地理信息科学

基于耦合地理模拟优化系统GeoSOS的农田保护区预警

展开
  • 1. 中山大学地理科学与规划学院,广州510275;
    2. 中山大学管理学院,广州510275
陈逸敏, 男, 博士研究生。研究方向为地理模拟与优化。E-mail: bikeystuart@yahoo.com.cn

收稿日期: 2010-02-26

  修回日期: 2010-04-01

  网络出版日期: 2010-09-20

基金资助

国家自然科学基金重点资助项目(40830532); 国家杰出青年基金资助项目(40525002);国家自然科学基金资助项目(40901187)、广东省自然科学基金项目(9451027501002471)

Coupling Geosimulation and Optimization (GeoSOS) for Zoning and Alerting of Agricultural Conservation Areas

Expand
  • 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
    2. School of Business, Sun Yat-sen University, Guangzhou 510275, China

Received date: 2010-02-26

  Revised date: 2010-04-01

  Online published: 2010-09-20

Supported by

Foundation: Key National Natural Science Foundation of China, No.40830532; National Outstanding Youth Foundation of NSF of China, No.40525002; National Natural Science Foundation of China, No.40901187; Guangdong Provincial Natural Science Foundation, No. 9451027501002471]

摘要

依据农田保护区规划与城市发展模拟相耦合的新思路,开展农田保护区预警研究。以广东番禺为实验区,首先利用基于多智能体的空间优化配置模型(AgentLA) 自动生成农田保护区范围。AgentLA模型能避免产生分散、破碎的空间格局,有利于保护区的管理和维护。通过地理模拟优化系统GeoSOS中的神经网络CA模型对研究区2025 年的城市发展格局进行三个情景的模拟:低速增长情景、基准情景和高速增长情景。最后利用GIS空间分析方法将预测结果与农田保护区相结合,提取农田保护与城市扩张产生冲突的区域。总体上,冲突的严重程度随着空间上城市扩张强度的增大而加剧,高速增长情景中冲突区域的面积较大。冲突区域的存在一方面说明了农田保护区需要相应的法律、法规支持,否则可能会因城市扩张而被侵占;另一方面也反映了农田保护与地区发展之间的冲突。因此,可以根据所提取的冲突区域面积大小、空间位置等特征,采取某种形式补偿,以此取得农田保护与地方利益之间的平衡。

本文引用格式

陈逸敏, 黎夏, 刘小平, 李少英 . 基于耦合地理模拟优化系统GeoSOS的农田保护区预警[J]. 地理学报, 2010 , 65(9) : 1137 -1145 . DOI: 10.11821/xb201009011

Abstract

Rapid urban expansion in China has caused the significant loss of agricultural land. Specific efforts are made by the country to protect its scarce and valuable agricultural land. The State Council promulgated the Ordinance for the Protection of Primary Agricultural Land in 1994 to promote the zoning of agricultural conservation areas. However, the agricultural land still underwent a rapid loss due to the urgent demand of local economic development. The conflict between protection and local development calls for solution. This paper provides a new thought of integrating urban development into the zoning of agricultural conservation areas, and Panyu district in Guangdong province is selected for a case study. The proposed Agent-based Land Allocation (AgentLA) model is used to automatically generate the spatial distribution of agricultural conservation areas. Compared to conventional methods, the resulted pattern from AgentLA is more compact that favors routing maintenance of the conservation areas. Moreover, the future development of the region is predicted using Geographical Simulation and Optimization System (GeoSOS), a platform for geosimulation. Finally, GIS provides the function to combine the zoning result and the predicted development pattern in order to recognize the spatial distribution of the conflict areas.

参考文献


[1] United Nations Population Fund. Population, Resources and the Environment: The Critical Challenges. New York: United Nations, 1991.

[2] Seto K C, Woodcock C E, Song C et al. Monitoring land-use change in the Pearl River Delta using Landsat TM. International Journal of Remote Sensing, 2002, 23(10): 1985-2004.

[3] Seto K C, Kaufmann R K, Wood-cock C E. Landsat reveals China's farm-land reserves, but they're vanishing fast. Nature, 2000, 406: 121.

[4] Yeh A G O, Li X. Economic development and agricultural land loss in the Pearl River Delta, China. Habitat International, 1999, 23(3): 373-390.

