Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis

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ID: 265605
2021
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Abstract
In recent years, wildfires have caused havoc across the world, which are 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.
Reference Key
serra-burriel2021mathematicswildfires Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Feliu Serra-Burriel;Pedro Delicado;Fernando M. Cucchietti;Serra-Burriel, Feliu;Delicado, Pedro;Cucchietti, Fernando M.;
Journal Mathematics
Year 2021
DOI
10.3390/math9111305
URL
https://www.mdpi.com/2227-7390/9/11/1305/htm
Keywords
Landsat causal inference remote sensing ndvi wildfires functional data analysis synthetic controls functional principal components analysis function-on-scalar regression time series decomposition

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