Suchergebnisse

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 Earth observation (EO) data and machine learning complement existing impact datasets
and open the way to a new type of dynamic and large-scale vulnerability
models.