Wildfires vegetation recovery through satellite remote sensing and Functional Data Analysis

TL;DR

This paper uses satellite remote sensing data and Functional Data Analysis to model and understand the slow vegetation recovery process after wildfires, highlighting the importance of pre-wildfire vegetation diversity as a key predictor.

Contribution

It introduces a novel application of Functional Data Analysis to model wildfire effects on vegetation over time using satellite data and pre-wildfire information.

Findings

Vegetation recovery after wildfires is slow.

Pre-wildfire vegetation diversity predicts recovery speed.

Vegetation richness influences post-wildfire recovery.

Abstract

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.