Articles | Volume 22, issue 9
https://doi.org/10.5194/nhess-22-2829-2022
https://doi.org/10.5194/nhess-22-2829-2022
Research article
 | 
31 Aug 2022
Research article |  | 31 Aug 2022

Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data

Sébastien Biass, Susanna F. Jenkins, William H. Aeberhard, Pierre Delmelle, and Thomas Wilson

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Latest update: 24 Feb 2024
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Short summary
We present a methodology that combines big Earth observation data and interpretable machine learning to revisit the impact of past volcanic eruptions recorded in archives of multispectral satellite imagery. Using Google Earth Engine and dedicated numerical modelling, we revisit and constrain processes controlling vegetation vulnerability to tephra fallout following the 2011 eruption of Cordón Caulle volcano, illustrating how this approach can inform the development of risk-reduction policies.
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