3D Lava flow mapping of the 17–25 May 2016 Etna eruption using tri-stereo optical satellite data

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Gaetana Ganci
Annalisa Cappello
https://orcid.org/0000-0002-0610-9504
Vito Zago
Giuseppe Bilotta
Alexis Herault
Ciro Del Negro

Abstract

During basaltic eruptions, the average rate at which lava is erupted (effusion rate) is one of the most important factor controlling the evolution, growth and extent of the flow field. This has implication both for forecasting purposes as input parameter into physics-based numerical models, and for advancing knowledge on the shallow feeder system by constraining the mass supplied. Satellite remote sensing provides a means to estimate the average effusion rate by applying a direct conversion from the measured radiant heat loss by an active lava flow. This conversion relies on a set of parameters of lava (e.g. rock density, heat capacity, vesicularity, emissivity, etc.) and suffers of multiple sources of uncertainties and measurements errors, whose quantification is still an open problem. Here we constrain the lava volume emitted at Mt Etna on 17-25 May 2016, by using pre-eruptive and post eruptive digital elevation models (DEMs) obtained processing satellite images acquired by the Pléiades constellation, which provides images at 50 cm resolution in stereo and tri-stereo mode. The 3D processing of the tri-stereo Pléiades imagery (acquired on 24 December 2015 and 18 July 2016), performed using the free and open source MicMac photogrammetric library provides estimations of the distribution of thickness and the bulk volume emitted. The integration of multi-platform remote sensing products represents a new potential of merging capabilities to enable a more comprehensive response to effusive crises.

Article Details

How to Cite
Ganci, G., Cappello, A., Zago, V., Bilotta, G., Herault, A. and Del Negro, C. (2019) “3D Lava flow mapping of the 17–25 May 2016 Etna eruption using tri-stereo optical satellite data”, Annals of Geophysics, 62(2), p. VO220. doi: 10.4401/ag-7875.
Section
Special Issue: MeMoVolc

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