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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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    1. Max A. Moritz & Enric Batllori & Ross A. Bradstock & A. Malcolm Gill & John Handmer & Paul F. Hessburg & Justin Leonard & Sarah McCaffrey & Dennis C. Odion & Tania Schoennagel & Alexandra D. Syphard, 2014. "Learning to coexist with wildfire," Nature, Nature, vol. 515(7525), pages 58-66, November.
    2. A. Westerling & B. Bryant & H. Preisler & T. Holmes & H. Hidalgo & T. Das & S. Shrestha, 2011. "Climate change and growth scenarios for California wildfire," Climatic Change, Springer, vol. 109(1), pages 445-463, December.
    3. Febrero-Bande, Manuel & de la Fuente, Manuel Oviedo, 2012. "Statistical Computing in Functional Data Analysis: The R Package fda.usc," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i04).
    4. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
    5. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    6. Yanlan Liu & Mukesh Kumar & Gabriel G. Katul & Amilcare Porporato, 2019. "Reduced resilience as an early warning signal of forest mortality," Nature Climate Change, Nature, vol. 9(11), pages 880-885, November.
    7. Theodore Alexandrov & Silvia Bianconcini & Estela Bee Dagum & Peter Maass & Tucker S. McElroy, 2012. "A Review of Some Modern Approaches to the Problem of Trend Extraction," Econometric Reviews, Taylor & Francis Journals, vol. 31(6), pages 593-624, November.
    8. Antonio Cuevas & Manuel Febrero & Ricardo Fraiman, 2007. "Robust estimation and classification for functional data via projection-based depth notions," Computational Statistics, Springer, vol. 22(3), pages 481-496, September.
    9. Haiganoush K. Preisler & Benjamin P. Bryant & Anthony L. Westerling, 2014. "Scenarios for future wildfire risk in California: links between changing demography, land use, climate, and wildfire," Environmetrics, John Wiley & Sons, Ltd., vol. 25(6), pages 454-471, September.
    10. Amatulli, Giuseppe & Peréz-Cabello, Fernando & de la Riva, Juan, 2007. "Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty," Ecological Modelling, Elsevier, vol. 200(3), pages 321-333.
    11. Jeff Goldsmith & Vadim Zipunnikov & Jennifer Schrack, 2015. "Generalized multilevel function-on-scalar regression and principal component analysis," Biometrics, The International Biometric Society, vol. 71(2), pages 344-353, June.
    12. Jan Verbesselt & Nikolaus Umlauf & Marina Hirota & Milena Holmgren & Egbert H. Van Nes & Martin Herold & Achim Zeileis & Marten Scheffer, 2016. "Remotely sensed resilience of tropical forests," Nature Climate Change, Nature, vol. 6(11), pages 1028-1031, November.
    13. Marten Scheffer & Jordi Bascompte & William A. Brock & Victor Brovkin & Stephen R. Carpenter & Vasilis Dakos & Hermann Held & Egbert H. van Nes & Max Rietkerk & George Sugihara, 2009. "Early-warning signals for critical transitions," Nature, Nature, vol. 461(7260), pages 53-59, September.
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