不同区域森林火灾对生态因子的响应及其概率模型

火灾是影响森林生态系统过程的重要干扰之一,其对森林生态系统内各生态因子的响应各不相同.由于植被状况及生态环境的不同,森林火灾的时空分布特征在中国不同植被气候类型内表现不同,根据植被气候类型分类系统,将中国主要森林火灾地区划分为4个区域:东北(冷温带松林)、华北(落叶阔叶林)、东南(常绿阔叶林)和西南(热带雨林),应用遥感监测数据和地面环境数据,以时空变量、生态因子(植被生长变化指数、湿度等)为可选自变量,应用半参数化Logistic回归模型,就森林火险对不同生态影响因子的响应规律进行了分析,建立了基于生态因子的着火概率模型和大火蔓延概率模型,通过模拟及实际数据散点图、火险概率图,评估了模型应用价值.结果表明,土壤湿度及植被含水量在落叶阔叶林、常绿阔叶林、热带雨林地区对着火概率影响显著.在4个植被气候区内,土壤及凋落物湿度对大火蔓延的作用较小.在冷温带松林、落叶阔叶林、常绿阔叶林地区,植被生长的年内变化对火灾发生的影响显著,在常绿阔叶林地区,年内植被生长变化对大火蔓延的作用较小.森林火险概率与各生态因子的相关关系主要呈现出非线性.不同植被气候区内,火险概率受不同生态因子组合的影响,这与不同区域的植被状况及生态环境不同有关.在不同植被气候类型,应用时空变量、生态因子建立半参数化logistic回归模型,进行着火概率和大火蔓延概率的模拟具有可行性和实际应用能力.为进一步分析森林生态系统与火灾之间的动态关系、展开生态系统火灾干扰研究提供了理论基础.

Probability models of forest fire risk based on ecology factors in different vegetation regions over China

Forest fires are considered an important disturbance factor for forest ecosystems. Forest fires are influenced by ecological factors. There are different relationships between diverse ecological factors and forest fires in disparate vegetation regions in China. The objectives of this study were two-fold: i) to assess relationships between ecological factors (NDVI and weather elements) and probability of occurrence of fires (i.e. fire risk), and, ii) to establish fire probability models in four contrasting vegetation regions across China. This study covers the monsoonal region where plenty of rainfall is received but significant seasonal variation within dry and wet seasons are characterised by air mass transitions between inland air and oceanic air. The study area was divided into four sub-regions according to their distinct biomes: i) north China plain with a dominant vegetation of deciduous broad-leaved trees; ii) north-east of China dominated by cool temperate coniferous forest; iii) southeast of China dominated by mixed evergreen broadleaf and deciduous broad-leaved forest, and iv) southwest of China dominated by tropical rain forest. Fire data were extracted from the Along Track Scanning Radiometer of the European Space Agency. Daily values of weather elements from 245 stations covering majority of the four climatic regions above were obtained from China Meteorological Administration. Normalized Difference Vegetation Index (NDVI) was applied as a measure of vegetation status. We linked vegetation with location, time, altitude, weather elements,, and fire characteristics during 1998-2007 in the four regions above using semi-parametric logistic (SPL) regression models. Non-linear relationships between different ecological factors and fire risk (i.e. probability of fire ignition and occurrence of large fire events) were assessed by semi-parametric logistic regression models. We analyzed characteristics of forest fire activity in the four contrasting vegetation regions across China. NDVI was found to be a significant indicator of probability of fire ignition in all the four forest regions except tropical rain forest. On the other hand, NDVI had a significant effect on probability of occurrence of large fire events in all the three forest regions except for evergreen broadleaf region. There was a significant correlation between probability of fire ignition and altitude. Location was found to have a highly significant effect on fire risk in all the four regions, except for the cool temperate coniferous forest. Mean monthly values of ecological factors had more effect on probability of fire ignition than monthly maximum values of those ecological factors. On the contrary, maximum monthly values of ecological factors had more effect on probability of large fire events than mean monthly values of the ecological factors. For each of the four regions of China, a different model of probability of fire ignition and occurrence of large fire events was established. Graphs of observed versus estimated probabilities of large fire events and fire danger maps were produced from the probability models to assess robustness of these models. The results of these final models with selected ecological factors as explanatory variables seem reasonable. The analysis presents some of the dynamics of ecosystem-fire interactions and their value for management systems.