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Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis

Author

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  • Feliu Serra-Burriel

    (Barcelona Supercomputing Center, 08034 Barcelona, Spain
    Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain)

  • Pedro Delicado

    (Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
    Institut de Matemàtiques de la UPC—BarcelonaTech (IMTech), 08028 Barcelona, Spain)

  • Fernando M. Cucchietti

    (Barcelona Supercomputing Center, 08034 Barcelona, Spain)

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.

Suggested Citation

  • Feliu Serra-Burriel & Pedro Delicado & Fernando M. Cucchietti, 2021. "Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis," Mathematics, MDPI, vol. 9(11), pages 1-22, June.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1305-:d:570115
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    References listed on IDEAS

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