地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (4): 469-477.doi: 10.3724/SP.J.1047.2015.00469

• • 上一篇    下一篇

城市扩张驱动下植被净第一性生产力动态模拟研究——以广东省为例

裴凤松1(), 黎夏2, 刘小平1,2, 夏庚瑞1   

  1. 1. 江苏师范大学城市与环境学院, 徐州 221116
    2. 中山大学地理科学与规划学院, 广州 510275
  • 收稿日期:2014-11-17 修回日期:2014-12-29 出版日期:2015-04-10 发布日期:2015-04-10
  • 作者简介:

    作者简介:裴凤松(1982-),男,博士,主要从事城市扩张、气候变化及陆地碳循环模拟研究。E-mail:peifs@foxmail.com

  • 基金资助:
    国家自然科学基金项目(41401438)

Dynamic Simulation of Urban Expansion and Their Effects on Net Primary Productivity: A Scenario Analysis of Guangdong Province in China

PEI Fengsong1,*(), LI Xia2, LIU Xiaoping1,2, XIA Gengrui1   

  1. 1. School of Urban and Environmental Sciences, Jiangsu Normal University, Xuzhou 221116, China
    2. Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
  • Received:2014-11-17 Revised:2014-12-29 Online:2015-04-10 Published:2015-04-10
  • Contact: PEI Fengsong E-mail:peifs@foxmail.com
  • About author:

    *The author: SHEN Jingwei, E-mail:jingweigis@163.com

摘要:

城市间相互作用对城市扩张时空演变及其植被碳循环效应具有重要影响。本文将城市相互作用因子引入到元胞自动机(CA)的城市扩张模拟中,使用极限学习机(ELM)来自动获取CA的转换规则,并提出了ELM-CA模型;结合Biome-BGC模型,以广东省为例,对未来城市扩张及其植被净第一性生产力(NPP)效应进行耦合研究。结果表明:ELM-CA模型无需人工确定各变量权重大小,在不同类型变量的参数获取方面具有优势。通过嵌入城市间相互作用,ELM-CA模型能较好地模拟广东省城市用地扩张过程和格局。另外,广东省城市用地扩张对植被NPP具有重要影响,主要表现为城市用地的增加显著地降低植被NPP。按2000-2005年间城市扩张趋势,到2020年,由城市用地扩张导致的植被NPP降低约占广东省植被NPP的1.79%。引导城市合理扩张,对于维持生态系统碳平衡、促进社会经济的可持续发展具有重要意义。

关键词: 空间相互作用, 极限学习机, 元胞自动机, 净第一性生产力

Abstract:

Spatial interactions between multiple cities are important to the temporal and spatial evolution of urban expansion, and even significant to the carbon cycle. In this paper, an ELM-CA model was proposed by introducing extreme learning machine (ELM) into cellular automata (CA) to obtain the CA’s conversion rules. Taking Guangdong Province as an example, the effects of urban expansion on net primary productivity (NPP) were investigated by coupling Biome-BGC with the ELM-CA model. To represent the close interconnections between different cities, their spatial interactions were explicitly embedded in the ELM-CA model. Our results indicated that: the ELM-CA model could simulate the urban expansions in Guangdong Province at a high accuracy. In addition, the urban expansions exhibited crucial impacts on the NPP in Guangdong, which reduced the vegetation NPP evidently. According to the inertial trends of the urban expansion from 2000 to 2005, we found that the urban land development in 2020 may cause a reduction in NPP, which had taken up about 1.79% of the total provincial NPP of Guangdong. In summary, a reasonable guidance on the planning of future urban expansion is critical for the maintenance of carbon balance and climate change.

Key words: spatial interaction, extreme learning machine, cellular automata, net primary productivity