A time-dependent stochastic grassland fire ignition probability model for Hulun Buir Grassland of China

被引:0
作者
Zhixing Guo
Weihua Fang
Jun Tan
Xianwu Shi
机构
[1] Ministry of Civil Affairs & Ministry of Education,Academy of Disaster Reduction and Emergency Management
[2] State Oceanic Administration,National Marine Hazard Mitigation Service
[3] State Key Laboratory of Earth Surface Processes and Resource Ecology,Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education
[4] Beijing Normal University,undefined
来源
Chinese Geographical Science | 2013年 / 23卷
关键词
grassland fire; binary logistic regression; GIS spatial analysis; ignition probability; Monte Carlo method;
D O I
暂无
中图分类号
学科分类号
摘要
Grassland fire is one of the most important disturbance factors in the natural ecosystems. This paper focuses on the spatial distribution of long-term grassland fire patterns in the Hulun Buir Grassland located in the northeast of Inner Mongolia Autonomous Region in China. The density or ratio of ignition can reflect the relationship between grassland fire and different ignition factors. Based on the relationship between the density or ratio of ignition in different range of each ignition factor and grassland fire events, an ignition probability model was developed by using binary logistic regression function and its overall accuracy averaged up to 81.7%. Meanwhile it was found that daily relative humidity, daily temperature, elevation, vegetation type, distance to county-level road, distance to town are more important determinants of spatial distribution of fire ignitions. Using Monte Carlo method, we developed a time-dependent stochastic ignition probability model based on the distribution of inter-annual daily relative humidity and daily temperature. Through this model, it is possible to estimate the spatial patterns of ignition probability for grassland fire, which will be helpful to the quantitative evaluation of grassland fire risk and its management in the future.
引用
收藏
页码:445 / 459
页数:14
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共 61 条
[11]  
Francisco R(2010)Information diffusion-based spatio-temporal risk analysis of grassland fire disaster in northern China Knowledge-Based Systems 23 53-60
[12]  
Fernando B(2009)Human-caused wildfire risk rating for prevention planning in Spain Journal of Environmental Management 90 1241-1252
[13]  
Finch J(2004)Spectral indices and fire behavior simulation for fire risk assessment in savanna ecosystems Remote Sensing of Environment 91 1-13
[14]  
Marchant R(2001)GIS analysis of spatial and temporal patterns of human-caused wildfires in the temperate rain forest of Vancouver Island, Canada Forest Ecology and Management 140 1-18
[15]  
Flannigan M D(2006)Evaluation of satellite based primary production modeling in the semi-arid Sahel Remote Sensing of Environment 105 173-188
[16]  
Krawchuk M A(2001)Spatial prediction of fire ignition probabilities: Comparing logistic regression and neural networks Photogrammetric Engineering and Remote Sensing 67 73-81
[17]  
de Groot W J(2007)Integrating new methods and tools in fire danger rating International Journal of Wildland Fire 16 306-316
[18]  
Giuseppe A(1999)Fire history in northern Patagonia: The roles of humans and climatic variation Ecological Monographs 69 47-67
[19]  
Fernando P(2010)Using GIS spatial analysis and logistic regression to predict the probabilities of human-caused grassland fires Journal of Arid Environments 74 386-393
[20]  
de la Riva J(2005)Improvements of the MODIS terrestrial gross and net primary production global data set Remote Sensing of Environment 95 164-176