Aims The pattern and driving factors of forest fires are of interest for fire occurrence prediction and forest fire management.The aims of the study were:(i)to describe the history of human-caused fires by season and ...Aims The pattern and driving factors of forest fires are of interest for fire occurrence prediction and forest fire management.The aims of the study were:(i)to describe the history of human-caused fires by season and size of burned area over time;(ii)to identify the spatial patterns of human-caused fires and test for the existence of‘hotspots’to determine their exact locations in the Daxing’an mountains;(iii)to determine the driving factors that determine the spatial distribution and the possibility of human-caused fire occurrence.Methods In this study,K-function and Kernel density estimation were used to analyze the spatial pattern of human-caused fires.The analysis was conducted in s-plus and arcgIs environments,respectively.The analysis of driving factors was performed in SPSS 19.0 based on a logistic regression model.The variables used to identify factors that influence fire occurrence included vegetation types,meteorological conditions,socioeconomic factors,topography and infrastructure factors,which were extracted and collected through the spatial analysis mode of arcgIs and from official statistics,respectively.Important Findings The annual number of human-caused fires and the area burnt have declined since 1987 due to the implementation of a forest fire protection act.There were significant spatial heterogeneity and seasonal variations in the distribution of human-caused fires in the Daxing’an mountains.The heterogeneity was caused by elevation,distance to the nearest railway,forest type and temperature.a logistic regression model was developed to predict the likelihood of human-caused fire occurrence in the Daxing’an mountains;its global accuracy attained 64.8%.The model was thus comparable to other relevant studies.展开更多
文摘Aims The pattern and driving factors of forest fires are of interest for fire occurrence prediction and forest fire management.The aims of the study were:(i)to describe the history of human-caused fires by season and size of burned area over time;(ii)to identify the spatial patterns of human-caused fires and test for the existence of‘hotspots’to determine their exact locations in the Daxing’an mountains;(iii)to determine the driving factors that determine the spatial distribution and the possibility of human-caused fire occurrence.Methods In this study,K-function and Kernel density estimation were used to analyze the spatial pattern of human-caused fires.The analysis was conducted in s-plus and arcgIs environments,respectively.The analysis of driving factors was performed in SPSS 19.0 based on a logistic regression model.The variables used to identify factors that influence fire occurrence included vegetation types,meteorological conditions,socioeconomic factors,topography and infrastructure factors,which were extracted and collected through the spatial analysis mode of arcgIs and from official statistics,respectively.Important Findings The annual number of human-caused fires and the area burnt have declined since 1987 due to the implementation of a forest fire protection act.There were significant spatial heterogeneity and seasonal variations in the distribution of human-caused fires in the Daxing’an mountains.The heterogeneity was caused by elevation,distance to the nearest railway,forest type and temperature.a logistic regression model was developed to predict the likelihood of human-caused fire occurrence in the Daxing’an mountains;its global accuracy attained 64.8%.The model was thus comparable to other relevant studies.
