The observation of volcanic thermal activity from space dates back to the late 1960s. Several methods have been proposed to improve detection and monitoring capabilities of thermal volcanic features, and to characterize them to improve our understanding of volcanic processes, as well as to inform operational decisions. In this paper we review the RSTVOLC algorithm, which has been designed and implemented for automated detection and near-real-time monitoring of volcanic hotspots. The algorithm is based on the general Robust Satellite Techniques (RST) approach, representing an original strategy for satellite data analysis in the space–time domain. It has proven to be a useful tool for investigating volcanoes worldwide, by means of different satellite sensors, onboard polar orbiting and geostationary platforms. The RSTVOLC rationale, its requirements and main operational capabilities are described here, together with the advantages of the tool and the known limitations. Results achieved through the study of two past eruptive events are shown, together with some recent examples demonstrating the near-continuous monitoring capability offered by RSTVOLC. A summary is also made of the type products that the method is able to generate and provide. Lastly, the future perspectives, in terms of its possible implementation on the new generation of satellite systems, are briefly discussed.

The AVHotRR routine has been in operation since 2006 to process satellite data for monitoring active volcanoes in the Mediterranean area. Although originally developed to work with advanced very high-resolution radiometer (AVHRR) data, AVHotRR has been developed over the years to adapt to other sensors. In this work we present an improved version of the algorithm for hot-spot detection and effusion rate estimate. The underlying principles upon which the algorithm is based are discussed, focusing on the enhancements. The currently implemented version makes it possible to integrate results from different datasets in order to better constrain the detection of volcanic hot spots. In particular, the high temporal resolution of the SEVIRI instrument aboard MSG is key to reducing false positives in AVHRR and moderate resolution imaging spectroradiometer MODIS images. We propose here a new detection method based on the wavelet transform of SEVIRI data. Results from the application of AVHotRR to a dataset of AVHRR and SEVIRI images from Mt Etna, Italy, are presented and discussed with reference to the advantages and limitations of the algorithm.

Thermal observations of volcanic activity when the volcano is partially covered by clouds or observed under a wide-scan angle are often removed from further analyses. In the event of a volcanic crisis, such a reduced set of data is not adequate. Even when the observation conditions are favourable, the full observation set is still required to provide decision-makers with quality information about the data. Automatic quality estimation and outlier detection was not estimated in the past. We propose to analytically define the uncertainty for individual observations based on the measurement circumstances. To additionally reduce the temporal noise of the radiant power (RP) time series we apply a Kalman Filter (KF). The KF is able to recursively analyse an unevenly sampled time series. Based on some proposed rules, it can also detect outliers. We apply the proposed methodology to the 2008–09 Etna eruption monitored by MODIS (Moderate Resolution Imaging Spectroradiometer). The analysis of the results shows that the topography has a greater influence on RP than previously considered.

The Volcano Sensor Web (VSW) is a globe-spanning net of sensors and applications for detecting volcanic activity. Alerts from the VSW are used to trigger observations from space using the Earth Observing-1 (EO-1) spacecraft. Onboard EO-1 is the Autonomous Sciencecraft Experiment (ASE) advanced autonomy software. Using ASE has streamlined spacecraft operations and has enabled the rapid delivery of high-level products to end-users. The entire process, from initial alert to product delivery, is autonomous. This facility is of great value as a rapid response is vital during a volcanic crisis. ASE consists of three parts: (1) Science Data Classifiers, which process EO-1 Hyperion data to identify anomalous thermal signals; (2) a Spacecraft Command Language; and (3) the Continuous Activity Scheduling Planning Execution and Replanning (CASPER) software that plans and replans activities, including downlinks, based on available resources and operational constraints. For each eruption detected, thermal emission maps and estimates of eruption parameters are posted to a website at the Jet Propulsion Laboratory, California Institute of Technology, in Pasadena, CA. Selected products are emailed to end-users. The VSW uses software agents to detect volcanic activity alerts generated from a wide variety of sources on the ground and in space, and can also be easily triggered manually.

