Using model-based geostatistics to predict lightning-caused wildfires

The probability of fire in a particular area depends on a range of environmental and geographic variables. Fire prevention planning can be assisted by the construction of models to identify the variables that have a significant influence on the occurrence of fires and by building maps showing the spatial probability distribution for fires occurring in specific geographic areas. We used generalized spatial linear models to predict spatially distributed probabilities for fire occurrence in locations where storms featuring lightning occurred, on the basis of a set of variables related to climatology, orography, vegetation and lightning characteristics, and to assess the relative importance of these variables. A comparison of this model with simple logistic regression models used by other researchers to resolve similar problems demonstrates the importance of bearing in mind spatial correlation between variables.

[1]  Javier Roca-Pardiñas,et al.  ROC curve and covariates: extending induced methodology to the non-parametric framework , 2011, Stat. Comput..

[2]  Wiktor L. Adamowicz,et al.  A Logit Model for Predicting the Daily Occurrence of Human Caused Forest-Fires , 1995 .

[3]  Earle R. Williams,et al.  Lightning and Forest Fires , 2001 .

[4]  F. Csillag,et al.  Spatial patterns of lightning-caused forest fires in Ontario, 1976–1998 , 2003 .

[5]  Fernando Bação,et al.  Characterizing and modelling the spatial patterns of wildfire ignitions in Portugal: fire initiation and resulting burned area. , 2008 .

[6]  Jeremy Russell-Smith,et al.  Fire and vegetation dynamics: Studies from the North American boreal forest: By Edward A. Johnson. Cambridge Studies in Ecology, Cambridge University Press, Cambridge, UK. 1992. 129 pp. ISBN-0-521-34596-0 (hbk). Price £30·00 (hbk) , 1993 .

[7]  Mike D. Flannigan,et al.  LIGHTNING-IGNITED FOREST FIRES IN NORTHWESTERN ONTARIO , 1991 .

[8]  Lasse Loepfe,et al.  An integrative model of human-influenced fire regimes and landscape dynamics , 2011, Environ. Model. Softw..

[9]  Patricia L. Andrews,et al.  Introduction To Wildland Fire , 1984 .

[10]  Xiao-Hua Zhou,et al.  Statistical Methods in Diagnostic Medicine , 2002 .

[11]  E. Chuvieco,et al.  Human-caused wildfire risk rating for prevention planning in Spain. , 2009, Journal of environmental management.

[12]  N. Breslow,et al.  Approximate inference in generalized linear mixed models , 1993 .

[13]  B. Kedem,et al.  Bayesian Prediction of Transformed Gaussian Random Fields , 1997 .

[14]  David L. Martell,et al.  A lightning fire occurrence model for Ontario , 2005 .

[15]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[16]  P. McCullagh,et al.  Generalized Linear Models , 1984 .

[17]  Hao Zhang On Estimation and Prediction for Spatial Generalized Linear Mixed Models , 2002, Biometrics.

[18]  P. Diggle,et al.  Childhood malaria in the Gambia: a case-study in model-based geostatistics. , 2002 .

[19]  Sue A. Ferguson,et al.  Model-Generated Predictions of Dry Thunderstorm Potential , 2007 .

[20]  O. F. Christensen Monte Carlo Maximum Likelihood in Model-Based Geostatistics , 2004 .

[21]  M. Vasconcelos,et al.  Spatial Prediction of Fire Ignition Probabilities: Comparing Logistic Regression and Neural Networks , 2001 .

[22]  E. Johnson,et al.  Forest fires : behavior and ecological effects , 2001 .

[23]  P. Diggle,et al.  Model‐based geostatistics , 2007 .

[24]  J. de las Heras ... Modelling, Monitoring and Management of Forest Fires , 2008 .

[25]  C. Tomás,et al.  Ten-year study of cloud-to-ground lightning activity in the Iberian Peninsula , 2005 .

[26]  Hao Zhang Optimal Interpolation and the Appropriateness of Cross-Validating Variogram in Spatial Generalized Linear Mixed Models , 2003 .

[27]  David L. Verbyla,et al.  Spatial patterns of lightning strikes in interior Alaska and their relations to elevation and vegetation , 2003 .

[28]  R. Waagepetersen,et al.  Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models , 2002, Biometrics.

[29]  Peter J. Diggle,et al.  An Introduction to Model-Based Geostatistics , 2003 .

[30]  Jing Yang,et al.  Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis , 2011, Environ. Model. Softw..

[31]  Wisdom M. Dlamini,et al.  A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland , 2010, Environ. Model. Softw..

[32]  David L. Martell,et al.  Modelling seasonal variation in daily people-caused forest fire occurrence , 1989 .

[33]  P. Diggle,et al.  Analysis of Longitudinal Data. , 1997 .

[34]  P. McCullagh,et al.  Generalized Linear Models , 1992 .

[35]  Boris Kompare,et al.  Environmental Modelling & Software , 2014 .

[36]  J. Moreno,et al.  Patterns of Lightning-, and People-Caused Fires in Peninsular Spain , 1998 .

[37]  Sue A. Ferguson,et al.  Space-time modelling of lightning-caused ignitions in the Blue Mountains, Oregon , 2001 .

[38]  E. Johnson,et al.  Fire and Vegetation Dynamics: Studies from the North American Boreal Forest. , 1993 .

[39]  Jesper Møller,et al.  Spatial statistics and computational methods , 2003 .

[40]  Yves Bergeron,et al.  Role of vegetation and weather on fire behavior in the Canadian mixedwood boreal forest using two fire behavior prediction systems. , 2001 .

[41]  Sw. Banerjee,et al.  Hierarchical Modeling and Analysis for Spatial Data , 2003 .