土地利用动态模拟模型的构建及其应用——以北京市海淀区为例

doi:10.11821/xb200406028

地理学报 ›› 2004, Vol. 59 ›› Issue (6): 1037-1047.doi: 10.11821/xb200406028

土地利用动态模拟模型的构建及其应用——以北京市海淀区为例

段增强¹, P.H.Verburg², 张凤荣¹, 宇振荣¹

1. 中国农业大学资源与环境学院,北京 100094;
2. 瓦赫宁恩大学环境系,荷兰瓦赫宁恩

收稿日期:2004-06-10 修回日期:2004-08-20 出版日期:2004-11-25 发布日期:2004-11-25
通讯作者: 张凤荣, 男, 教授, 博士生导师, 主要从事土地利用管理及土地利用可持续评价。
作者简介:段增强 (1972-), 男, 讲师, 在读博士, 从事土地、地理信息系统及资源环境建模研究。 E-Mail:duanzq@cau.edu.cn
基金资助:
北京市自然科学基金项目 (6031001; 4022005); 国土资源部科技专项 (2001010102); 北京市科委项目(955421000)

Construction of a Land-use Change Simulation Model and Its Application in Haidian District, Beijing

DUAN Zengqiang¹, P. H. Verburg², ZHANG Fengrong¹, YU Zhenrong¹

1. Resources and Environment College, China Agricultural University, Beijing 100094, China;
2. Department of Environmental Sciences, Wageningen University, P.O.Box 37, 6700 AA Wageningen, The Netherlands

Received:2004-06-10 Revised:2004-08-20 Online:2004-11-25 Published:2004-11-25
Supported by:
Natural Science Foundation of Beijing, No.6031001; No.4022005; China Ministry of Land and Resources LUCC Project, No.2001010102; Science and Technology Commission of Beijing,, No.955421000

摘要/Abstract

摘要：

CLUE-S模型是一个基于经验统计原理的模拟多土地利用类型空间变化的动态模拟模型，它在世界多个国家和地区的区域尺度农业、森林为主的土地利用变化模拟中得到应用。作者对CLUE-S模型进行了改进，改进后的模型 (CLUE-SII) 引入了动态计算的邻域分析因子，可以对土地利用变化中的自发过程、自组织过程和土地利用类型间的竞争进行模拟，还可以根据研究区域特点构建不同的模拟方案，在这些模拟方案中，局地因子和邻域因子在土地利用变化中的作用方式不同。应用CLUE-SII对北京市海淀区1991~2001年土地利用变化进行了多方案模拟，结果表明邻域因子对城镇用地变化具有重要作用，其中将邻域因子看作自发过程放大因子的模拟方案获得了较好的模拟结果，整图符合比达到77%，其中城镇用地符合比达到82%，Kappa值达到0.754。CLUE-SII在北京市海淀区的应用实例表明，该模型可以对高分辨率和多土地利用类型下的城市扩展进行有效模拟，扩展了CLUE-S的应用领域；通过构建多模拟方案，不但可以探索最佳的模拟结果，还可以研究和分析不同土地利用驱动因子在土地利用变化中的作用模式。

关键词: CLUE-S, 改进, 土地利用, 模拟, 模型, 北京

Abstract:

CLUE-s (Conversion of Land Use and its Effects in small extent) is a model for simulating spatial changes in land use. The model was applied for regional agriculture and forest land-use system simulations in many countries. The author extended the CLUE-s model with a dynamic neighborhood analysis. The extended model (CLUE-sII) could simulate the self-organizational processes, spontaneous processes and the competition processes of the land-use changes. CLUE-sII can also implement different simulation schemes according to the characteristics of the study area. The local factors and the neighborhood factors play different roles in each scheme. Therefore, the researchers could find the best scheme for the specific study area. Land use change from 1991 to 2001 is simulated using CLUE-sII for Haidian District, Beijing. The results indicate that neighborhood factor analyse is important to simulate urban land-use changes and the scheme that took the neighborhood factor as a magnifying factor to the spontaneous processes obtained the best results, with which the whole map correspondence proportion gained 77% and the Kappa of urban attained 0.754. The case study in this paper showed that CLUE-sII could simulate urban sprawl with multiple land-use types at a high spatial and temporal resolution. Through constructing multiple simulation schemes, one can not only find the best simulation performance, but also could obtain more information on the importance of different land-use drivers.