[5] Yeh A G O, Li X. An integrated remote sensing and GIS approach in the monitoring and evaluation of rapid urban growth for sustainable development in the Pearl Rive Delta, China. International Planning Studies, 1997, 2: 193-210.

[6] Niu Fengrui, Pan Jiahua. Urban Development in China: 2007. Beijing: China City Press, 2008.

[牛凤瑞, 潘家华. 中国 城市发展报告2007. 北京: 中国城市出版社, 2008.]

[7] Li X, Yeh A G O. Study on zoning of agricutural land protection using cellular automata model. China Environmental Science, 2000, 20(4): 318-322.

[黎夏, 叶嘉安. 单元自动演化模型自动生成农田保护区的研究. 中国环境科学, 2000, 20(4): 318-322.]

[8] Li Xia, Yeh A G O, Liu Xiaoping et al. Geographical Simulation System. Beijing: Science Press, 2007.

[黎夏, 叶嘉安, 刘小平等. 地理模拟系统. 北京: 科学出版社, 2007.]

[9] Stewart T J, Janssen R, van Herwijnen M. A genetic algorithm approach to multiobjective land use planning. Computers & Operations Research, 2004, 31: 2293-2313.

[10] Cova T J, Church R L. Exploratory spatial optimization in site search: A neighborhood operator approach. Computers & Operations Research, 2000, 24: 401-419.

[11] Brookes C J. A parameterized region-growing programme for site allocation on raster suitability maps. International Journal of Geographical Information Science, 1997, 11: 375-396.

[12] Chen Y M, Li X, Liu X P et al. An agent-based model for optimal land allocation (AgentLA) with a contiguity constraint. International Journal of Geographical Information Science, 2010, 24(8): 1269-1288

[13] Tobler W. A computer movie simulating urban growth in the Detroit region. Economic Geography, 1970, 46: 234-40.

[14] Li Xia, Liu Xiaping, He Jinqiang et al. A geographical simulation and optimization system based on coupling strategies. Acta Geographica Sinica, 2009, 64(8): 1009-1018.

[黎夏, 刘小平, 何晋强等. 基于耦合的地理模拟优化系统. 地理学报, 2009, 64(8): 1009-1018.]

[15] Wu F. Calibration of stochastic cellular automata: The application to rural-urban land conversions. International Journal of Geographical Information Science, 2002, 16(8): 795-818.

[16] Li Xia, Yeh A G O. Neural-network-based cellular automata for realistic and idealized urban simulation. Acta Geographica Sinica, 2002, 57(2): 159-166.

[黎夏, 叶嘉安. 基于神经网络的单元自动机CA及真实和优化的城市模 拟. 地理学报, 2002, 57(2): 159-166.]

[17] Li X, Yeh A G O. Data mining of cellular automata's transition rules. International Journal of Geographical Information Science, 2004, 18: 723-744.

[18] Li Xia, Liu Xiaoping. Case-based cellular automaton for simulating urban development in a large complex region. Acta Geographica Sinica, 2007, 62(10): 1097-1109.

[黎夏, 刘小平. 基于案例推理的元胞自动机及大区域城市演变模 拟. 地理学报, 2007, 62(10): 1097-1109.]

[19] Liu Xiaoping, Li Xia, Zhang Xiaohu et al. Embedding urban planning objective by integr ated ar tificial immune system and cellular automata. Acta Geographica Sinica, 2008, 63(8): 882-894.

[刘小平, 黎夏, 张啸虎等. 人工免疫系 统与嵌入规划目标的城市模拟及应用. 地理学报, 2008, 63(8): 882-894.]

[20] Long Ying, Han Haoying, Mao Qizhi. Establishing urban growth boundaries using constrained CA. Acta Geographica Sinica, 2009, 64(8): 999-1008.

[龙瀛, 韩昊英, 毛其智. 利用约束性CA 制定城市增长边界. 地理学报, 2009, 64(8): 999-1008.]

[21] Li X, Yeh A G O. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 2002, 16: 323-343.

[22] Quinlan J R. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.

[23] Yan Xiaopei, Mao Jiangxing, Pu Jun. Research on the human dimensions of land use changes in the mega-urban region:. Acta Geographica Sinica, 2006, 61(6): 613-623.

[闫小培, 毛蒋兴, 普军. 巨型城市区域土地利用变化的人文 因素分析. 地理学报, 2006, 61(6): 613-623.]

文章导航

/