Feng Zi-Xin, Sun Xin-Tong, Xue Ling, Zhagn Tian-Jiao
School of Government, Peking University, Beijing 100871, China.
Ying Yong Sheng Tai Xue Bao. 2023 Jan;34(1):187-195. doi: 10.13287/j.1001-9332.202301.024.
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.
国家公园是中国推进生态文明建设的一项重大制度创新。如何科学分类和空间识别是国家公园布局建设的一项基础性工作，也是实践中迫切需要的。基于中国国情和国际相关经验，我们将国家公园分为荒野主导型、生态优先型、游憩主导型和遗产主导型，并构建了一套较为完整的国家公园分类体系。以自然和人文多样性程度较高的云南省为例，基于“双评价”建立了一套指标和分区规则。利用人工神经网络建立土地利用演变学习算法，运用融入自适应惯性机制的元胞自动机进行时空模拟，在高分辨率下对全省不同类型国家公园进行空间识别，并运用收缩—扩张原理对识别出的区域进行对比、修正和优化，提出了云南省国家公园未来布局的综合方案。结果表明，云南省国家公园主要集中在三江地区和横断山脉，即滇西和滇西南地区。这三类区域可作为未来国家公园规划和保护的重点区域。本文建立的国家公园类型划分与空间识别的一般且值得推广的研究范式可为全国应用提供参考。
