Abstract The Kosi River in India is well known in the fluvial fan literature because of its well-documented avulsive dynamics and because of the relationships between changes in the course of the river and megafan aggradational processes. The radial configuration of the Kosi drainage network was instrumental in the recognition of large, low-gradient, fluvial-dominated counterparts of alluvial fans, commonly defined as megafans, and the system forms a well-constrained example contributing to the recent development of the concept of distributive fluvial systems. A major flood inundated the Kosi Megafan in August 2008. The available data on the temporal evolution of the flood inundation patterns illustrate how the exceptional discharge event travelled across the megafan surface using the pre-existing distributary channel network, how the anthropogenic infrastructure affected the flood path and propagation, and the type of geomorphic changes that were induced by this catastrophic hydrological event. In spite of the large discharge involved in the flood, the fan drainage network appears not to have been significantly modified by the event, probably because the flood wave followed a pre-existing channel network for avulsion and down-fan propagation. Most of the evident aggradation took place on the proximal and medial domains along a well-defined, radially oriented sector of the fan. The observed pattern of flood propagation and associated sedimentation provide important clues to understanding the processes operating during exceptional discharge events, which are an integral part of the long-term, avulsion-dominated evolution of such systems.

Abstract This article describes an attempt to map snow cover accurately from other land covers using Moderate Resolution Imaging Spectrometer (MODIS) data of 500 m spatial resolution. The workflow includes reflectance modelling, computing snow-cover fraction (SCF) and establishing an empirical relationship between the SCF and normalized difference snow index (NDSI) to map snow cover at operational level. Regression relationships have been developed between the SCF derived from the linear mixture model (LMM) and snow obtained from the NDSI based on two criteria, namely: SCF greater than 0.0 and SCF greater than 0.1. The best regression equation has been selected by examining respective graph plots using statistical measures of mean absolute error, correlation coefficient, root mean square error (RMSE) and uncertainty analysis. The results have been validated against the actual SCF obtained from a high-resolution 15 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible and near infrared (VNIR) scene and covering a substantial range of snow cover of the same area. The selected regression model SCF = 0.25 + 0.35 × NDSI has been tested on other areas and validation efforts show that the pixel-level SCF relationship provides useful results as measured in independent tests against actual SCF obtained from ASTER scene.

Abstract During the period 28 February 2000–31 December 2013, the MODVOLC system ( http://modis.higp.hawaii.edu ) autonomously analysed almost 9 trillion (i.e. 9×10 12 ) pixels contained within almost 3 million MODIS images, searching for evidence of high-temperature thermal signatures associated with volcanic eruptions. Thermal unrest, mainly associated with active lava, be it in the form of flows, domes, lakes or confined to vents, was detected at 93 volcanoes during this period of time. The first part of this paper describes the physical basis and operational implementation of the MODVOLC algorithm. The second part presents data to detail the nature of the thermal emission from these 93 volcanoes over the past 14 years.

Abstract 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 RST VOLC 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 RST VOLC 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 RST VOLC . 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.

Abstract 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.

Abstract 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.

Abstract Many volcanoes around the world are poorly monitored and new eruptions increase the need for rapid ground-based monitoring, which is not always available in a timely manner. Initial observations therefore are commonly provided by orbital remote sensing instruments at different temporal, spatial and wavelength scales. Even at well-monitored volcanoes, satellite data still play an important role. The ASTER (Advanced Spaceborne Thermal Emission Radiometer) orbital sensor provides moderately high spatial resolution images in multiple wavelength regions; however, because ASTER is a scheduled instrument, the data are not acquired over specific targets every orbit. Therefore, in an attempt to improve the temporal frequency of ASTER specifically for volcano observations and to have the images integrate synergistically with high temporal resolution data, the Urgent Request Protocol (URP) system was developed in 2004. Now integrated with both the AVHRR (Advanced Very High Resolution Radiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) hotspot monitoring programmes, the URP acquires an average of 24 volcanic datasets every month and planned improvements will allow this number to increase in the future. New URP data are sent directly to investigators responding to the ongoing eruption, and the large archive is also being used for retrospective science and operational studies for future instruments. The URP Program has been very successful over the past decade and will continue until at least 2017 or as long as the ASTER sensor is operational. Several volcanic science examples are given here that highlight the various stages of the URP development. However, not all are strictly focused on effusive eruptions. Rather, these examples were chosen to demonstrate the wide range of applications, as well as the general usefulness of the higher resolution, multispectral data of ASTER.

Abstract 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.

Abstract 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.

