| 本文已被：浏览 430次 下载 173次 |
码上扫一扫！ |
|
|
| 农业区生态系统服务的影响因素及空间优化分析——以北京市为例 |
|
杨婉清1,2,3，王晨睿2,3，孙晓2,3，杨鹏1,2,3，王妍4
|
|
1.陕西地建-西安交大土地工程与人居环境技术创新中心,西安 710049;2.北方干旱半干旱耕地高效利用全国重点实验室,北京 100081;3.中国农业科学院农业资源与农业区划研究所,北京 100081;4.北京市智慧水务发展研究院,北京 100036
|
|
| 摘要: |
| 目的 探索北京市农业区生态系统服务的主要影响因素，并提出空间优化策略，促进农业区生态系统可持续性。方法 文章利用InVEST、ROS等模型定量评估北京市多种生态系统服务，在此基础上采用K-means聚类方法识别生态系统服务簇，从而划分出北京市农业功能区。采用地理探测器分析其生态系统服务主要影响因素，并基于影响因素进行多情景优化，提出改善农业区生态系统服务的土地利用组成比例和空间配置方案。结果 （1）北京市农业区主要分布在东部、南部地区，占全市面积的26%；（2）农业区的气候调节服务主要受坡度影响，生境质量服务主要受植被指数影响，粮食生产服务受景观多样性、地区生产总值、农用化肥施用量等因素的综合影响；（3）耕地保护情景下的粮食生产服务提升更为显著；综合优化情景下的生境质量、休闲娱乐以及综合生态系统服务提升更为显著。结论 探讨了北京市农业区生态系统服务时空演变、驱动机制及多种空间优化方案，为北京市及其他特大型城市的生态系统服务提升与农业区景观可持续规划提供参考依据。 |
| 关键词: 生态系统服务 农业区 景观格局 影响因素 空间优化 |
| DOI：10.7621/cjarrp.1005-9121.20240111 |
| 分类号:S181 |
| 基金项目:陕西地建-西安交大土地工程与人居环境技术创新中心开放基金资助项目“农田天空地立体化监测关键技术与平台研究”（2021WHZ0072）；中国科协青年人才托举工程项目“农业生态系统服务定量评估与空间优化”（2021QNRC001）；国家自然科学基金面上项目“面向城乡融合的京津冀生态系统服务供—流—需耦合机制及安全格局优化”（42271113） |
|
| IMPACTS OF LANDSCAPE PATTERN ON ECOSYSTEM SERVICES AND THE SPATIAL OPTIMIZATION OF AGRICULTURAL PRODUCTION REGION——A CASE STUDY OF BEIJING |
|
Yang Wanqing1,2,3, Wang Chenrui2,3, Sun Xiao2,3, Yang Peng1,2,3, Wang Yan4
|
|
1.Technology Innovation Center for Land Engineering and Human Settlements，Shaanxi Land Engineering Construction Group Co., Ltd and Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China;2.State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Beijing 100081, China;3.Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;4.Beijing Research Institute of Smart Water, Beijing 100036, China
|
| Abstract: |
| This paper aims to enhance the ecological sustainability of agricultural region by analyzing the driving factors of ecosystem services (ESs) and put forward spatial optimization strategies. Taking Beijing as a case study, we assessed multiple ESs by adopting the integrated valuation of ecosystem services and tradeoffs (InVEST) and recreation opportunity spectrum (ROS) models, and identified ESs bundles through K-means clustering method, then identified the agricultural functional region. Moreover, the dominant factors affecting ESs in agricultural region were explored by using geographical detectors. Based on the influencing factors, we developed different optimization scenarios that could improve ESs by exploring various strategies of land use composition and spatial configuration. The results showed that: (1) The agricultural region in Beijing was mainly distributed in the eastern and southern areas, accounting for 26% of the total area. (2) In agricultural region, carbon storage was mainly influenced by slope, while habitat quality was mainly influenced by normalized vegetation index. Food production was influenced by landscape diversity, gross domestic product and chemical fertilizer. (3) Food production increased the most under the cropland conservation scenario, while habitat quality, recreation opportunity, and most of ESs all increased under the comprehensive optimization scenario. In conclusion, the results of spatial-temporal evolution, driving mechanism, and spatial optimization in this study can provide theoretical and practical basis for ESs improvement and agricultural landscape sustainable planning in Beijing and other metropolitan areas. |
| Key words: cosystem services (ESs) agricultural region landscape pattern impact factors spatial optimization |
