Modeling fire size of wildfires in Castellon (Spain), using spatiotemporal marked point processes
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http://dx.doi.org/10.1016/j.foreco.2016.09.013 |
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Título
Modeling fire size of wildfires in Castellon (Spain), using spatiotemporal marked point processesFecha de publicación
2016-12-01Editor
ElsevierCita bibliográfica
DÍAZ ÁVALOS, Carlos; JUAN VERDOY, Pablo; SERRA, Laura. Modeling fire size of wildfires in Castellon (Spain), using spatiotemporal marked point processes. Forest Ecology and Management (2016), v. 381, pp. 360-369Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0378112716305746Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
The extent of fires, their periodicity and their impact on terrestrial communities have been a major con-
cern in the last century. Wildfires play an important role in shaping landscapes and as a source of CO
2
a ... [+]
The extent of fires, their periodicity and their impact on terrestrial communities have been a major con-
cern in the last century. Wildfires play an important role in shaping landscapes and as a source of CO
2
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 possi-
ble 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. [-]
Publicado en
Forest Ecology and Management (2016), v. 381Derechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/restrictedAccess
info:eu-repo/semantics/restrictedAccess
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