Key words: CLUE-sII, model, land-use, simulation, Beijing

段增强,P.H.Verburg,张凤荣,宇振荣. 土地利用动态模拟模型的构建及其应用——以北京市海淀区为例[J]. 地理学报, 2004, 59(6): 1037-1047.

DUAN Zengqiang, P. H. Verburg, ZHANG Fengrong, YU Zhenrong. Construction of a Land-use Change Simulation Model and Its Application in Haidian District, Beijing[J]. Acta Geographica Sinica, 2004, 59(6): 1037-1047.

参考文献

[1] Vitousek P, 1994. Beyond global warming: ecology and global change. Ecology, 1975: 1861-1876.

[2] Vitousek P M, Mooney H A, Lubchenco J et al. Human domination of earth's ecosystems. Science, 1997, 277: 494-499.

[3] Dawn C Parker, Steven M Manson, Marco A Janssen et al. Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review. 2002.

[4] Lambin E F, Baulies X, Bockstael N et al. Land-use and land-cover change (LUCC): Implementation Strategy. A Core Project of the International Geospere-Biosphere Programme and the International Human Dimensions Programme on Global Environmental Change. IGBP Report 48. IHDP Report 10. IGBP, Stockholm, 1999, 125.

[5] Veldkamp A, Lambin E F. Predicting land-use change. Agriculture, Ecosystems and Environment, 2001, 85: 1-6.

[6] Schneider S H. Integrated assessment modeling of global climate change: transparent rational tool for policy making or opaque screen hiding value-laden assumptions? Environmental Modeling and Assessment, 1997, 2: 229-249.

[7] Walsh S J, Crawford T W, Crews-Meyer K A. A multi scale analysis of land use/land cover change and NDVI Variation in Nang Rong District, Northeast Thailand. Agriculture, Ecosystems and Environment, 2001, 85: 47-64.

[8] Verburg P H, Soepboer W, Limpiada R et al. Land use change modelling at the regional scale: the CLUE-S model. Environmental Management, 2002, 30. (in press)

[9] Fulong Wu. International Journal of Geographical Information Science, 2002, 16(8): 795-818.

[10] Jianquan Cheng. Modelling spatial & temporal urban growth. Doctoral Dissertation, 2003.

[11] Jin Chen et al. Assessment of the urban deveopment plan of Beijing by using a CA-based urban growth model. Photogrammetric Engineering & Remote Sensing, 2002, 68(10): 1063-1071.

[12] Verburg P H, A Veldkamp, G H J de Koning et al. A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use. Ecological Modelling, 1999, 116: 45-61.

[13] Verburg P H, Schot P, Dijst M et al. Land-use change modelling: current practice and research priorities. Geojournal, 2004, in press.

[14] Verburg P H, A Veldkamp, J Bouma. Land use change under conditions of high population pressure: the case of Java. Global Environmental Change, 1999, 9: 303-312.

[15] Verburg P H, A Veldkamp, G H J de Koning et al. A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use. Ecological Modelling, 1999, 116: 45-61.

[16] Verburg P H, Youqi Chen, Welmoed Soepboer et al. GIS-based modeling of human-environment interactions for natural resource management applications in Asia. In: 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4): Problems, Prospects, and Research Needs, Banff, Alberta, Canada. Boulder: Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, 2000. http: // www. colorado. edu/research /cires /Banff /upload/337/.

[17] Verburg P H, Y Q Chen. Multiscale characterization of land-use patterns in China. Ecosystems, 2000, 3(4): 369-385.

