地理学报 ›› 2022, Vol. 77 ›› Issue (3): 736-756.doi: 10.11821/dlxb202203016

• 碳收支与生态系统服务 • 上一篇    

滇东南喀斯特山区生态系统服务时空格局及功能分区

赵筱青1(), 石小倩1, 李驭豪1, 李益敏1(), 黄佩1,2   

  1. 1.云南大学地球科学学院,昆明 650500
    2.云南大学国际河流与生态安全研究院,昆明 650500
  • 收稿日期:2021-02-22 修回日期:2022-01-04 出版日期:2022-03-25 发布日期:2022-05-25
  • 通讯作者: 李益敏(1965-), 女, 云南昆明人, 硕士, 研究员, 主要从事3S技术在土壤侵蚀与地质灾害中的应用研究。E-mail: liyimin1965@163.com
  • 作者简介:赵筱青(1969-), 女, 云南大理人, 博士, 教授, 主要从事土地生态学及国土空间研究。E-mail: xqzhao@ynu.edu.cn
  • 基金资助:
    国家自然科学基金项目(42061052);国家自然科学基金项目(41361020);国家自然科学基金项目(40961031);云南省科技厅—云南大学联合基金(2018FY001(-017));云南大学研究生创新人才培养项目(C176230200)

Spatio-temporal pattern and functional zoning of ecosystem services in the karst mountainous areas of southeastern Yunnan

ZHAO Xiaoqing1(), SHI Xiaoqian1, LI Yuhao1, LI Yimin1(), HUANG Pei1,2   

  1. 1. School of Earth Sciences, Yunnan University, Kunming 650500, China
    2. Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
  • Received:2021-02-22 Revised:2022-01-04 Published:2022-03-25 Online:2022-05-25
  • Supported by:
    National Natural Science Foundation of China(42061052);National Natural Science Foundation of China(41361020);National Natural Science Foundation of China(40961031);Joint Fund of Yunnan University, Department of Science and Technology of Yunnan Province(2018FY001(-017));Graduate Innovative Talents Training Program of Yunnan University(C176230200)

摘要:

明晰生态系统服务时空格局,划分生态系统服务功能区,将有助于实现土地资源精细化管理。本文以滇东南喀斯特山区典型区域文山市为例,分析2000—2017年食物供给、产水量、植被净初级生产力、土壤保持、生境质量和旅游文化6项生态系统服务的时空格局及其分异特征;运用K-means聚类法识别生态系统服务簇,划分生态系统服务功能区,并提出差异化生态管控措施。结果表明:① 2000—2017年间,除生境质量服务下降外,文山市其他5项服务均呈上升趋势。② 空间分布上,文山市6项生态系统服务高值区集中分布在西部和南部,低值区集中分布在北部和中部;6项服务的冷热点区域空间分布有差异,但整体上具有重叠性。除旅游文化服务的热点和冷点区分布面积占比分别为2.56%和0以外,其他5项服务的冷热点区面积占比均在21%~32%;能够同时提供3项及以上高值生态系统服务功能的区域较少,85.50%的区域只能同时提供2项及以下高值服务功能。③ 根据服务簇聚类结果,文山市划分为生态保护区、生态过渡区、农业主产区和人类生产生活区4类功能区,针对各分区提出差异化生态管控建议。研究结果可为喀斯特山区城市资源利用和国土空间管控提供参考。

关键词: 生态系统服务, 时空分异, K-means聚类分析, 功能分区, 喀斯特山区

Abstract:

Clarifying the changes of spatio-temporal pattern of ecosystem services and dividing the ecosystem service function zoning will help to achieve the fine management of land resources. The study took Wenshan city, a typical area in the karst mountainous southeastern Yunnan, as an example. We identified six types of ecosystem services in Wenshan from 2000 to 2017, namely, the food supply, water production, net primary productivity of vegetation, soil conservation, habitat quality and tourism culture. Then we examined their spatio-temporal patterns and differentiation characteristics. In addition, we used K-means clustering method to identify ecosystem service bundles and ecosystem service functional zones, and proposed differentiated ecological management measures. The results show that: (1) From 2000 to 2017, all the ecosystem services in Wenshan showed an upward trend, with the exception of the habitat quality service. (2) In terms of spatial distribution, the high-value areas of the six types are concentrated in the west and south, while the low-value areas are concentrated in the north and central parts. There are differences in the spatial distribution of hot and cold spots of the six types of ecosystem services, but they overlap on the whole except for the hot and cold spots of tourism culture service, which account for 2.56% and 0%, respectively, the areas of cold and hot spots account for 21% to 32% for the other five types. Only a small number of regions can provide three or more high-value areas of ecosystem services at the same time, and 85.50% of the regions can only provide two or less high-value areas. (3) According to the cluster results of ecosystem service bundles, Wenshan city could be divided into four types of ecosystem service function zones: ecological protection area, ecological transition area, main agricultural production area, and human production and living area. Differentiated ecological management and control suggestions are proposed for each functional area. The research results can provide references for resource utilization and space management in other Karst mountainous cities.

Key words: ecosystem service, spatio-temporal differentiation, K-means cluster analysis, functional zoning, Karst mountainous areas