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

Wildfires vegetation recovery through satellite remote sensing and Functional Data Analysis