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林业资源管理 ›› 2023›› Issue (1): 25-33.doi: 10.13466/j.cnki.lyzygl.2023.01.004

• 综合管理 • 上一篇    下一篇

基于人为干扰预警评价的自然保护地整合优化方法研究

查飞云1,2(), 徐瑾1,2(), 燕翃翔1,2, 陈慧敏1,2, 赖智慧1,2   

  1. 1.广州大学 建筑与城市规划学院,广州 510006
    2.广州大学 国家公园研究中心,广州 510006
  • 收稿日期:2022-12-24 修回日期:2023-02-15 出版日期:2023-02-28 发布日期:2023-05-05
  • 通讯作者: 徐瑾(1985-),女,浙江人,硕士生导师,讲师,博士,主要从事自然保护地规划工作。Email:xujin620@sohu.com
  • 作者简介:查飞云(1997-),男,江苏人,硕士研究生,主要从事国家公园整合优化研究工作。Email:chafeiyun@126.com
  • 基金资助:
    广东省基础与应用基础研究基金项目(2022A1515110527)

Research on the Integration and Optimization of Natural Protected Areas based on Early Warning Evaluation of Human Disturbance

ZHA Feiyun1,2(), XU Jin1,2(), YAN Hongxiang1,2, CHEN Huimin1,2, LAI Zhihui1,2   

  1. 1. College of Architecture and Urban Planning,Guangzhou University,Guangzhou 510006,China
    2. National Park Research Center,Guangzhou University,Guangzhou 510006,China
  • Received:2022-12-24 Revised:2023-02-15 Online:2023-02-28 Published:2023-05-05

摘要:

在我国积极推动绿色发展,促进人与自然和谐共生的背景下,对自然保护地整合优化流程进行探讨具有重要的意义。在总结现有方法和步骤的基础上,以人为干扰评价、生态本底评价和人为干扰空间预警评价结果为依据,以整合归并、功能区划分和边界优化为主要步骤的自然保护地整合优化流程,以期为我国自然保护地整合优化工作的进一步深入推进提供参考。

关键词: 自然保护地, 整合优化流程, 生态本底评价, 人为干扰评价, 人为干扰预警模型

Abstract:

Under the background of China's active promotion of green development and the harmonious co-existence of man and nature,it is of great significance to discuss the integration and optimization of natural protected areas.On the basis of summarizing the existing methods,this paper proposed a procedure of the integration and optimization of protected areas according to the results of human disturbance evaluation,ecological background evaluation and human disturbance spatial early warning assessment,and including the main steps of integration and merging,zone division and boundary optimization,in order to provide a reference for the further advancement of the integration and optimization of natural protected areas in China.

Key words: natural protected areas, integration and optimization, ecological background evaluation, human disturbance evaluation, early warning model of human disturbance

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