主管:中华人民共和国应急管理部
主办:应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU

消防科学与技术 ›› 2022, Vol. 41 ›› Issue (6): 841-846.

• • 上一篇    下一篇

基于GIS的凉水国家级自然保护区林火应急路网规划研究

袁铭鞠,卢照林,周 远,孙术发   

  1. (东北林业大学 工程技术学院,黑龙江 哈尔滨 150040)
  • 出版日期:2022-06-15 发布日期:2022-06-15
  • 作者简介:袁铭鞠(1999-),女,黑龙江哈尔滨人,东北林业大学工程技术学院在读研究生,主要从事林区道路网络方面研究,黑龙江省哈尔滨市和兴路26号东北林业大学,150040。
  • 基金资助:
    黑龙江省自然基金项目(LH2020C052)

Research on forest fire emergency road network planning of Liangshui National Nature Reserve based on GIS technology

YUAN Ming-ju, LU Zhao-lin, ZHOU Yuan, SUN Shu-fa   

  1. (College of Engineering and Technology, Northeast Forestry University, Heilongjiang Harbin 150040, China)
  • Online:2022-06-15 Published:2022-06-15

摘要: 摘 要:对凉水国家级自然保护区林火应急路网分析,进行优化选线,使其达到《全国森林防火规划(2016-2025)》2025年路网密度要求。运用K-means聚类算法选取道路节点,在考虑地形等环境因素下,运用ArcGIS多因素叠加分析选取整体线路,经过实地考察验证线路可行。建立林区应急道路评价体系。最终提高林区道路密度达到3.22 m/hm2,符合国家2025年期望林区道路密度。区域分割指数中的区域面积均值(``s)和区域面积方差(D)分别降低了45.7%和 94.7%。为凉水保护区应急路网开设提供参考。

关键词: 关键词:林区路网密度, 层次分析, K-means聚类, GIS选线, 林火应急路网

Abstract: Abstract: Analyze the forest fire emergency road network of Liangshui National Nature Reserve and optimize the route selection to meet the road network density requirements of 2025 in the national forest fire prevention plan (2016-2025). K-means clustering algorithm is used to select road nodes. Considering terrain and other environmental factors, ArcGIS multi factor superposition analysis is used to select the overall line. The line is verified to be feasible through field investigation. Establish an evaluation system for emergency roads in forest areas. Finally, the forest road density will be increased to 3.22 m/hm2, which is in line with the national expectation of forest road density in 2025. Regional area mean in regional segmentation index (S`)and regional area variance (D) decreased by 45.7% and 94.7% respectively, which provides a reference for the establishment of emergency road network in Liangshui protection area.

Key words: Key words: forest road network density, analytic hierarchy process, K-means clustering, GIS route selection figure, forest fire emergency road network