Faculty of Geography, Yunnan Normal University, Kunming, 650500, China.
GIS Technology Engineering Research Centre for West-China Resources and Environment of Education-Al Ministry, Kunming, 650500, China.
Sci Rep. 2022 Nov 8;12(1):19029. doi: 10.1038/s41598-022-23697-6.
Forest fires are among the major natural disasters that destroy the balance of forest ecosystems. The construction of a forest fire prediction model to investigate the driving mechanism of fire drivers on forest fires can help reveal the mechanism of forest fire occurrence and its risk, and thus contribute to the prevention and control of forest fires. However, previous studies on the mechanisms of forest fire drivers have not considered the effect of differences in spatial scale of action of forest fire drivers on the predicted effect. Therefore, the present study proposes a spatial prediction model of forest fires that considers the spatial scale effect of forest fire drivers to predict forest fire risk. First, based on historical forest fire data and geographic environmental data in the Yunnan Province, geographically weighted logistic regression (GWLR) was used to determine the forest fire drivers and to estimate the probability of forest fire occurrence at locations where fire observations are absent. Then, multi-scale geographically weighted regression (MGWR) was used to explore the spatial scales of action of different drivers on forest fires. The results show that meteorological factors such as relative humidity, air temperature, air pressure, sunshine hours, daily precipitation, wind speed, topographic factors such as elevation, slope, and aspect, anthropogenic factors such as population density and road network, as well as vegetation type, were significantly correlated with forest fires; thus, they are identified as important factors influencing occurrence of forest fires in the Yunnan Province. The MGWR model regression results show that the role of different forest fire drivers on forest fire occurrence has spatial scale differences. The spatial scale of drivers such as altitude, aspect, wind speed, temperature, slope, and distance from the road to the fire point was larger and their spatial influence was relatively stable, with spatial heterogeneity having less influence on the model evaluation results. The spatial scale of drivers such as relative humidity, sunshine, air pressure, precipitation, population density, and vegetation type were smaller, and spatial heterogeneity had a more obvious influence on the model evaluation results. This study provides a reference for selecting drivers and evaluating their spatial scale effects to construct predictive regional forest fire models.
森林火灾是破坏森林生态系统平衡的主要自然灾害之一。建立森林火灾预测模型，研究火灾驱动因素对森林火灾的驱动机制，可以帮助揭示森林火灾发生的机制及其风险，从而有助于森林火灾的预防和控制。然而，以前关于森林火灾驱动因素的机制研究并没有考虑到森林火灾驱动因素的空间作用尺度对预测效果的影响。因此，本研究提出了一种考虑森林火灾驱动因素空间尺度效应的森林火灾空间预测模型，以预测森林火灾风险。首先，基于云南省历史森林火灾数据和地理环境数据，利用地理加权逻辑回归（GWLR）确定森林火灾驱动因素，并估计无火灾观测点的森林火灾发生概率。然后，利用多尺度地理加权回归（MGWR）探讨不同驱动因素对森林火灾的空间作用尺度。结果表明，相对湿度、气温、气压、日照时数、日降水量、风速等气象因素、海拔、坡度、坡向等地形因素、人口密度和路网等人为因素以及植被类型与森林火灾显著相关，是影响云南省森林火灾发生的重要因素。MGWR 模型回归结果表明，不同森林火灾驱动因素对森林火灾发生的作用具有空间尺度差异。海拔、坡向、风速、温度、坡度、距火点道路距离等驱动因素的空间尺度较大，空间影响较为稳定，空间异质性对模型评价结果的影响较小。相对湿度、日照、气压、降水、人口密度、植被类型等驱动因素的空间尺度较小，空间异质性对模型评价结果的影响较为明显。本研究为选择驱动因素和评价其空间尺度效应，构建预测性区域森林火灾模型提供了参考。