We describe a new volcanic hotspot detection system, named Middle InfraRed Observation of Volcanic Activity (MIROVA), based on the analysis of infrared data acquired by the Moderate Resolution Imaging Spectroradiometer sensor (MODIS). MIROVA uses the middle infrared radiation (MIR), measured by MODIS, in order to detect and measure the heat radiation deriving from volcanic activity. The algorithm combines spectral and spatial principles, allowing the detection of heat sources from 1 megawatt (MW) to more than 10 gigawatt (GW). This provides a unique opportunity to: (i) recognize small-scale variations in thermal output that may precede the onset of effusive activity; (ii) track the advance of large lava flows; (iii) estimate lava discharge rates; (iv) identify distinct effusive trends; and, lastly, (v) follow the cooling process of voluminous lava bodies for several months. Here we show the results obtained from data sets spanning 14 years recorded at the Stromboli and Mt Etna volcanoes, Italy, and we investigate the above aspects at these two persistently active volcanoes. Finally, we describe how the algorithm has been implemented within an operational near-real-time processing chain that enables the MIROVA system to provide data and infrared maps within 1–4 h of the satellite overpass.

The HOTSAT multiplatform system for the analysis of infrared data from satellites provides a framework that allows the detection of volcanic hotspots and an output of their associated radiative power. This multiplatform system can operate on both Moderate Resolution Imaging Spectroradiometer and Spinning Enhanced Visible and Infrared Imager data. The new version of the system is now implemented on graphics processing units and its interface is available on the internet under restricted access conditions. Combining the estimation of time-varying discharge rates using HOTSAT with the MAGFLOW physics-based model to simulate lava flow paths resulted in the first operational system in which satellite observations drive the modelling of lava flow emplacement. This allows the timely definition of the parameters and maps essential for hazard assessment, including the propagation time of lava flows and the maximum run-out distance. The system was first used in an operational context during the paroxysmal episode at Mt Etna on 12–13 January 2011, when we produced real-time predictions of the areas likely to be inundated by lava flows while the eruption was still ongoing. This allowed key at-risk areas to be rapidly and appropriately identified.

Infrared (IR) satellite-based sensors allow the detection and quantification of volcanic hot spots. Sensors flown on geostationary satellites are particularly helpful in the early warning and continuous tracking of effusive activity. Development of operational monitoring and dissemination systems is essential to achieve the real-time ingestion and processing of IR data for a timely response during volcanic crises. HOTVOLC is a web-based satellite-data-driven monitoring system developed at the Observatoire de Physique du Globe de Clermont-Ferrand (Clermont-Ferrand), designed to achieve near-real-time monitoring of volcanic activity using on-site ingestion of geostationary satellite data (e.g. MSG-SEVIRI, MTSAT, GOES-Imager). Here we present the characteristics of the HOTVOLC system for the monitoring of effusive activity. The system comprises two acquisition stations and secure databases (i.e. mirrored archives). The detection of volcanic hot spots uses a contextual algorithm that is based on a modified form of the Normalized Thermal Index (NTI*) and VAST. Raster images and numerical data are available to open-access on a Web-GIS interface. Tests are carried out and presented here, particularly for the 12–13 January 2011 eruption of Mount Etna, to show the capability of the system to provide quantitative information such as lava volume and time-averaged discharge rate. Examples of operational application reveal the ability of the HOTVOLC system to provide timely thermal information about volcanic hot spot activity.

DOWNFLOW is a probabilistic code for the simulation of the area covered by lava flows. This code has been used extensively for several basaltic volcanoes in the last decade, and a review of some applications is presented. DOWNFLOW is based on the simple principle that a lava flow tends to follow the steepest descent path downhill from the vent. DOWNFLOW computes the area possibly inundated by lava flows by deriving a number, N, of steepest descent paths, each path being calculated over a randomly perturbed topography. The perturbation is applied at each point of the topography, and ranges within the interval ±Δh. N and Δh are the two basic parameters of the code. The expected flow length is constrained by statistical weighting based on the past activity of the volcano. The strength of the code is that: (i) only limited volcanological knowledge is necessary to apply the code at a given volcano; (ii) there are only two (easily tunable) input parameters; and (iii) computational requirements are very low. However, DOWNFLOW does not provide the progression of the lava emplacement over time. The use of DOWNFLOW is ideal when a large number of simulations are necessary: for example, to compile maps for hazard and risk-assessment purposes.

