Articles | Volume 22, issue 9
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-79', Matthieu Kervyn, 09 May 2022
    • AC1: 'Reply on RC1', Sebastien Biass, 23 Jun 2022
  • RC2: 'Comment on nhess-2022-79', Anonymous Referee #2, 29 May 2022
    • AC2: 'Reply on RC2', Sebastien Biass, 23 Jun 2022
    • AC3: 'Reply on RC2', Sebastien Biass, 24 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (07 Jul 2022) by Giovanni Macedonio
AR by Sebastien Biass on behalf of the Authors (07 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (22 Jul 2022) by Giovanni Macedonio
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.
Final-revised paper