Article(id=1322942935305695455, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322942934613635295, articleNumber=null, orderNo=null, doi=10.14067/j.cnki.1673-923x.2026.05.007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744128000000, receivedDateStr=2025-04-09, revisedDate=1752422400000, revisedDateStr=2025-07-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1793343774867, onlineDateStr=2026-10-30, pubDate=1779638400000, pubDateStr=2026-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793343774867, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793343774867, creator=13701087609, updateTime=1793343774867, updator=13701087609, issue=Issue{id=1322942934613635295, tenantId=1146029695717560320, journalId=1322258771877134531, year='2026', volume='46', issue='5', pageStart='1', pageEnd='222', issueExtLink='null', onlineDate='null', pubDate='1779638400000', pubDateStr='2026-05-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793343774703, creator='13701087609', updateTime=1793344699149, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322946812499329386, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322942934613635295, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322946812499329387, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322942934613635295, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=59, endPage=67, ext={EN=ArticleExt(id=1322942935473467616, articleId=1322942935305695455, tenantId=1146029695717560320, journalId=1322258771877134531, language=EN, title=Spatial distribution pattern of forest fire video monitoring points in state-owned forest farms in Hunan Province, columnId=null, journalTitle=Journal of Central South University of Forestry & Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=
【Objective】

Forest fire video monitoring is the key means of forest fire monitoring in forest management agencies. Reasonable spatial distribution pattern can improve monitoring efficiency, reduce monitoring blind spots, detect fires in time, and reduce losses from forest fires.

【Method】

Taking 216 state-owned forest fire video monitoring points in Hunan Province as study objects, the spatial distribution type, spatial distribution density and spatial distribution direction of forest fire video monitoring points were analyzed by using the nearest neighbor index, kernel density estimation method and standard deviation ellipse, respectively. The spatial distribution pattern of forest fire video monitoring points in state-owned forest farms in Hunan Province was studied, and the influencing factors of site selection of forest fire video monitoring points were discussed from four aspects: topography, traffic, fire risk and social economy.

【Result】

There are 645 forest fire video monitoring points in the state-owned forest farms in Hunan Province, showing a significant agglomeration pattern in space. From the distribution of forest fire video monitoring points in various cities and prefectures of Hunan Province, Shaoyang City has the largest number of forest fire video monitoring points, and Xiangtan City has the least, accounting for 17.36% and 0.31% of the total number of the provinces, respectively. From the perspective of distribution density, the spatial distribution shows a regional difference of ‘less in the north and more in the south’. There are four high-density area centers, located in Changsha City, Zhuzhou City, Yongzhou City and Shaoyang City; in addition, there is a sub-intensive area center located in Xiangxi Tujia and Miao Autonomous Prefecture. The numerical difference between the long and short axes is large, indicating that the spatial distribution of forest fire video monitoring points has a strong directionality, and presents a northwest-southeast distribution trend.

【Conclusion】

The spatial distribution of forest fire video monitoring points in state-owned forest farms in Hunan Province is significantly affected by factors such as elevation, slope, traffic accessibility, number of graves, operating area of state-owned forest farms, and number of tourist attractions. It provides a reference for strengthening the construction of forest fire prevention and extinguishing capacity in Hunan Province and the rational layout of forest fire video monitoring points.

, authors=Xinyu WANG1, Shisheng LONG1, Xiaojing CAI1, Ziming LI2, Falin LIU1, authorsList=Xinyu WANG, Shisheng LONG, Xiaojing CAI, Ziming LI, Falin LIU, authorCompany=null, correspAuthors=Falin LIU, authorNote=null, correspAuthorsNote=null, copyrightStatement=© 2026 Journal of Central South University of Forestry & Technology, copyrightOwner=Journal of Central South University of Forestry & Technology, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322942936278773985, articleId=1322942935305695455, tenantId=1146029695717560320, journalId=1322258771877134531, language=CN, title=湖南省国有林场林火视频监控点空间分布格局, columnId=1322924153761837409, journalTitle=中南林业科技大学学报, columnName=林学, runingTitle=null, highlight=null, articleAbstract=
【目的】

林火视频监控是森林经营单位进行林火监测的核心手段。分析林火视频监控点的空间分布特征及其影响因素,对优化监控点的空间布局、提升监测效率、减少监测盲区、实现火情早发现早预警,进而降低森林火灾损失具有重要意义。