Using the NEODAAS-Dundee AVHRR receiving station (Scotland), NEODAAS-Plymouth can provide calibrated brightness temperature data to end users or interim users in near-real time. Between 2000 and 2009 these data were used to undertake volcano hot spot detection, reporting and time-average discharge rate dissemination during effusive crises at Mount Etna and Stromboli (Italy). Data were passed via FTP, within an hour of image generation, to the hot spot detection system maintained at Hawaii Institute of Geophysics and Planetology (HIGP, University of Hawaii at Manoa, Honolulu, USA). Final product generation and quality control were completed manually at HIGP once a day, so as to provide information to onsite monitoring agencies for their incorporation into daily reporting duties to Italian Civil Protection. We here describe the processing and dissemination chain, which was designed so as to provide timely, useable, quality-controlled and relevant information for ‘one voice’ reporting by the responsible monitoring agencies.

RED SEED stands for Risk Evaluation, Detection and Simulation during Effusive Eruption Disasters, and combines stakeholders from the remote sensing, modelling and response communities with experience in tracking volcanic effusive events. The group first met during a three day-long workshop held in Clermont Ferrand (France) between 28 and 30 May 2013. During each day, presentations were given reviewing the state of the art in terms of (a) volcano hot spot detection and parameterization, (b) operational satellite-based hot spot detection systems, (c) lava flow modelling and (d) response protocols during effusive crises. At the end of each presentation set, the four groups retreated to discuss and report on requirements for a truly integrated and operational response that satisfactorily combines remote sensors, modellers and responders during an effusive crisis. The results of collating the final reports, and follow-up discussions that have been on-going since the workshop, are given here. We can reduce our discussions to four main findings. (1) Hot spot detection tools are operational and capable of providing effusive eruption onset notice within 15 min. (2) Spectral radiance metrics can also be provided with high degrees of confidence. However, if we are to achieve a truly global system, more local receiving stations need to be installed with hot spot detection and data processing modules running on-site and in real time. (3) Models are operational, but need real-time input of reliable time-averaged discharge rate data and regular updates of digital elevation models if they are to be effective; the latter can be provided by the radar/photogrammetry community. (4) Information needs to be provided in an agreed and standard format following an ensemble approach and using models that have been validated and recognized as trustworthy by the responding authorities. All of this requires a sophisticated and centralized data collection, distribution and reporting hub that is based on a philosophy of joint ownership and mutual trust. While the next chapter carries out an exercise to explore the viability of the last point, the detailed recommendations behind these findings are detailed here.

The Aftershock Imaging with Dense Arrays (AIDA) project recorded 12 days of high-density seismic array data following the 23 August 2011 Mineral, Virginia (USA), earthquake. AIDA utilized short-period, vertical-component seismographs at 201 locations to record closely spaced data that would reduce spatial aliasing. Interstation correlation enabled a detection threshold between magnitude −1.5 and −2. A joint hypocenter and velocity inversion algorithm was applied to compressional and shear wave arrival times for 300 of the larger events. Traveltime misfits were minimized using a constant velocity of Vp = 6.2–6.25 and Vs = 3.61–3.63. Hypocenter location error estimates for this range of velocities are ~100 m. Little to no three-dimensional variation exists in the seismic velocity of the upper crust, consistent with the aftershock zone being within a single crystalline rock terrane. The hypocenter locations define a 1–2-km-wide cloud with a strike of ~029° and dip of ~53°E, consistent with the focal mechanism of the main shock. The cloud bends ~5° along strike and has a slightly shallower dip angle below ~6 km depth, indicating a broad, complex fault zone with a slightly concave shape. This study shows that seismic arrays comparable to those used in controlled-source seismology can be successfully applied to aftershock sequences, and that dense array data can produce high-resolution information about earthquake rupture zones.

Creating accurate soil maps at large scales using traditional methods is a timeconsuming and expensive process. However, remote-sensing techniques can provide spatially and spectrally contiguous data in a timely manner. For this study, 20 root zone soil moisture maps derived from Landsat images during the growing season were used for the detection of soil boundaries. A split moving-window analysis along two demonstration transects in, respectively, a semi-arid desert and riparian area located in the Middle Rio Grande Valley of New Mexico showed that remotely sensed root zone soil moisture can reveal subsurface trends that can be used to identify soil boundaries that do not have a strong surface expression. Overall, the use of multiple remotely sensed root zone soil moisture and Landsat images for soil boundary delineation shows great promise of becoming a valuable tool in the field of digital soil mapping.

