Evaluation of wildfire propagation susceptibility in grasslands using burned areas and multivariate logistic regression
Evaluation of wildfire propagation susceptibility in grasslands using burned areas and multivariate logistic regression
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DOI：
10.1080/01431161.2013.805280
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发表时间：
2013-10
影响因子：
3.4
通讯作者：
Xin Cao;X. Cui;Miao Yue;Jin Chen;H. Tanikawa;Y. Ye
中科院分区：
文献类型：
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作者：
Xin Cao;X. Cui;Miao Yue;Jin Chen;H. Tanikawa;Y. Ye
本研究基于多元逻辑回归模拟了森林野火的传播敏感性。中分辨率成像光谱仪（MODIS）衍生燃料指标和地形因子为自变量，燃烧面积为因变量。在2001 - 2007年4 - 5月和9 - 10月的野火季节，每天采集MODIS数据，获取蒙中草原的活燃和死燃信息。野火传播敏感性模型的独立参数输入为归一化植被指数（NDVI）、优化土壤调整植被指数（OSAVI）、水分胁迫指数（MSI）、全球植被水分指数（GVMI）、死燃料指数（DFI）、海拔、坡度和坡向。多元logistic回归排序表明，DFI、MSI、DEM和OSAVI是排名前4位的因素，总体准确率为80%。“遗漏一个”交叉验证表明，繁殖敏感性模型的总体准确性在65%到87%之间。最后，利用该模型绘制了2001-2007年野火季节的10天平均野火传播敏感性图，并预测了被烧毁区域的位置。该研究将有助于了解草原地区野火的传播敏感性，并制定防止野火蔓延的政策。
This research simulated wildfire propagation susceptibility based on multivariate logistic regression. Moderate Resolution Imaging Spectrometer (MODIS)-derived fuel indicators and topographic factors were the independent variables, and burnt areas served as the dependent variable. MODIS data were collected daily during the wildfire seasons of April to May and September to October from 2001 to 2007 to acquire information about live and dead fuel in the Mongolia–China grasslands. The inputs for the independent parameters for wildfire propagation susceptibility modelling were the normalized difference vegetation index (NDVI), optimized soil-adjusted vegetation index (OSAVI), moisture stress index (MSI), global vegetation moisture index (GVMI), dead fuel INDEX (DFI), elevation, slope, and aspect. Multivariate logistic regression ranking indicates that DFI, MSI, DEM, and OSAVI are the top four factors, with an overall accuracy of 80%. ‘Leave one out’ cross-validation demonstrated that the overall accuracy of the propagation susceptibility modelling ranged from 65% to 87%. Finally, the model was used to produce 10 day average wildfire propagation susceptibility maps during the wildfire seasons of 2001–2007 and to predict the location of burned areas. This research will be useful for understanding the propagation susceptibility of wildfires in grassland areas and for creating policies for preventing wildfire spread.