【方法】

以湖南省216个国有林场林火视频监控点为研究对象,采用最近邻距离指数、核密度估计法、标准差椭圆法分别分析林火视频监控点的空间分布类型、空间分布密度、空间分布方向性,对湖南省国有林场林火视频监控点的空间分布格局进行研究,从地形、交通、火灾风险、社会经济四个方面对林火视频监控点的选址影响因素进行了探讨。

【结果】

湖南省国有林场共有林火视频监控点645个,在空间上呈现显著集聚特征。从湖南省各市州林火视频监控点的分布数量来看,邵阳市林火视频监控点的数量最多,湘潭市最少,分别占全省总数的17.36%和0.31%。从分布密度来看,在空间分布上呈“北少南多”的区域性差异,有4个高密集区中心,分别位于长沙市、株洲市、永州市和邵阳市;存在1个次密集区中心,位于湘西土家族苗族自治州;而湘潭市、娄底市、岳阳市、常德市以及张家界市等地区的核密度值较低。从分布方向性来看,标准差椭圆长短轴之间的数值差距较大,表明林火视频监控点的空间分布方向性较强,并呈现西北—东南走向的分布态势。

【结论】

湖南省国有林场林火视频监控点空间分布受高程、坡度、交通通达度、墓地数量、国有林场经营面积、旅游景点数量等因素影响较为显著,研究结果为加强湖南省森林防灭火能力建设及林火视频监控点合理布局提供参考。

, authors=汪欣雨1, 龙时胜1, 蔡晓晶1, 李梓铭2, 刘发林1, authorsList=汪欣雨, 龙时胜, 蔡晓晶, 李梓铭, 刘发林, authorCompany=null, correspAuthors=刘发林, authorNote=

汪欣雨(),硕士研究生。

, correspAuthorsNote=
刘发林(),教授,博士,博士研究生导师。
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汪欣雨(),硕士研究生。

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湖南省国有林场林火视频监控点空间分布格局
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汪欣雨 1 , 龙时胜 1 , 蔡晓晶 1 , 李梓铭 2 , 刘发林 1
中南林业科技大学学报 | 林学 2026,46(5): 59-67
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中南林业科技大学学报 |林学 2026 , 46 (5) : 59 -67
湖南省国有林场林火视频监控点空间分布格局
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汪欣雨1 , 龙时胜1, 蔡晓晶1, 李梓铭2, 刘发林1
作者信息
  • 1.中南林业科技大学 林学院,湖南 长沙 410004
  • 2.湖南省林业局,湖南 长沙 410007
通讯作者:
刘发林(),教授,博士,博士研究生导师。
作者简介:

汪欣雨(),硕士研究生。

Spatial distribution pattern of forest fire video monitoring points in state-owned forest farms in Hunan Province
Xinyu WANG1 , Shisheng LONG1, Xiaojing CAI1, Ziming LI2, Falin LIU1
Affiliations
  • 1.School of Forestry, Central South University of Forestry & Technology, Changsha 410004, Hunan, China
  • 2.Hunan Forestry Bureau, Changsha 410007, Hunan, China
出版时间: 2026-05-25 doi: 10.14067/j.cnki.1673-923x.2026.05.007
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【目的】

林火视频监控是森林经营单位进行林火监测的核心手段。分析林火视频监控点的空间分布特征及其影响因素,对优化监控点的空间布局、提升监测效率、减少监测盲区、实现火情早发现早预警,进而降低森林火灾损失具有重要意义。

【方法】

以湖南省216个国有林场林火视频监控点为研究对象,采用最近邻距离指数、核密度估计法、标准差椭圆法分别分析林火视频监控点的空间分布类型、空间分布密度、空间分布方向性,对湖南省国有林场林火视频监控点的空间分布格局进行研究,从地形、交通、火灾风险、社会经济四个方面对林火视频监控点的选址影响因素进行了探讨。

