Factor contribution to fire occurrence, size, and burn probability in a subtropical coniferous forest in East China

The contribution of factors including fuel type, fire-weather conditions, topography and human activity to fire regime attributes (e.g. fire occurrence, size distribution and severity) has been intensively discussed. The relative importance of those factors in explaining the burn probability (BP), which is critical in terms of fire risk management, has been insufficiently addressed. Focusing on a subtropical coniferous forest with strong human disturbance in East China, our main objective was to evaluate and compare the relative importance of fuel composition, topography, and human activity for fire occurrence, size and BP. Local BP distribution was derived with stochastic fire simulation approach using detailed historical fire data (1990–2010) and forest-resource survey results, based on which our factor contribution analysis was carried out. Our results indicated that fuel composition had the greatest relative importance in explaining fire occurrence and size, but human activity explained most of the variance in BP. This implies that the influence of human activity is amplified through the process of overlapping repeated ignition and spreading events. This result emphasizes the status of strong human disturbance in local fire processes. It further confirms the need for a holistic perspective on factor contribution to fire likelihood, rather than focusing on individual fire regime attributes, for the purpose of fire risk management.

[1]  Ian R. Noble,et al.  McArthur's fire-danger meters expressed as equations , 1980 .

[2]  P. Sen,et al.  Introduction to bivariate and multivariate analysis , 1981 .

[3]  Z. Wang.  Current Forest Fire Danger Rating System (Cffdrs) , 1992 .

[4]  X. Lee,et al.  Introduction to wildland fire , 1997 .

[5]  E. Pastor,et al.  Mathematical models and calculation systems for the study of wildland fire behaviour , 2003 .

[6]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[7]  James M. LeBreton,et al.  History and Use of Relative Importance Indices in Organizational Research , 2004 .

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  R. Novak,et al.  West Nile Virus and Wildlife , 2004 .

[10]  G. Casella,et al.  Generalized Accept-Reject sampling schemes , 2004 .

[11]  W. Romme,et al.  The Interaction of Fire, Fuels, and Climate across Rocky Mountain Forests , 2004 .

[12]  T. Swetnam,et al.  Landscape-scale controls over 20th century fire occurrence in two large Rocky Mountain (USA) wilderness areas , 2002, Landscape Ecology.

[13]  Nikos Koutsias,et al.  FIRE OCCURRENCE PATTERNS AT LANDSCAPE LEVEL: BEYOND POSITIONAL ACCURACY OF IGNITION POINTS WITH KERNEL DENSITY ESTIMATION METHODS , 2008 .

[14]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[15]  Ulrike Groemping,et al.  Relative Importance for Linear Regression in R: The Package relaimpo , 2006 .

[16]  Paola Zuccolotto,et al.  Variable Selection Using Random Forests , 2006 .

[17]  T. Swetnam,et al.  Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity , 2006, Science.

[18]  F. Chapin,et al.  Human Impacts on the Fire Regime of Interior Alaska: Interactions among Fuels, Ignition Sources, and Fire Suppression , 2006, Ecosystems.

[19]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[20]  Joel L. Schiff,et al.  Cellular Automata: A Discrete View of the World (Wiley Series in Discrete Mathematics & Optimization) , 2007 .

[21]  B. Mike Wotton,et al.  Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications , 2009, Environmental and Ecological Statistics.

[22]  Susan I. Stewart,et al.  Human influence on California fire regimes. , 2007, Ecological applications : a publication of the Ecological Society of America.

[23]  Donald McKenzie,et al.  Spatial models for inferring topographic controls on historical low-severity fire in the eastern Cascade Range of Washington, USA , 2008, Landscape Ecology.

[24]  F. J. Lozano,et al.  A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case study in a mountainous Mediterranean region , 2008 .

[25]  A. Gill,et al.  Large fires, fire effects and the fire-regime concept , 2008 .

[26]  O. Viedma,et al.  Landscape structural features control fire size in a Mediterranean forested area of central Spain. , 2009 .

[27]  E. Chuvieco,et al.  Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem , 2009 .

[28]  Stefano Mazzoleni,et al.  Fire regime: history and definition of a key concept in disturbance ecology , 2010, Theory in Biosciences.

