Space-time modeling for post-fire vegetation recovery
| Autor: | Alan E. Gelfand, Marcos Rodrigues, María Asunción Beamonte, Fernando Pérez-Cabello, Lucia Paci |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
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Environmental Engineering 010504 meteorology & atmospheric sciences 0211 other engineering and technologies 02 engineering and technology 01 natural sciences Dynamic model Normalized Difference Vegetation Index Hierarchical database model symbols.namesake Bayes' theorem Hierarchical model Markov chain Monte Carlo Spatial autoregression Environmental Chemistry Water Science and Technology Safety Risk Reliability and Quality 2300 Predictability 021101 geological & geomatics engineering 0105 earth and related environmental sciences General Environmental Science Parametric statistics Space time Vegetation Settore SECS-S/01 - STATISTICA Climatology Reliability and Quality symbols Environmental science Safety |
| Popis: | 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 Autonomia of Aragon 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. |
| Databáze: | OpenAIRE |
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