Space-time modeling for post-fire vegetation recovery

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• 作者：Lucia Paci ; Alan E. Gelfand…
• 关键词：Dynamic model ; Hierarchical model ; Markov chain Monte Carlo ; Spatial autoregression
• 刊名：Stochastic Environmental Research and Risk Assessment
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：31
• 期：1
• 页码：171-183
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Math. Appl. in Environmental Science; Earth Sciences, general; Probability Theory and Stochastic Processes; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Computa
• 出版者：Springer Berlin Heidelberg
• ISSN：1436-3259
• 卷排序：31

文摘

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.