【结果】

湖南省国有林场共有林火视频监控点645个,在空间上呈现显著集聚特征。从湖南省各市州林火视频监控点的分布数量来看,邵阳市林火视频监控点的数量最多,湘潭市最少,分别占全省总数的17.36%和0.31%。从分布密度来看,在空间分布上呈“北少南多”的区域性差异,有4个高密集区中心,分别位于长沙市、株洲市、永州市和邵阳市;存在1个次密集区中心,位于湘西土家族苗族自治州;而湘潭市、娄底市、岳阳市、常德市以及张家界市等地区的核密度值较低。从分布方向性来看,标准差椭圆长短轴之间的数值差距较大,表明林火视频监控点的空间分布方向性较强,并呈现西北—东南走向的分布态势。

【结论】

湖南省国有林场林火视频监控点空间分布受高程、坡度、交通通达度、墓地数量、国有林场经营面积、旅游景点数量等因素影响较为显著,研究结果为加强湖南省森林防灭火能力建设及林火视频监控点合理布局提供参考。

林火视频监控  /  空间分布格局  /  国有林场  /  湖南省
【Objective】

Forest fire video monitoring is the key means of forest fire monitoring in forest management agencies. Reasonable spatial distribution pattern can improve monitoring efficiency, reduce monitoring blind spots, detect fires in time, and reduce losses from forest fires.

【Method】

Taking 216 state-owned forest fire video monitoring points in Hunan Province as study objects, the spatial distribution type, spatial distribution density and spatial distribution direction of forest fire video monitoring points were analyzed by using the nearest neighbor index, kernel density estimation method and standard deviation ellipse, respectively. The spatial distribution pattern of forest fire video monitoring points in state-owned forest farms in Hunan Province was studied, and the influencing factors of site selection of forest fire video monitoring points were discussed from four aspects: topography, traffic, fire risk and social economy.

【Result】

There are 645 forest fire video monitoring points in the state-owned forest farms in Hunan Province, showing a significant agglomeration pattern in space. From the distribution of forest fire video monitoring points in various cities and prefectures of Hunan Province, Shaoyang City has the largest number of forest fire video monitoring points, and Xiangtan City has the least, accounting for 17.36% and 0.31% of the total number of the provinces, respectively. From the perspective of distribution density, the spatial distribution shows a regional difference of ‘less in the north and more in the south’. There are four high-density area centers, located in Changsha City, Zhuzhou City, Yongzhou City and Shaoyang City; in addition, there is a sub-intensive area center located in Xiangxi Tujia and Miao Autonomous Prefecture. The numerical difference between the long and short axes is large, indicating that the spatial distribution of forest fire video monitoring points has a strong directionality, and presents a northwest-southeast distribution trend.

【Conclusion】

The spatial distribution of forest fire video monitoring points in state-owned forest farms in Hunan Province is significantly affected by factors such as elevation, slope, traffic accessibility, number of graves, operating area of state-owned forest farms, and number of tourist attractions. It provides a reference for strengthening the construction of forest fire prevention and extinguishing capacity in Hunan Province and the rational layout of forest fire video monitoring points.

forest fire video monitoring  /  spatial distribution pattern  /  state-owned forest farm  /  Hunan Province
汪欣雨, 龙时胜, 蔡晓晶, 李梓铭, 刘发林. 湖南省国有林场林火视频监控点空间分布格局. 中南林业科技大学学报, 2026 , 46 (5) : 59 -67 . DOI: 10.14067/j.cnki.1673-923x.2026.05.007
Xinyu WANG, Shisheng LONG, Xiaojing CAI, Ziming LI, Falin LIU. Spatial distribution pattern of forest fire video monitoring points in state-owned forest farms in Hunan Province[J]. Journal of Central South University of Forestry & Technology, 2026 , 46 (5) : 59 -67 . DOI: 10.14067/j.cnki.1673-923x.2026.05.007
  • 国家自然科学基金项目(31470659)
  • 湖南省林业科技攻关与创新资金项目(XLK202435)
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2026年第46卷第5期
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doi: 10.14067/j.cnki.1673-923x.2026.05.007
  • 接收时间:2025-04-09
  • 首发时间:2026-10-30
  • 出版时间:2026-05-25
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  • 收稿日期:2025-04-09
  • 修回日期:2025-07-14
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国家自然科学基金项目(31470659)
湖南省林业科技攻关与创新资金项目(XLK202435)
作者信息
    1.中南林业科技大学 林学院,湖南 长沙 410004
    2.湖南省林业局,湖南 长沙 410007

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刘发林(),教授,博士,博士研究生导师。
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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