地理研究

地理研究 ›› 2008, Vol. 27 ›› Issue (1): 100-108.doi: 10.11821/yj2008010011

• 地球信息科学 • 上一篇    下一篇

基于遥感的成都市及其周边城镇的扩展

杨存建1,2, 张 果1, 陈 军3, 邓丽丽1, 王小燕1   

  1. 1. 四川师范大学软件重点实验室遥感与GIS应用研究中心,成都 610068;
    2. 电子科技大学地表空间信息技术研究所,成都 610054;
    3. 成都信息工程学院,成都 610225
  • 收稿日期:2007-02-11 修回日期:2007-07-12 出版日期:2008-01-25 发布日期:2008-01-25
  • 作者简介:杨存建(1967-),男,成都市人,博士,教授。主要从事遥感和地理信息系统应用研究。 E-mail :yangcj2008@126.com.
  • 基金资助:

    四川省教育厅重点项目(2002A081);四川省杰出青年基金项目(2002)1号和(2003);四川省软件重点实验室项目支持

The research of the sprawl of Chengdu city and its peripheral towns by using remote sensing

YANG Cun-jian1,2, ZHANG Guo1, CHEN Jun3, DENG Li-li1, WANG Xiao-yan1   

  1. 1. Research Center of Remote Sensing and GIS Applications, Sichuan Key Lab of Software, Sichuan Normal University, Chengdu 610068,China;
    2. Institute of Geo-surface Information Technology, University of Electronic and Technology of China,Chengdu 610054, China;
    3. Chengdu University of Information Technology, Chengdu,610225, China
  • Received:2007-02-11 Revised:2007-07-12 Online:2008-01-25 Published:2008-01-25
  • Supported by:

    四川省教育厅重点项目(2002A081);四川省杰出青年基金项目(2002)1号和(2003);四川省软件重点实验室项目支持

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被引次数

84 | 20

摘要：

利用1987年和2000年的两期LANDSAT TM卫星影像进行了成都市及其周边城镇的扩展研究。首先,将两期卫星影像与GIS数据配准。然后,在对遥感影像信息机理分析的基础上,发现并定义了反映建筑物覆盖的差值建筑物覆盖指数,并利用该指数建立了成都市及其周边城镇空间信息提取模型,并利用该指数和模型提取了两期成都市及其周边城镇空间分布数据。将两期数据进行叠加,从而得到成都市及其周边城镇的扩展数据。再次,利用行政界线、三环和绕城高速等界线与扩展数据进行叠加和掩模,从而得到各分析单元的扩展动态数据,并对其进行统计分析。研究表明:利用差值建筑物覆盖指数和阈值法能有效地提取两期的成都市及其周边城镇的空间信息。利用GIS技术能有效地对各单元的扩展情况进行分析。就成都市的扩展而言,三环内的扩展倍数为0.9。其市区的扩展倍数低于周边城镇的扩展倍数。

关键词: 卫星遥感, 市和城镇信息提取, 市和城镇扩展

Abstract:

It is of great importance to the study of the dynamic extension of a city and its peripheral towns for simulating, predicting, adjusting and controlling urban extension, and for rationally protecting the farmland and using land resources. The extension of Chengdu and its peripheral towns is studied here by using LANDSAT TM satellite images respectively acquired in 1987 and 2000. Firstly, The images are registered to the GIS data. Secondly, the Different Building Index(DBI) is formulated by LANDSAT TM7-TM4 on the basis of analyzing the remote sensing image mechanism of the cities and towns and their background, which reflects the situation of construction. The model of extracting Chengdu city and its peripheral towns from DBI image is formulated by the threshold method. The spatial data of Chengdu city and its peripheral towns for the two periods is extracted from the remote sensing images of the two periods by calculating DBI and applying the model. Thirdly, the sprawl data of Chengdu city and its peripheral towns are obtained by overlaying the spatial data of the two periods. Finally, the sprawl data of each analytical unit obtained by masking or overlaying the sprawl data with the administrative boundary, the third circular road and the highway around the city are analyzed. It is shown that the spatial data of the two periods can be effectively extracted by using DBI and the extraction model. The GIS technology is especially suitable for analyzing the sprawl of the city in each unit. The sprawl area in the highway around the city is about 11132 hm2, which is 1.3 times of its original area. The sprawl area in the third circular road is about 6546 hm2, which is 0.9 hm2 of its original area, and mainly occurrs in the western, northwestern and southwestern parts, and most of which are external extensions. Between the third circular road and the highway around the city, the sprawl mainly occurs in the northwestern and southwestern parts,expanding mainly along the road. The sprawl pattern is mainly caused by the construction of high technology area and the spatial difference of investment and residential environment. In the five districts, Wuhou district is of the highest sprawl, which is 2.5 times of its original area. The sprawl area is over two times of its original area for each peripheral town, which is higher than the sprawl multiples of the city.

Key words: remote sensing of the satellite, extracting the spatial data of city and town, the sprawl of city and town