| 国家公园类型划分与空间识别——以云南省为例 |
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| 引用本文： | 冯子欣,孙欣彤,薛领,张天骄. 国家公园类型划分与空间识别——以云南省为例[J]. 应用生态学报, 2023, 34(1): 187-195. DOI: 10.13287/j.1001-9332.202301.024 |
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| 作者姓名： | 冯子欣 孙欣彤 薛领 张天骄 |
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| 作者单位： | 北京大学政府管理学院, 北京 100871 |
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| 基金项目： | 国家自然科学基金面上项目(71873007)和国家自然科学基金重点项目(71733001)资助。 |
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| 摘 要： | 国家公园是我国推进生态文明建设的重大制度创新,如何科学地对国家公园进行类型划分及空间识别,是国家公园布局和建设中的基础性工作,既有必要性也有紧迫性。本研究以中国国情为基础,参考国际经验,将国家公园划分为荒野导向型、生态优先型、游憩导向型与遗产导向型,构建了一个比较完整的国家公园分类体系。并以自然和人文多样化程度较高的云南为案例,以“双评价”为基础建立了一套指标体系和区划规则,利用人工神经网络建立土地利用演化学习算法,利用融入自适应惯性机制的元胞自动机展开时空模拟,对云南全域进行高分辨率不同类型国家公园的空间辨识,并通过收缩-膨胀原理对识别区域进行比较、修正和优化,进而提出未来云南国家公园布局的综合方案。结果表明: 云南省国家公园主要集中在三江地区与横断山区、滇西以及西南部地区,这3类地区可作为未来国家公园区划与分类保护的重点。本研究所建立的国家公园类型划分和空间识别的一般性可推广的研究范式和工作流程可作为全国应用的参考。
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| 关 键 词： | 国家公园 类型划分 空间识别 人工神经网络-元胞自动机 云南省 |
| 收稿时间： | 2022-03-02 |
National park classification and spatial identification: A case of Yunnan Province,China |
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| FENG Zi-xin,SUN Xin-tong,XUE Ling,ZHAGN Tian-jiao. National park classification and spatial identification: A case of Yunnan Province,China[J]. The journal of applied ecology, 2023, 34(1): 187-195. DOI: 10.13287/j.1001-9332.202301.024 |
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| Authors: | FENG Zi-xin SUN Xin-tong XUE Ling ZHAGN Tian-jiao |
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| Affiliation: | School of Government, Peking University, Beijing 100871, China |
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| Abstract: | National park is a major institutional innovation to promote the construction of ecological civilization in China. How to scientifically classify types and identify spaces is a fundamental task in the layout and construction of national parks, which is critically needed in practice. Based on the national conditions of China and related international experience, we classified national parks into wilderness oriented, ecological priority, recreation oriented, and heritage oriented types, and constructed a relatively complete national park classification scheme. With Yunnan Province as a case, which has a high degree of natural and human diversity, we established a set of index and zoning rules based on “dual evaluation”. The artificial neural networks were used to establish a land use evolution learning algorithm. The meta-cellular automata incorporating an adaptive inertia mechanism was used for spatio-temporal simulation. Spatial identification of different types of national parks was performed for the whole province under high resolution. The contraction-expansion principle was applied to compare, correct, and optimize the identified areas. A comprehensive plan for the future layout of Yunnan National Park was proposed. The results showed that national parks in Yunnan Province were mainly concentrated in the Sanjiang region and the Hengduan Mountains, the west and southwest Yunnan. Those three types of areas could be used as key areas for future natio-nal park planning and protection. The general and worth popularizing research paradigm for national park typology and spatial identification established here could be served as a reference for national application. |
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| Keywords: | national park classification spatial identification artificial neural network-cellular automaton Yunnan Province. |
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