J Plant Ecol ›› 2015, Vol. 8 ›› Issue (5): 480-490 .

• Research Articles •

### Historic distribution and driving factors of human-caused fires in the Chinese boreal forest between 1972 and 2005

Futao Guo1,2, John L. Innes2, Guangyu Wang2,*, Xiangqing Ma1, Long Sun3, Haiqing Hu3 and Zhangwen Su1

1. 1 Faculty of Forestry, Fujian Agriculture and Forestry University, No. 15 Shangxiadian Road, Fuzhou 350002, China; 2 Sustainable Forest Management Laboratory, Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada; 3 Faculty of Forestry, Northeast Forestry University, No. 26 Hexing Road, Harbin, Heilongjiang Province 150040, China
• Received:2014-03-02 Accepted:2014-11-22 Published:2015-09-16
• Contact: Guo, Futao

### Historic distribution and driving factors of human-caused fires in the Chinese boreal forest between 1972 and 2005

Abstract: 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.

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.