[18] Verburg P H, Ton de Nijs, Jan Ritsema van Ec. A method to analyse neighbourhood characteristics of land use patterns, computer, environment and urban system, 2004, in press).

[19] Ritsema van Eck J R, de Jong T. Accessibility analysis and spatial competition effects in the context of GIS-supported service location planning. Computers, Environment and Urban Systems, 1999, 23: 75-89.

[20] Costanza R. Model goodness of fit: a multiple resolution procedure. Ecol. Model., 1989, 47: 199-215.

[21] Veldkamp A, Fresco L O. Reconstructing land use drivers and their spatial scale dependence for Costa Rica. Agricultural Systems, 1997, 55: 19-43.

[22] Hoshino S. Statistical analysis of land-use change and driving forces in the Kansai District, Japan. WP-96-120. 1996. Laxenburg, IIASA. IIASA working papers.

土地利用动态模拟模型的构建及其应用——以北京市海淀区为例

Construction of a Land-use Change Simulation Model and Its Application in Haidian District, Beijing

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

[1]	袁玉, 方国华, 陆承璇, 颜敏. 基于景观生态学的城市化背景下洪灾风险评估[J]. 地理学报, 2020, 75(9): 1921-1933.
[2]	杨微石, 戴尔阜, 郑度, 董玉祥, 尹乐, 马良, 王隽雄, 潘理虎, 秦世鹏. 基于多主体模型的典型区域退耕还林工程实施空间模拟[J]. 地理学报, 2020, 75(9): 1983-1995.
[3]	彭立, 邓伟, 谭静, 林磊. 横断山区水土资源利用与经济增长的匹配关系[J]. 地理学报, 2020, 75(9): 1996-2008.
[4]	敖荣军, 常亮. 基于结构方程模型的中国县域人口老龄化影响机制[J]. 地理学报, 2020, 75(8): 1572-1584.
[5]	胡宪洋, 白凯, 花菲菲, 王利尖. 西安曲江新区移民群体的地方意义建构与检验[J]. 地理学报, 2020, 75(8): 1773-1789.
[6]	高晓路, 吴丹贤, 颜秉秋. 北京城市老年贫困人口识别与空间分布[J]. 地理学报, 2020, 75(8): 1557-1571.
[7]	王开泳, 冯润东. 行政区划调整对政区位势的影响与定量化测度[J]. 地理学报, 2020, 75(8): 1617-1632.
[8]	邓铭江, 龙爱华, 李江, 邓晓雅, 张沛. 西北内陆河流域“自然—社会—贸易”三元水循环模式解析[J]. 地理学报, 2020, 75(7): 1333-1345.
[9]	孙思奥, 王晶, 戚伟. 青藏高原地区城乡虚拟水贸易格局与影响因素[J]. 地理学报, 2020, 75(7): 1346-1358.
[10]	王彦君, 吴保生, 钟德钰. 黄河下游主槽断面形态对水沙变化响应过程的模拟[J]. 地理学报, 2020, 75(7): 1494-1511.
[11]	刘瑜, 姚欣, 龚咏喜, 康朝贵, 施迅, 王法辉, 王姣娥, 张毅, 赵鹏飞, 朱递, 朱欣焰. 大数据时代的空间交互分析方法和应用再论[J]. 地理学报, 2020, 75(7): 1523-1538.
[12]	瞿诗进, 胡守庚, 李全峰. 中国城市建设用地转型阶段及其空间格局[J]. 地理学报, 2020, 75(7): 1539-1553.
[13]	魏素豪, 李晶, 李泽怡, 宗刚. 中国农业竞争力时空格局演化及其影响因素[J]. 地理学报, 2020, 75(6): 1287-1300.
[14]	王少剑, 高爽, 黄永源, 史晨怡. 基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测[J]. 地理学报, 2020, 75(6): 1316-1330.
[15]	孙才志, 马奇飞, 赵良仕. 基于GWR模型的中国水资源绿色效率驱动机理[J]. 地理学报, 2020, 75(5): 1022-1035.