地理学报 ›› 2004, Vol. 59 ›› Issue (6): 1058-1067.doi: 10.11821/xb200406030

• 城市地理 • 上一篇    下一篇

城市景观格局尺度效应的空间统计规律——以上海中心城区为例

徐建华1,2, 岳文泽1, 谈文琦1   

  1. 1. 教育部地理信息科学重点实验室,华东师范大学地理系,上海200062;
    2. 中国科学院地理科学与资源研究所,北京 100101
  • 收稿日期:2003-12-30 修回日期:2004-06-01 出版日期:2004-11-25 发布日期:2010-09-09
  • 作者简介:徐建华 (1965-), 男, 教授, 博士生导师, 主要从事地理数量方法与GIS方面的科研和教学工作, 出版教材、专著10部, 发表学术论文100余篇。E-mail: jhxu@geo.ecnu.edu.cn
  • 基金资助:

    国家自然科学基金项目(40371092)

A Statistical Study on Spatial Scaling Effects of Urban Landscape Pattern: A Case Study of the Central Area of the External Circle Highway in Shanghai

XU Jianhua1,2, YUE Wenze1, TAN Wenqi1   

  1. 1. The Key Lab of GIScience of the Ministry of Education, PRC, Dept. of Geography, East China Normal University, Shanghai 200062, China;
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2003-12-30 Revised:2004-06-01 Online:2004-11-25 Published:2010-09-09
  • Supported by:

    National Natural Science Foundation of China, No.40371092

摘要:

以上海市外环以内的中心城区为区域背景,基于SPOT全色波段遥感影像数据和GIS技术,运用空间统计分析方法,研究城市景观格局的空间尺度效应。研究结果表明:(1) 城市景观格局的空间自相关性具有显著的尺度依赖性。Moran指数I与Geary系数c对粒度变化的敏感点相同,均为50 m。(2) 城市景观形态具有分形特征,各类景观斑块的分维数对粒度变化的响应不同,它们随着粒度的变化呈非线性变化趋势;在粒度较小时,各类景观斑块之间的分维数差异较大,而随着粒度的增大,其分维数之间的差异逐渐缩小。(3) 城市景观多样性格局,与空间区位及人类活动的空间格局息息相关。在城市中心,主导型景观是经济效益较高的商业文化景观,并且景观斑块聚集度、破碎度大;经济效益较低的农业景观分布在城市边缘区,而且景观类型单一,斑块面积较大,破碎度较小;在由城市中心向边缘过渡的中间地带,景观类型多样,空间格局复杂。(4) 景观多样性具有尺度依赖性。随着幅度的增加,景观多样性指数逐渐增大,多样性的空间格局也显著变化。在0.5 km幅度下,多样性指数的最大值出现在市中心,从市中心向外呈现高低起伏的环状模式扩展,随着幅度增加,多样性指数的高值区向景观类型变化最剧烈的城乡过渡地带转移。(5) 景观多样性的空间变异也具有明显的尺度依赖性。在较小幅度下,总体空间变异主要来自空间自相关的贡献,随机因素贡献较小;而较大幅度掩盖了更小尺度上的变异,导致块金效应增强,总体空间变异中自相关部分的贡献明显下降。

关键词: 城市景观, 尺度效应, 空间自相关, Shannon多样性指数, 半变异函数, 上海

Abstract:

Based on SPOT remote sensing images and GIS, choosing the central area of the external circle highway in Shanghai as a case study area, the paper studied the spatial scaling effect of the urban landscape pattern with different grains and extents. The conclusions are drawn as follows: (1) The spatial autocorrelation of urban landscape pattern depends on different scales within a certain range of scales, and Moran I and Geary c related to the grains, which characterized the spatial autocorrelation of the urban landscape structure, have the same sensitive points to the scaling at the level of 50 m. (2) The patches of all kinds of landscapes have the fractal character. The fractal dimensions of landscapes respond to scaling differently, and the present nonlinear change trends with grains. The fractal dimensions of landscapes are obviously different at a small grain, but the differences become not obvious with the increasing grain. (3) The landscape diversity are closely linked to the location and the pattern of human activities, especially to economic and social activities. Due to the high land cost in the urban center, the dominant landscapes are mainly for business and culture, and their patches have the characters of high congregation and high fragmentation. While agricultural landscapes with low economic benefit can only be located at the fringe or outskirt of the urban area, and they have the characters of simplex, larger patches and less fragmentation. (4) The landscape diversity depends on spatial scale. With the increasing extent, the Shannon diversity index (SHDI) increases and the spatial pattern of landscape varies dramatically. At 0.5 km extent, the maximum of diversity is in the center of the urban area. The landscape diversity is distributed with a ring mode up and down from the center to the outer. With the increasing extent, the maximum of diversity moves to the urban-rural transition zone where landscape types change dramatically. (5) The semivariogram discloses the spatial variance character and internal mechanism of landscape diversity. At a small scale, the spatial variance of diversity is more complicated. The spatial heterogeneity, which is caused by spatial autocorrelation, contributes a lot to the total spatial heterogeneity of terrestrial ecosystem, while the spatial heterogeneity caused by random factors (scale and measure error) contributes less. The increasing scale washes off the detailed variances in a fine scale. The coarse scale may result in more nugget effect and less contribution, which is caused by spatial autocorrelation.

Key words: urban landscape, scaling effect, spatial autocorrelation, fractal dimension, Shannon diversity index, semivariogram