  In recent years wildfires have caused havoc across the world, especially
aggravated in certain regions, due to climate change. Remote sensing has become
a powerful tool for monitoring fires, as well as for measuring their effects on
vegetation over the following years. We aim to explain the dynamics of
wildfires' effects on a vegetation index (previously estimated by causal
inference through synthetic controls) from pre-wildfire available information
(mainly proceeding from satellites). For this purpose, we use regression models
from Functional Data Analysis, where wildfire effects are considered functional
responses, depending on elapsed time after each wildfire, while pre-wildfire
information acts as scalar covariates. Our main findings show that vegetation
recovery after wildfires is a slow process, affected by many pre-wildfire
conditions, among which the richness and diversity of vegetation is one of the
best predictors for the recovery.

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