Preprints
https://doi.org/10.5194/nhess-2022-79
https://doi.org/10.5194/nhess-2022-79
 
31 Mar 2022
31 Mar 2022
Status: this preprint is currently under review for the journal NHESS.

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

Sébastien Biass1,2, Susanna F. Jenkins1,3, William H. Aeberhard4, Pierre Delmelle5, and Thomas Wilson6 Sébastien Biass et al.
  • 1Earth Observatory of Singapore, Nanyang Technological University, Singapore
  • 2Department of Earth Sciences, University of Geneva, Switzerland
  • 3Asian School of the Environment, Nanyang Technological University, Singapore
  • 4Swiss Data Science Center, ETH Zürich, Switzerland
  • 5Environmental Sciences, Earth and Life Institute, UCLouvain, Belgium
  • 6School of Earth and the Environment, University of Canterbury, New Zealand

Abstract. Although the generally high fertility of volcanic soils is often seen as an opportunity, short-term consequences of eruptions on natural and cultivated vegetation are likely to be negative. The empirical knowledge obtained from post-event impact assessments provides crucial insights into the range of parameters controlling impact and recovery of vegetation, but their limited coverage in time and space offers a limited sample of all possible eruptive and environmental conditions. Consequently, vegetation vulnerability remains largely unconstrained, thus impeding quantitative risk analyses.

Here, we explore how cloud-based big Earth Observation data, remote sensing and interpretable machine learning (ML) can provide a large-scale alternative to identify the nature of, and infer relationships between, drivers controlling vegetation impact and recovery. We present a methodology developed using Google Earth Engine to systematically revisit the impact of past eruptions and constrain critical hazard and vulnerability parameters. Its application to the impact associated with the tephra fallout from the 2011 eruption of Cordón Caulle volcano (Chile) reveals its ability to capture different impact states as a function of hazard and environmental parameters and highlights feedbacks and thresholds controlling impact and recovery of both natural and cultivated vegetation. We therefore conclude that big EO data and machine learning complement existing impact datasets open the way to a new type of dynamic and large-scale vulnerability models.

Sébastien Biass et al.

Status: open (until 01 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Sébastien Biass et al.

Sébastien Biass et al.

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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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