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 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 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.

Abstract Developments in spaceborne Earth Observation (EO) sensor technology over the last decade, combined with well-tested physical models and multispectral data-processing techniques developed from the early 1980s, have paved the way to the global monitoring of volcanoes by sensors of metric, decametric, kilometric and multi-kilometric spatial resolution. Such variable geometries provide for revisit intervals ranging from about monthly – at high-spatial resolution in Low-Earth Orbit – to less than 5 min – at low-spatial resolution, from geostationary platforms. There are currently about 20 spacecrafts available for carrying out 24/7 quantitative observations of volcanic unrest, at all resolutions and as close as possible to real-time. We show some successful examples of synergetic EO on volcanoes on three continents from 10 different payloads, automatically processed with three, end-to-end unsupervised procedures, on eight major eruptions and a lava lake between 2006 and 2014.

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 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.