DOI: 10.1016/j.foreco.2016.09.013
Scopus记录号: 2-s2.0-84993917341
论文题名: Modeling fire size of wildfires in Castellon (Spain), using spatiotemporal marked point processes
作者: Díaz-Avalos C. ; Juan P. ; Serra-Saurina L.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2016
卷: 381 起始页码: 360
结束页码: 369
语种: 英语
英文关键词: Bayesian inference
; Forest fires
; Spatiotemporal marked point pattern
; Wildfire spatial modeling
Scopus关键词: Bayesian networks
; Carbon dioxide
; Deforestation
; Fire hazards
; Forestry
; Global warming
; Inference engines
; Managers
; Maps
; Markov processes
; Stochastic systems
; Bayesian inference
; Forest fires
; Laplace approximation
; Marked point pattern
; Point pattern analysis
; Spatial modeling
; Spatio-temporal marked point process
; Stochastic partial differential equation
; Fires
; Bayesian analysis
; carbon dioxide
; forest fire
; global warming
; landscape change
; Markov chain
; particulate matter
; risk factor
; spatiotemporal analysis
; stochasticity
; wildfire
; Castellon
; Comunidad Valencia
; Spain
英文摘要: The extent of fires, their periodicity and their impact on terrestrial communities have been a major concern in the last century. Wildfires play an important role in shaping landscapes and as a source of CO2 and particulate matter, contributing to the green house effect and to global warming. Modeling the spatial variability of wildfire extent is therefore an important subject in order to understand and to predict future trends on their effect in landscape changes and global warming. The most common approaches have been through point pattern analysis or by Markov random fields. Those methods have made possible to build risk maps, but for many forest managers knowing the fire size besides the location of the fire is very useful. In this work we use spatial marked point processes to model the fire size of the forest fires observed in Castellón, Spain, during the years 2001–2006. Our modeling approach incorporates spatial covariates as they are useful to model spatial variability and to gain insight about factors related to the presence of forest fires. Such information may be of great utility to predict the spreading of ongoing fires and also to prevent wildfire outburst by controlling risk factors. We describe and take advantage of the Bayesian methodology including Integrated Nested Laplace Approximation (INLA) and Stochastic Partial Differential Equation (SPDE) in the modeling process. We present the results of different models fitted to the data and discuss its usefulness to fire managers and planners. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/64686
Appears in Collections: 影响、适应和脆弱性
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作者单位: Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Apartado Postal 20-726, Del. Alvaro Obregón, México D.F., Mexico; Department of Mathematics, Universidad Jaume I, Spain; Center for Research in Occupational Health (CiSAL), Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain
Recommended Citation:
Díaz-Avalos C.,Juan P.,Serra-Saurina L.. Modeling fire size of wildfires in Castellon (Spain), using spatiotemporal marked point processes[J]. Forest Ecology and Management,2016-01-01,381