Although the causes of large intraplate earthquakes are still not fully understood, they pose certain hazard and risk to societies. Estimating hazard and risk in these regions is difficult because of lack of earthquake records. The New Madrid seismic zone is one such region where large and rare intraplate earthquakes (M = 7.0 or greater) pose significant hazard and risk. Many different definitions of hazard and risk have been used, and the resulting estimates differ dramatically. In this paper, seismic hazard is defined as the natural phenomenon generated by earthquakes, such as ground motion, and is quantified by two parameters: a level of hazard and its occurrence frequency or mean recurrence interval; seismic risk is defined as the probability of occurrence of a specific level of seismic hazard over a certain time and is quantified by three parameters: probability, a level of hazard, and exposure time. Probabilistic seismic hazard analysis (PSHA), a commonly used method for estimating seismic hazard and risk, derives a relationship between a ground motion parameter and its return period (hazard curve). The return period is not an independent temporal parameter but a mathematical extrapolation of the recurrence interval of earthquakes and the uncertainty of ground motion. Therefore, it is difficult to understand and use PSHA. A new method is proposed and applied here for estimating seismic hazard in the New Madrid seismic zone. This method provides hazard estimates that are consistent with the state of our knowledge and can be easily applied to other intraplate regions.

A new numerical code for simulating flows of granular material, TITAN2D, is used to model the Merapi-type block and ash flows resulting from the 1991 eruption of Colima Volcano, México. The 1991 block and ash flows reached distances of up to 4 km from the vent with a total estimated volume of 8 × 105 m3. The block and ash flows were modeled using a digital elevation model (DEM) of the region and compared to field data using a quantitative center-line comparison method. Input parameter values, which dictate flow dynamics, were varied to demonstrate the effect these parameters have on the program output. Analysis showed that the TITAN2D model performs best in replicating the center lines of the 1991 Colima block and ash flows when using an initial volume value representative of a single flow event rather than a total deposit volume.

The geometry of Laramide foreland uplifts near the basement-cover interface provides a critical test of models for basement-involved foreland structures. Limits and controls on basement folding are well defined by deflections of the basement-cover unconformity in the northeastern Front Range of Colorado, Rattlesnake Mountain anticline of northern Wyoming, and northern Teton Range of northwestern Wyoming. Where basement overlies sedimentary strata on a single reverse fault, basement folding is largely limited to the area within 1 km of the fault. In this area, distributed high-angle faulting bends the unconformity in the hanging-wall basement tip up to 20° toward the fault. Folding of the hanging-wall tip increases with progressive fault slip and decreased fault dip. Smaller structures commonly show little basement folding, contradicting the fold then fault sequence predicted by the fold-thrust structural model. The footwall basement is either undeformed or bent downward by hanging-wall loading, with upward folding limited to the fault’s narrow zone of cataclastic flow. Where fault splays break the basement into one or more fault-bounded wedges, overlying strata form more open folds without necessitating penetrative basement folding. The hanging-wall basement commonly shows little folding because the weak basement tip, defined by the main fault and the hanging-wall unconformity, is often left behind in the footwall as a subthrust wedge, filling the gap between the flexed sedimentary strata and the footwall basement block. The downward-narrowing zones of deformation with their variably curved fold surfaces and heterogeneous strain are consistent with fault-propagation folding by distributed shear in a triangular shear zone (trishear). Heterogeneous trishear models predict either extensive basement folding in the hanging wall (footwall-fixed trishear) or no basement folding in the hanging wall (hanging-wall–fixed trishear). These trishear modes form a continuum of basement behavior, with the strength of the hanging-wall basement tip controlling the shear-zone location and the extent of basement deformation. Structures with lower angle thrusts show more basement folding because their basement tips are narrowly tapered and weak, fixing the triangular shear zones with respect to the footwall. Structures with higher angle reverse faults show less basement folding because their basement tips are blunt and stronger, fixing the triangular shear zones with respect to the hanging wall.