地理研究

地理研究 ›› 2009, Vol. 28 ›› Issue (1): 19-26.doi: 10.11821/yj2009010004

• 地球信息科学 • 上一篇    下一篇

基于多源遥感的聚落与多级人口统计 数据的关系分析

杨存建1,2, 白 忠1, 贾月江1, 程 熙1, 邓丽丽1   

  1. 1. 四川师范大学西南土地资源评价与监测教育部重点实验室,遥感与GIS应用研究中心,成都 610068;
    2. 四川师范大学地理与资源科学学院,成都 610054
  • 收稿日期:2008-05-12 修回日期:2008-09-05 出版日期:2009-01-25 发布日期:2009-01-25
  • 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: yangcj9802@sina.com.
  • 基金资助:

    四川省青年基金项目(08ZQ026-047);国家自然科学基金项目(40771144);四川省教育厅重大培育项目(07ZZ029)

Study on the relationship between residential area from multi-source remote sensing images and multi-level population data

YANG Cun-jian1,2, BAI Zhong1, JIA Yue-jiang1, CHEN Xi1, DENG Li-li1   

  1. 1. Research Center of Remote Sensing and GIS Applications, Education Ministry Key Lab of Southwest Land Resources Evaluatiion and Monitor, Sichuan Normal University, Chengdu 610068, China;
    2. The Faculty of Geography and Resources Sciences, Sichan Normal University, Chengdu 610054, China
  • Received:2008-05-12 Revised:2008-09-05 Online:2009-01-25 Published:2009-01-25
  • Supported by:

    四川省青年基金项目(08ZQ026-047);国家自然科学基金项目(40771144);四川省教育厅重大培育项目(07ZZ029)

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摘要：

在四川省市州、区县和典型村等三级尺度上,探讨了基于多源遥感的聚落面积与多级人口统计数据的关系。首先,从LANSAT TM影像中提取农村和城镇聚落信息,从Quickbird 影像上提取农村聚落及其房屋地基信息。其次,通过叠加统计得到各级统计单元内的聚落面积;再次,在四川省市州和区县尺度上,分别对城乡聚落面积和总人口数、城镇聚落面积和非农业人口数、农村聚落面积和农业人口数等进行相关性分析, 城镇聚落和非农业人口数的相关系数最高,分别为0.962和0.791,并建立了基于城镇聚落面积的非农业人口数估算模型,其模型的判定系数分别为0.926和0.625;最后,在村级尺度上,对农村聚落及其房屋地基面积与农村人口数之间的相关性进行分析,其相关系数分别为0.806和0.825,分别建立基于农村聚落及其房屋地基面积的农村人口数估算模型,其模型的判定系数分别为0.65和0.68。研究表明,LANDSAT TM适用于大尺度的非农业人口估算,估算效果随尺度的降低而有所降低;Quickbird适合于精细尺度的农业人口估算。

关键词: 人口, 聚落, 相关分析, 遥感

Abstract:

The relationship between the residential area extracted from multi-source remote sensing images and the population data at the three levels of city and prefecture, county and village in Sichuan province is discussed in this paper. This includes the following steps. Firstly, the rural and urban residential areas are extracted from Landsat TM images in Sichuan province, and the rural residential area and its building land are extracted from Quickbird Images in Juntun town, Xindu district, Chengdu City. Secondly, the residential areas for each unit of the three levels are obtained by overlaying and statistical analysis. Thirdly, the correlation relationship between the total residential areas and the total population, urban and town residential areas and the non-farm population, and the rural residential area and the farm population are analyzed respectively for city and county levels. The non-farm population strongly relates to the urban and town residential areas for city level with the correlation coefficient of 0.962 and county level with the correlation coefficient of 0.791.The non-farm population estimation models based on the urban and town residential areas are formulated respectively for the city and county levels by using regression analysis, whose judgment coefficients are respectively 0.926 and 0.625. Finally, the correlation relationship between the rural population, rural residential area and its building land are analyzed at the village level, and their correlation coefficients are respectively 0.806 and 0.825. The farm population estimation models based on the rural residential area and its building land are formulated by using regression analysis, whose judgment coefficients are respectively 0.65 and 0.68. It is shown that Landsat TM images are suitable for the estimation of the non-farm populations on a large scale, and Quickbird images are suitable for the estimation of the farm population on a small scale.

Key words: population, residential area, correlation analysis, remote sensing