title: [Prediction model of human-caused fire occurrence in the boreal forest of northern China].
abstract: The Chinese boreal forest is an important forest resource in China. However, it has been suffering serious disturbances of forest fires, which were caused equally by natural disasters (e.g., lightning) and human activities. The literature on human-caused fires indicates that climate, topography, vegetation, and human infrastructure are significant factors that impact the occurrence and spread of human-caused fires. But the studies on human-caused fires in the boreal forest of northern China are limited and less comprehensive. This paper applied the spatial analysis tools in ArcGIS 10.0 and Logistic regression model to investigate the driving factors of human-caused fires. Our data included the geographic coordinates of human-caused fires, climate factors during year 1974-2009, topographic information, and forest map. The results indicated that distance to railway (x1) and average relative humidity (x2) significantly impacted the occurrence of human-caused fire in the study area. The logistic model for predicting the fire occurrence probability was formulated as P= 1/[11+e-(3.026-0.00011x1-0.047x2)] with an accuracy rate of 80%. The above model was used to predict the monthly fire occurrence during the fire season of 2015 based on the HADCM2 future weather data. The prediction results showed that the high risk of human-caused fire occurrence concentrated in the months of April, May, June and August, while April and May had higher risk of fire occurrence than other months. According to the spatial distribution of possibility of fire occurrence, the high fire risk zones were mainly in the west and southwest of Tahe, where the major railways were located.
authors.forename: Fu-tao
authors.initials: FT
authors.lastname: Guo
authors.forename: Zhang-wen
authors.initials: ZW
authors.lastname: Su
authors.forename: Guang-yu
authors.initials: GY
authors.lastname: Wang
authors.forename: Qiang
authors.initials: Q
authors.lastname: Wang
authors.forename: Long
authors.initials: L
authors.lastname: Sun
authors.forename: Ting-ting
authors.initials: TT
authors.lastname: Yang
mesh_terms: D002681:China; D002980:Climate; D005390:Fires; D005544:Forecasting; D006802:Human Activities; D006801:Humans; D008030:Lightning; D016015:Logistic Models; D066188:Taiga; D014887:Weather