Abstract Effusion rate is a crucial parameter for the prediction of lava-flow advance and should be assessed in near real-time in order to better manage a volcanic crisis. Thermal remote sensing offers the most promising avenue to attain this goal. We present here a ‘dynamic’ thermal proxy based on laboratory experiments and on the physical framework of viscous gravity currents, which can be used to estimate the effusion rate from thermal remote sensing during an eruption. This proxy reproduces the first-order relationship between effusion rate measured in the field and associated powers radiated by basaltic lava flows. Laboratory experiments involving fluids with complex rheology and subject to solidification give additional insights into the dynamics of lava flows. The introduction of a time evolution of the supply rates during the experiments gives rise to a transient adjustment of the surface thermal signal that further compromises the simple proportionality between the thermal flux and the effusion rate. Based on the experimental results, we conclude that a thermal proxy can only yield a minimum and time-averaged estimate of the effusion rate.

Abstract Timely detection and quantification of lava effusion rates are crucial for volcanic hazard mitigation during effusive eruptions. Satellite-based detection methods typically exploit the exceptional radiant heat fluxes associated with lava effusion, but effusive eruptions can also emit prodigious amounts of sulphur dioxide (SO 2 ). Measuring the magnitude and temporal evolution of SO 2 emissions provides an additional means for monitoring effusive eruptions, complementing thermal monitoring. Examples of effusive eruptions detected since 1978 using ultraviolet (UV) satellite measurements of SO 2 emissions by the Total Ozone Mapping Spectrometer (TOMS), Ozone Monitoring Instrument (OMI) and Ozone Mapping and Profiler Suite (OMPS) are reviewed. During many effusive eruptions, trends in SO 2 production mimic the classic waxing–waning pattern characteristic of such events that is also seen in thermal infrared (TIR) hotspot data, suggesting a qualitative link between SO 2 emissions and lava effusion rates. An example of lava effusion rate calculation based on TOMS SO 2 measurements is presented for the 1998 eruption of Cerro Azul (Galápagos Islands), for which detailed eruption observations and independent estimates of effusion rates are available. Combining TOMS-derived SO 2 emission rates with estimates of sulphur content in Cerro Azul lavas yields lava effusion rates almost identical to independently derived values, demonstrating the utility of the technique.

Abstract Hawaiian volcanoes are highly accessible and well monitored by ground instruments. Nevertheless, observational gaps remain and thermal satellite imagery has proven useful in Hawai‘i for providing synoptic views of activity during intervals between field visits. Here we describe the beginning of a thermal remote sensing programme at the US Geological Survey Hawaiian Volcano Observatory (HVO). Whereas expensive receiving stations have been traditionally required to achieve rapid downloading of satellite data, we exploit free, low-latency data sources on the internet for timely access to GOES, MODIS, ASTER and EO-1 ALI imagery. Automated scripts at the observatory download these data and provide a basic display of the images. Satellite data have been extremely useful for monitoring the ongoing lava flow activity on Kīlauea’s East Rift Zone at Pu‘u ‘Ō‘ō over the past few years. A recent lava flow, named Kahauale‘a 2, was upslope from residential subdivisions for over a year. Satellite data helped track the slow advance of the flow and contributed to hazard assessments. Ongoing improvement to thermal remote sensing at HVO incorporates automated hotspot detection, effusion rate estimation and lava flow forecasting, as has been done in Italy. These improvements should be useful for monitoring future activity on Mauna Loa.

Abstract Accurate and fast delivery of information about recent lava flows is important for near-real-time monitoring of eruptions. Here, we have characterized the October 2010 lava flow at Piton de la Fournaise using various InSAR datasets. We first produced a map of the area covered by the lava flow (i.e. Area lava =0.71–0.75 km 2 ) using the coherence of two syn-eruptive interferograms. Then we analysed two post-eruptive InSAR datasets (i.e. monostatic and bistatic data). The monostatic database provided us simultaneously with the displacement rates, lava thickness, volume and volume flux. We found that the lava flow was subsiding and moving eastward at maximum rates of 13±0.3 and 4±0.2 cm a −1 , respectively. Also, it had a mean thickness of Z mean =5.85 m, Vol DRE =1.77±0.75×10 6 m 3 (1σ) and MOR=1.25±0.53 m 3 s −1 . The bistatic database provided us only with the thickness and volume information (i.e. Z mean =6.00 m, Vol DRE =1.83±0.65×10 6 m 3 and MOR=1.29±0.46 m 3 s −1 ). Finally, we used a thermal remote sensing technique to verify the InSAR-derived measurements. Results show that the monostatic and bistatic datasets were both well within the range for the DRE volume obtained from MODIS data (2.44–4.40×10 6 m 3 ). Supplementary material: Tables A1 and A2 give satellite images used in this study. Table A3 gives the parameters used for the calculation of the effusion rates. The figures give the data processing of the post-eruptive radar images. These are available at https://doi.org/10.6084/m9.figshare.c.2213563