School of Forestry, Southwest Forestry University, Kunming 650224, China.
Key Laboratory of Forest Protection of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing 100091, China.
Sci Total Environ. 2023 Apr 15;869:161782. doi: 10.1016/j.scitotenv.2023.161782. Epub 2023 Jan 23.
Wildfires directly affect global ecosystem stability and severely threaten human life. The mountainous areas of Southwest China experience frequent wildfires. Mapping the susceptibility patterns and analyzing the drivers of wildfires are crucial for effective wildfire management, especially considering that the inclusion of seasonal dimensions will produce more dynamic results. Using Yunnan Province of China as a case study area, a method was attempted to distinguish dependable wildfires by season, while possible wildfire drivers were obtained and refined within seasons. The patterns of wildfire susceptibility in different seasons were modeled based on the Maxent and random forest models. Then, the spatial relationships between wildfire and potential drivers were analyzed integrating with GeoDetector to evaluate the variable importance of drivers and the marginal effect of drivers. The results showed that the two models effectively depicted each season's wildfire susceptibility. The susceptible wildfire areas in spring and winter are located throughout Yunnan Province, with anthropogenic factors being the most significant drivers. During the summer and autumn, wildfire risk areas are relatively concentrated, showing a trend of dominant drought-driven and humid conditions. The differences in wildfire drivers across seasons reflect the lagged effect of climate factors on wildfires, leading to significant discrepancies in the marginal effects of how seasonal drivers affect wildfires. The findings improve our understanding of the effects of the interseasonal variability of environmental variables on wildfires and promote the development of specific seasonal wildfire management strategies.
野火直接影响全球生态系统的稳定性，并严重威胁人类生命。中国西南山区经常发生野火。绘制野火易感性模式并分析野火驱动因素对于有效的野火管理至关重要，特别是考虑到包含季节性维度将产生更动态的结果。以中国云南省为例，尝试了一种区分季节性可靠野火的方法，同时在各个季节中获得并精炼了可能的野火驱动因素。基于 Maxent 和随机森林模型，对不同季节的野火易感性模式进行建模。然后，通过与 GeoDetector 集成，分析野火与潜在驱动因素之间的空间关系，以评估驱动因素的重要性和驱动因素的边际效应。结果表明，这两个模型有效地描述了每个季节的野火易感性。春季和冬季的易受野火影响的地区遍布云南省，人为因素是最重要的驱动因素。在夏季和秋季，野火风险地区相对集中，呈现出干旱驱动和湿润条件主导的趋势。季节性驱动因素之间的差异反映了气候因素对野火的滞后效应，导致季节性驱动因素对野火影响的边际效应存在显著差异。这些发现提高了我们对环境变量季节性变化对野火影响的认识，并促进了特定季节性野火管理策略的发展。
