College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, PR China; Sustainable Forest Management Laboratory, Faculty of Forestry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
College of Forestry, Northeast Forestry University, Harbin, Heilongjiang 150040, PR China.
Sci Total Environ. 2017 Dec 15;605-606:411-425. doi: 10.1016/j.scitotenv.2017.06.219. Epub 2017 Jun 30.
In this study, spatial patterns and driving factors of fires were identified from 2000 to 2010 using Ripley's K (d) function and logistic regression (LR) model in two different forest ecosystems of China: the boreal forest (Daxing'an Mountains) and sub-tropical forest (Fujian province). Relative effects of each driving factor on fire occurrence were identified based on standardized coefficients in the LR model. Results revealed that fires were spatially clustered and that fire drivers vary amongst differing forest ecosystems in China. Fires in the Daxing'an Mountains respond primarily to human factors, of which infrastructure is recognized as the most influential. In contrast, climate factors played a critical role in fire occurrence in Fujian, of which the temperature of fire season was found to be of greater importance than other climate factors. Selected factors can predict nearly 80% of the total fire occurrence in the Daxing'an Mountains and 66% in Fujian, wherein human and climate factors contributed the greatest impact in the two study areas, respectively. This study suggests that different fire prevention and management strategies are required in the areas of study, as significant variations of the main fire-driving exist. Rapid socio-economic development has produced similar effects in different forest ecosystems within China, implying a strong correlation between socio-economic development and fire regimes. It can be concluded that the influence of human factors will increase in the future as China's economy continues to grow - an issue of concern that should be further addressed in future national fire management.
本研究利用 Ripley's K（d）函数和逻辑回归（LR）模型，确定了 2000 年至 2010 年中国两个不同森林生态系统（大兴安岭的北方森林和福建省的亚热带森林）火灾的空间格局和驱动因素。根据 LR 模型中的标准化系数，确定了每个驱动因素对火灾发生的相对影响。结果表明，火灾在空间上呈聚集分布，火灾驱动因素在中国不同的森林生态系统中存在差异。大兴安岭的火灾主要受人为因素影响，其中基础设施被认为是最具影响力的因素。相比之下，气候因素在福建的火灾发生中起着关键作用，其中火灾季节的温度比其他气候因素更为重要。所选因素可预测大兴安岭火灾总发生率的近 80%，福建的火灾总发生率的 66%，其中人为因素和气候因素在两个研究区域的火灾发生中分别贡献最大。本研究表明，由于主要火灾驱动因素存在显著差异，研究区域需要采取不同的防火和管理策略。中国快速的社会经济发展对不同的森林生态系统产生了类似的影响，这表明社会经济发展与火灾制度之间存在很强的相关性。可以得出结论，随着中国经济的持续增长，人为因素的影响将会增加，这是未来国家火灾管理中需要进一步解决的问题。
