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云南省森林火灾时空聚集性研究
引用本文:魏建珩,李智,马泽南,王何晨阳,王秋华,舒立福,杨玉钰,高仲亮.云南省森林火灾时空聚集性研究[J].消防科学与技术,2020,39(10):1425-1429.
作者姓名:魏建珩  李智  马泽南  王何晨阳  王秋华  舒立福  杨玉钰  高仲亮
作者单位:1. 西南林业大学土木工程学院,云南昆明650224;2. 中国林科院森林生态环境与保护研究所,北京100091
基金项目:国家自然科学基金资助(31860214,301660210,31960318);西南林业大学科研启动基金项目(111437)
摘    要:基于云南省1990-2017 年的火灾数据和气象数据,应用SaTScan 9.4 进行林火空间聚集性分析,并通过ARMA 模型预测研究区2020-2025 年森林火灾发生次数。结论表明:时间尺度上云南省林火次数随年份增加而逐渐减少,云南省林火主要集中在1-5 月;空间尺度上云南省森林火灾主要聚集在中部、西北部、西南部地区;ARMA 预测模型得出2020-2025 年云南省森林火灾多于2017 年火灾次数,但总体呈下降趋势。通过该研究可以更好地掌握林火聚集性规律,为森林管理部门防控工作提供一定的理论支持。

关 键 词:森林火灾  聚集性  时空分布  SaTScan  ARMA  

Study on spatial and temporal aggregation of forest fire in Yunnan province
WEI Jian-heng,Li Zhi,MA Ze-nan,WANG He-chen-yang,WANG Qiu-hua,SHU Li-fu,YANG Yu-yu,GAO Zhong-liang.Study on spatial and temporal aggregation of forest fire in Yunnan province[J].Fire Science and Technology,2020,39(10):1425-1429.
Authors:WEI Jian-heng  Li Zhi  MA Ze-nan  WANG He-chen-yang  WANG Qiu-hua  SHU Li-fu  YANG Yu-yu  GAO Zhong-liang
Affiliation:1.Civil Engineering College, Southwest Forestry University,Yunnan Kunming 650224, China;2.Institute of Forest Ecological Environment and Protection, CAF, Beijing 100091, China
Abstract:Based on the forest fire and meteorological data of Yunnan province from 1990 to 2017, the spatial aggregation analysis of forest fire was completed using SaTScan 9.4, the ARMA model was used to predict the frequency of forest fires from 2020 to 2025 in the region. The results showed that: At the time scale, the frequency of forest fires in Yunnan province decreased with the increase of year and was concentrated from January to May; On the spatial scale, forest fires mainly gather in central, northwest, and southwest regions in Yunnan province;Based on the ARMA model, there were more fires in Yunnan province from 2020 to 2025 than in 2017, but the number showed a decline trend.The study can help mastering the characteristics of the forest fire clustering, and provide theoretical support for the prevention and control work of the forest management department.
Keywords:forest fire  aggregation  spatial and temporal distribution  SaTScan  ARMA model  
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