Articles | Volume 22, issue 9
https://doi.org/10.5194/nhess-22-2829-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-2829-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Aug 2022
Research article |
| 31 Aug 2022
| 31 Aug 2022
Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data
Sébastien Biass
CORRESPONDING AUTHOR
Earth Observatory of Singapore, Nanyang Technological University, Singapore
Department of Earth Sciences, University of Geneva, Geneva, Switzerland
Susanna F. Jenkins
Earth Observatory of Singapore, Nanyang Technological University, Singapore
Asian School of the Environment, Nanyang Technological University, Singapore
William H. Aeberhard
Swiss Data Science Center, ETH Zürich, Zurich, Switzerland
Pierre Delmelle
Environmental Sciences, Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium
Thomas Wilson
School of Earth and the Environment, University of Canterbury, Christchurch, New Zealand
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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.
We present a methodology that combines big Earth observation data and interpretable machine...
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