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我国北方针叶林人为火发生的预测模型

郭福涛1,苏漳文1,王光玉2,王强3,孙龙3**,杨婷婷1   

  1. 1福建农林大学林学院, 福州 350002; 2加拿大不列颠哥伦比亚大学林学院, 温哥华 V6T 1Z4; 3 东北林业大学林学院, 哈尔滨 150040)
  • 出版日期:2015-07-18 发布日期:2015-07-18

Prediction model of human-caused fire occurrence in the boreal forest of northern China.

GUO Fu-tao1, SU Zhang-wen1, WANG Guang-yu2, WANG Qiang3, SUN Long3, YANG Ting-ting1   

  1. (1College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China; 2Faculty of Forestry, University of British Columbia, Vancouver V6T 1Z4, Canada; 3School of Forestry, University of Northeast Forestry, Harbin 150040, China)
  • Online:2015-07-18 Published:2015-07-18

摘要:

我国北方针叶林带是重要的森林资源储藏地,也是林火发生的重灾区,其自然火和人为火所占比例相当. 气象因子、地形特征、植被条件、人为基础设施等因素对人为火发生具有显著影响,国内目前应用空间分析技术对北方针叶林带人为火影响因子的研究还存在一定不确定性. 本文基于1974—2009年间人为火的空间地理坐标,结合研究地的气象因子、基础地理信息及矢量化林相图,应用ArcGIS 10.0中的空间分析工具和SPSS 19.0的逻辑斯蒂回归模型对影响人为火发生的主要驱动因子进行分析,并建立人为火发生的概率模型. 利用HADCM2模式下研究区域未来气象数据对塔河地区2015年人为火发生情况进行计算.结果表明: 距离铁路距离(x1)和平均相对湿度(x2)对研究区域人为火发生具有显著影响,并得到火险概率模型P=1/[1+e-(3.026-0.00011x1-0.047x2)]. 模型校验结果显示,模型的准确度可达到80%.林火发生预测结果表明,塔河地区2015年 4—6月、8月为人为火高发期,其中,4—5月的林火发生概率最高.从火险空间分布来看,高火险主要集中在塔河西部和西南部,铁路线路主要包含在此区域.
 

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/[1+e-(3.026-0.00011x1-0.047x2)]  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.