14.12.2018, Argentina). We make a quantitative evaluation of the performance of our method using synthetic test data and find that for peak displacements exceeding a few cm and of length-scale greater than a few hundred metres, overall detection accuracy is 80 to 90%.

We report the first detection of unrest at Socompa, Northern Chile, a stratovolcano which has recorded no eruptions since ~7,200 years ago. We measure deformation at and around Socompa using Interferometric Synthetic Aperture Radar (InSAR) observations between Jan 2018 and Oct 2021. We find that, whilst initially inactive, Socompa shows a steady uplift (17.5 mm/yr) from Dec 2019, independently recorded by near-field continuous Global Positioning System (GPS) data. The data can be fit with pressure increase in an ellipsoidal source region stretching from 1.9 to 9.5 km, with a volume change rate of ~5.8×106 m3/yr. Our observations of the onset of uplift preclude the possibility that a nearby Mw 6.8 deep intraslab earthquake on 3rd June 2020 triggered the unrest. The deformation signal we detect indicates the initiation of unrest at Socompa, after at least two decades without measurable deformation, and many thousands of years without volcanic activity.

Satellite radar backscatter has the potential to provide useful information about the progression of volcanic eruptions when optical, ground-based, or radar phase-based measurements are limited. However, backscatter changes are complex and challenging to interpret: explosive deposits produce different signals depending on pre-existing ground cover, radar parameters and eruption characteristics. We use high temporal- and spatial-resolution backscatter imagery to examine the emplacement and alteration of pyroclastic flows, lahars, and ash from the June 2018 eruption of Volcan de Fuego, Guatemala, drawing on observatory reports and rain gauge data to ground truth our observations. We use dense timeseries of backscatter to reduce noise and extract deposit areas. Backscatter decreases where six flows were emplaced on 3 June 2018. In Barranca Las Lajas, we measured a 11.9-km-long flow that altered an area of 6.3 km2; and used radar shadows to estimate a thickness of 10.5 +/- 2 m in the lower sections. The 3 June eruption also changed backscatter over an area of 40 km2, consistent with ashfall. We use transient patterns in backscatter timeseries to identify nine periods of high lahar activity in B. Las Lajas between June and October 2018. We find that the characterisation of subtle backscatter signals associated with explosive eruptions is assisted by (1) radiometric terrain calibration, (2) speckle correction, and (3) consideration of pre-existing scattering properties. Our observations demonstrate that SAR backscatter can capture both the emplacement and subsequent alteration of a range of explosive products, allowing the progression of an explosive eruption to be monitored.

Determining the spatial relations between volcanic edifices and their underlying magma storage zones is fundamental for characterizing long-term evolution and short-term unrest. We compile centroid locations of upper crustal magma reservoirs at 56 arc volcanoes inferred from seismic, magnetotelluric, and geodetic studies. We show that magma reservoirs are often horizontally offset from their associated volcanic edifices by multiple kilometers, and the degree of offset broadly scales with reservoir depth. Approximately 20% of inferred magma reservoir centroids occur outside of the overlying volcano’s mean radius. Furthermore, reservoir offset is inversely correlated with edifice size. Taking edifice volume as a proxy for long-term magmatic flux, we suggest that high flux or prolonged magmatism leads to more centralized magma storage beneath arc volcanoes by overprinting upper crustal heterogeneities that would otherwise affect magma ascent. Edifice volumes therefore reflect the spatial distribution of underlying magma storage, which could help guide monitoring strategies at volcanoes.