南京林业大学学报(自然科学版) ›› 2023, Vol. 47 ›› Issue (5): 73-79.doi: 10.12302/j.issn.1000-2006.202107001

所属专题: 林草计算机应用研究专题

• 专题报道:林草计算机应用研究专题(执行主编 李凤日) • 上一篇    下一篇

基于MODIS的云南省2001—2020年林火发生时空特征分析

张文文1(), 王劲1, 王秋华1,*(), 张曦妍2, 曹恒茂1, 龙腾腾1   

  1. 1.西南林业大学土木工程学院,云南省森林灾害预警与控制重点实验室,云南 昆明 650224
    2.玉溪市新平县林业局,云南 玉溪 653400
  • 收稿日期:2021-07-01 修回日期:2021-09-14 出版日期:2023-09-30 发布日期:2023-10-10
  • 通讯作者: *王秋华(qhwang2010@swfu.edu.cn),教授。
  • 作者简介:张文文(zww819794389@163.com)。
  • 基金资助:
    国家自然科学基金项目(31960318);国家自然科学基金项目(32160376);国家自然科学基金项目(31660320);云南省农业联合面上项目(2018FG001-055);云南省教育厅科学研究项目(2020Y0382)

Analyses on spatial and temporal characteristics of forest fires in Yunnan Province based on MODIS from 2001 to 2020

ZHANG Wenwen1(), WANG Jin1, WANG Qiuhua1,*(), ZHANG Xiyan2, CAO Hengmao1, LONG Tengteng1   

  1. 1. College of Civil Engineering, Southwest Forestry University, Yunnan Key Laboratory of Forest Disaster Warning and Control, Kunming 650224, China
    2. Forestry Bureau in Xinping County, Yuxi 653400, China
  • Received:2021-07-01 Revised:2021-09-14 Online:2023-09-30 Published:2023-10-10

摘要:

【目的】 研究森林火灾发生的时空特征,掌握云南省林火发生的时空分异特征,定量分析林火发生规律,识别林火高发区和易发区,以进行森林火险分布科学区划。【方法】 基于云南省2001—2020年中分辨率成像光谱仪(MODIS)火灾位置/热异常产品数据(MCD14DL),利用火位置信息中所具有的关联性与异质性等,采用统计分析、中心点分析、标准差椭圆法、Ripley’s K 函数、核密度等方法,分析云南省林火发生的时空分布特征。【结果】 ①云南省2001—2020年间林火发生的年际波动较大,2010年出现火点高峰值;季节变化显著,林火主要集中在冬、春两季,春季火点最多且在3月出现高峰值。②林火发生具有显著的区域性特征,林火高发区主要分布在云南省西南部,其次是东南和西北部,东北部林火发生数最少。普洱市是林火发生的中位数中心,其西北部森林火点分布密度最高,核密度值约为0.43;其次是西双版纳州中西部区域,核密度值约为0.34;再者是大理州西北部和文山州东北部区域,这两处核密度值均约为0.26,这4个区域为云南省森林火点分布的热点区。③20年间,林火空间分布的方向性明显,云南省森林火点的总体重心位置正逐步由普洱一带向大理—楚雄—玉溪—红河州一带偏移。④云南省林火的整体空间分布类型为凝聚模式,但聚集程度不高且在逐渐分散。【结论】 2001—2020年云南省林火发生不是完全随机分布的,而是呈现一定的时空分布规律。该特征规律有助于强化区域火情管理,增强重点防火区域的防火建设与宣传,科学安排防火工作,降低林火发生率,实现森林可持续发展。

关键词: MODIS火产品, 火点, 林火发生, 时空特征, 云南省

Abstract:

【Objective】 Studying the temporal and spatial characteristics of forest fires in Yunnan Province, help quantify the occurrence of forest fires, identify areas of high incidence and finally carry out risk zoning scientifically. 【Method】Based on data from Yunnan Province between 2001 and 2020 (the MODIS fire location/thermal anomaly product, MCD14DL), we studied the correlation and heterogeneity of fire locations to analyze spatial and temporal distribution by statistical analysis, center point, standard deviational ellipse, Ripley’s K function, kernel density and other methods. 【Result】(1)We observed large annual fluctuations of forest fires between 2001 and 2020, peaking in 2010. Forest fires in Yunnan Province concentrated in winter and particularly spring, with fire spots peaking in March. (2)The occurrence of forest fires varied greatly between the regions, with the highest incidence in the southwest of Yunnan Province, followed by the southeast and northwest. The northeast had the smallest number of forest fires. The median centre was Pu’er, while the northwest of Pu’er had the highest kernel density(0.43) followed by the central and western regions of Xishuang banna Prefecture, with a kernel density of 0.34, and the northwest of Dali Prefecture and northeast of Wenshan Prefecture, with a nuclear density of 0.26; these four regions were hotspots for forest fires. (3)In the past 20 years, the spatial distribution of forest fires in Yunnan Province has displayed obvious directionality; the center of gravity has gradually shifted from Pu’er to Dali-Chuxiong-Yuxi-Honghezhou. (4)The spatial distribution of forest fires in Yunnan Province fits an aggregation model (but not to a high degree) and is gradually dispersed.【Conclusion】Forest fires in Yunnan Province between 2001 and 2020 were not randomly distributed, but followed a spatial-temporal distribution law. This law can be applied to inform a regional fire management, strengthen the construction and publicity for the fire prevention in priority regions, scientifically organize fire prevention work, reduce the incidence of forest fires and realize the sustainable development of forests.

Key words: moderate-resolution imaging spectroradiometer(MODIS) fire products, fire point, forest fire occurrence, spatial-temporal pattern, Yunnan Province

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