Measurement of three dimensional volcanic plume properties using multiple ground based infrared cameras

Abstract

This study presents a method and a proof of principle system for the direct measurement of volcanic plume 3-D spatial properties. The shape of a plume is reconstructed in three dimensions using multi-view imagery collected from static ground-based cameras. The method was developed using data collected during an expedition to Volcán de Fuego in Guatemala, where four thermal infrared cameras were deployed to capture simultaneous images of the regular ash-rich eruptions. A space carving method was applied to the problem to estimate the volume of the plume at any moment in time. By successively applying the method to sequential sets of images, other quantitative measurements such as the drift direction, ascent rate, and dispersion rate can be deduced. The complete method work-flow is presented including data capture, calibration processes, image processing, the space-carving method, and practical implementation issues. The method is sensitive to the camera alignment, hence a novel technique for estimating the camera orientation angles, making use if a high-accuracy terrain model, is described. Other sources of error relating to the number, synchronisation and resolution of the cameras are also discussed. Preliminary results are presented using data collected at Volcán de Fuego in November 2017 over a period of 1.25 h including three distinct eruptions.

Near-Real-Time Tephra Fallout Assessment at Mt. Etna, Italy

“…However, the new camera has a higher field of view, allowing detection of column heights rising up to about 15 km a.s.l. Moreover, new studies on the use of thermal cameras aimed at retrieval of volcanic plumes could increase the capacity of plume detection using ground-based systems [55,56]. It is worth noting that although the analysis could have greater errors for volcanic plumes with insufficient optically thickness, the probability of success in the H estimations by satellite is about 65% [38].…”

Classification of Video Observation Data for Volcanic Activity Monitoring Using Computer Vision and Modern Neural NetWorks (on Klyuchevskoy Volcano Example)

“…The continuous operational mode of the system leads to the generation of a large number of images, while many of them contain no valuable information, because the object of interest or the whole volcano is obscured by clouds, illumination effect, etc. The use of simple cameras instead of special tools (for example, thermal cameras [10][11][12], which overcomes the effects of environmental conditions such as fog or clouds [13]), does not allow us to filter out such information quickly and efficiently from future consideration by volcanologists. In this regard, the technologies required can provide analysis and filtering of incoming data for further assessment of the state of volcanoes, including the detection of signs of potential eruptions.…”

A New Radar‐Based Statistical Model to Quantify Mass Eruption Rate of Volcanic Plumes

“…Various geophysical strategies for the determination of H TP and Q M have been developed, such as the data integration from different sensors with field measurement analysis (e.g., Bonadonna et al., 2011; Corradini et al., 2016; Freret‐Lorgeril et al., 2021; Mereu et al., 2022; Poret et al., 2018). In fact, the Eruptive Source Parameters can be either based on tephra‐fallout deposits (e.g., Carey & Sparks, 1986; Constantinescu et al., 2022; Pyle, 1989; Rossi et al., 2019) or on both ground‐based and satellite sensors (e.g., Aiuppa et al., 2015; Dubuisson et al., 2014; Schellenberg et al., 2019; Wood et al., 2019).…”