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Space-time modeling for post-fire vegetation recovery

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Abstract

Recently, there has been increased interest in the behavior of wildfires. Behavior includes explaining: incidence of wildfires; recurrence times for wildfires; sizes, scars, and directions of wildfires; and recovery of burned regions after a wildfire. We study this last problem. In particular, we use the annual normalized difference vegetation index (NDVI) to provide a picture of vegetative levels. Employed post-wildfire, it provides a picture of vegetative recovery. The contribution here is to model post-fire vegetation recovery from a different perspective. What exists in the literature specifies a parametric monotone form for the recovery function and then fits it to the available data. However, recovery need not be monotone; NDVI levels may increase or decrease annually according to climate variables. Furthermore, when there is recovery, it need not follow a simple parametric form. Instead, we view recovery in a relative way. We model what NDVI would look like over the fire scar in the absence of a wildfire. Then, we can examine NDVI recovery locally, employing the observed NDVI recovery at a location relative to the predictive distribution of NDVI at that location. We work with wildfire data from the Communidad Autonomía of Aragón in Spain. We develop our approach in two stages. First, we validate the predictability of NDVI in the absence of wildfire. Then, we study annual recovery and evolution of recovery for an illustrative wildfire region. We work within a hierarchical Bayes framework, adopting suitable dynamic spatial models, attaching full uncertainty to our inference on recovery.

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Notes

  1. NDVI values are aggregated from 30m to 60m by averaging.

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Acknowledgments

The research of the first author was partially supported by the Statistical and Applied Mathematical Sciences Institute (SAMSI) within the Program on Mathematical and Statistical Ecology 2014–2015. The research of the second authors was facilitated by a travel award from Campus IBERUS. The work of the third author was supported in part by Grupo Consolidado Modelos Stocasticos, E-22 DGA. The work of the fourth and fifth authors was done under the Instituto Universitario de Investigacion en Ciencias Ambientales (IUCA). The authors thank Adam Wilson for providing a preprint of his forthcoming PNAS paper. We thank the anonymous reviewers for their comments which have improved the paper.

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Authors and Affiliations

  1. Department of Statistical Science, University of Bologna, Via delle Belle Arti, 41, Bologna, Italy

    Lucia Paci

  2. Department of Statistical Science, Duke University, Box 90251, Durham, NC, 27708-0251, USA

    Alan E. Gelfand

  3. Facultad de Economía y Empresa, Universidad de Zaragoza, C/ María de Luna s/n Campus Rio Ebro., 50018, Zaragoza, Spain

    María Asunción Beamonte

  4. Department of Geography and Land Management, Universidad de Zaragoza, C/ Pedro Cerbuna 12, 50009, Zaragoza, Spain

    Marcos Rodrigues & Fernando Pérez-Cabello

Authors
  1. Lucia Paci
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  2. Alan E. Gelfand
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  3. María Asunción Beamonte
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  4. Marcos Rodrigues
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  5. Fernando Pérez-Cabello
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Corresponding author

Correspondence to Lucia Paci.

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Paci, L., Gelfand, A.E., Beamonte, M.A. et al. Space-time modeling for post-fire vegetation recovery. Stoch Environ Res Risk Assess 31, 171–183 (2017). https://doi.org/10.1007/s00477-015-1182-6

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