[29]  Matthew P. Thompson,et al.  Integrated national-scale assessment of wildfire risk to human and ecological values , 2011 .

[30]  Elbert E. N. Macau,et al.  Stochastic cellular automata model for wildland fire spread dynamics , 2011 .

[31]  M. Parisien,et al.  Multi-scale evaluation of the environmental controls on burn probability in a southern Sierra Nevada landscape , 2011 .

[32]  Charles W. McHugh,et al.  Numerical Terradynamic Simulation Group 10-2011 A simulation of probabilistic wildfire risk components for the continental United States , 2017 .

[33]  Christopher I. Roos,et al.  The human dimension of fire regimes on Earth , 2011, Journal of biogeography.

[34]  Calvin A. Farris,et al.  Use of random forests for modeling and mapping forest canopy fuels for fire behavior analysis in Lassen Volcanic National Park, California, USA , 2012 .

[35]  Shixin Wang,et al.  Fire danger assessment with remote sensing: a case study in Northern China , 2012, Natural Hazards.

[36]  Zhijun Tong,et al.  GIS-based multi-dimensional risk assessment of the grassland fire in northern China , 2012, Natural Hazards.

[37]  J. Pereira,et al.  Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest , 2012 .

[38]  M. Parisien,et al.  Considerations for modeling burn probability across landscapes with steep environmental gradients: an example from the Columbia Mountains, Canada , 2013, Natural Hazards.

[39]  Yu Chang,et al.  Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China , 2012 .

[40]  M. Finney,et al.  Wildfire Exposure Analysis on the National Forests in the Pacific Northwest, USA , 2013, Risk analysis : an official publication of the Society for Risk Analysis.

[41]  Matthew P. Thompson,et al.  A national approach for integrating wildfire simulation modeling into Wildland Urban Interface risk assessments within the United States , 2013 .

[42]  Matthew P. Thompson,et al.  How risk management can prevent future wildfire disasters in the wildland-urban interface , 2013, Proceedings of the National Academy of Sciences.

[43]  Donatella Spano,et al.  Analyzing spatiotemporal changes in wildfire regime and exposure across a Mediterranean fire-prone area , 2014, Natural Hazards.

[44]  Zhihua Liu,et al.  Identifying the Threshold of Dominant Controls on Fire Spread in a Boreal Forest Landscape of Northeast China , 2013, PloS one.

[45]  S. L. J. Oliveira,et al.  Modelling Fire Frequency in a Cerrado Savanna Protected Area , 2014, PloS one.

[46]  José M. C. Pereira,et al.  Wildfire Selectivity for Land Cover Type: Does Size Matter? , 2014, PloS one.

[47]  O. Sass,et al.  Using multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests , 2014 .

[48]  Zhiliang Zhu,et al.  The spatial variation in forest burn severity in Heilongjiang Province, China , 2016, Natural Hazards.

[49]  Matthew P. Thompson,et al.  Wildfire Risk Transmission in the Colorado Front Range, USA , 2015, Risk analysis : an official publication of the Society for Risk Analysis.

[50]  C. Ricotta,et al.  Modelling the Meteorological Forest Fire Niche in Heterogeneous Pyrologic Conditions , 2015, PloS one.

[51]  Zhihua Liu,et al.  Climatic and Landscape Influences on Fire Regimes from 1984 to 2010 in the Western United States , 2015, PloS one.

[52]  Hai Lin,et al.  Lessons Learned from Whole Exome Sequencing in Multiplex Families Affected by a Complex Genetic Disorder, Intracranial Aneurysm , 2015, PloS one.

[53]  D. I. Iudin,et al.  Reprint of Infinity computations in cellular automaton forest-fire model , 2015, Commun. Nonlinear Sci. Numer. Simul..

[54]  C. Justice,et al.  Factors Controlling Vegetation Fires in Protected and Non-Protected Areas of Myanmar , 2015, PloS one.

[55]  Jinghu Pan,et al.  Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China , 2016, Natural Hazards.

[56]  R. Rothermel.  A Mathematical Model for Predicting Fire Spread in Wildland Fuels , 2017 .

[57]  N. Metropolis.  THE BEGINNING of the MONTE CARLO METHOD , 2022 .