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  • 1
    Publication Date: 2021-01-27
    Description: 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 pre- sentation 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 erup- tion 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.
    Description: Published
    Description: 1-82
    Description: 5V. Sorveglianza vulcanica ed emergenze
    Description: restricted
    Keywords: effusive eruptions ; volcano monitoring ; 04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: book chapter
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  • 2
    Publication Date: 2017-04-04
    Description: This paper describes an application of artificial neural networks for the recognition of volcanic lava flow hot spots using remote sensing data. Satellite remote sensing is a very effective and safe way to monitor volcanic eruptions in order to safeguard the environment and the people affected by such natural hazards. Neural networks are an effective and well-established technique for the classification of satellite images. In addition, once well trained, they prove to be very fast in the application stage. In our study a back propagation neural network was used for the recognition of thermal anomalies affecting hot lava pixels. The network was trained using the three thermal channels of the Advanced Very High Resolution Radiometer (AVHRR) sensor as inputs and the corre- sponding values of heat flux, estimated using a two thermal component model, as reference outputs. As a case study the volcano Etna (Eastern Sicily, Italy) was chosen, and in particular the effusive eruption which took place during the month of 2006 July. The neural network was trained with a time-series of 15 images (12 nighttime images and 3 daytime images) and validated on three independent data sets of AVHRR images of the same eruption and on two relative to an eruption occurred the following month. While for both nighttime and daytime validation images the neural network identified the image pixels affected by hot lava with a 100 per cent success rate, for the daytime images also adjacent pixels were included, apparently not interested by lava flow. Despite these performance differences under different illumination conditions, the proposed method can be considered effective both in terms of classification accuracy and generalization capability. In particular our approach proved to be robust in the rejection of false positives, often corresponding to noisy or cloudy pixels, whose presence in multispectral images can often undermine the performance of traditional classification algorithms. Future work shall address application of the proposed method to data acquired with a high temporal resolution, such as those provided by the spinning enhanced visible and infrared imager sensor on board the Meteosat second generation geostationary satellite.
    Description: Published
    Description: 1525-1535
    Description: 5V. Sorveglianza vulcanica ed emergenze
    Description: JCR Journal
    Description: restricted
    Keywords: Image processing ; Neural networks ; fuzzy logic ; Remote sensing of volcanoes ; Hot-spot detection ; Mt. Etna ; 04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 3
    Publication Date: 2017-04-04
    Description: When remote sensing users are asked to define their requirements for a new sensor, the big question that always arises is: will the technical specifications meet the scientific requirements? Herein, we discuss quantitative relationships between instrumental spectral and radiometric characteristics and data exploitable for lava flow subpixel temperature analysis. This study was funded within the framework of ESA activities for the IR GMES (Global Monitoring for Environment and Security) element mission requirements in 2005. Subpixel temperature retrieval from satellite infrared data is a well-established method that is well documented in the remote sensing literature. However there is little attention paid to the error analysis on estimated parameters due to atmospheric correction and radiometric accuracy of the sensor. In this study, we suggest the best spectral bands combination to estimate subpixel temperature parameters. We also demonstrate that poor atmospheric corrections may vanish the effectiveness of the most radiometrically accurate instrument.
    Description: Published
    Description: 112-125
    Description: 3V. Dinamiche e scenari eruttivi
    Description: JCR Journal
    Description: restricted
    Keywords: Remote sensing, error analysis, IR sensors, sub-pixel temperature, Numerical solutions; Non-linear differential equations; Effusive volcanism; Eruption mechanisms and flow emplacement; Remote sensing of volcanoes; Volcano monitoring ; 04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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