地理科学进展 ›› 2013, Vol. 32 ›› Issue (6): 932-939.doi: 10.11820/dlkxjz.2013.06.010

• 遥感与GIS模型应用 • 上一篇    下一篇

矢量和栅格数据土地利用结构分维值比较——以苏州为例

张晶1, 濮励杰1,2, 朱明1, 许艳1, 李鹏1   

  1. 1. 南京大学地理与海洋科学学院, 南京210023;
    2. 国土资源部土地利用重点实验室, 北京100029
  • 收稿日期:2012-12-01 修回日期:2013-04-01 出版日期:2013-06-25 发布日期:2013-06-25
  • 通讯作者: 濮励杰(1965-),男,江苏吴江人,博士,教授,博士生导师,主要从事土地利用与生态效应研究。E-mail:ljpu@nju.edu.cn E-mail:ljpu@nju.edu.cn
  • 作者简介:张晶(1988-),女,湖北黄冈人,硕士研究生,主要研究方向为土地利用生态效应。E-mail:zhangjing5043@126.com
  • 基金资助:
    国家自然科学基金重点项目(41230751);国家“十二五”科技支撑计划项目(2012BAC01B01)。

Comparison of land-use structure fractal dimension based on vector and raster data: A case study of Suzhou City

ZHANG Jing1, PU Lijie1,2, ZHU Ming1, XU Yan1, LI Peng1   

  1. 1. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China;
    2. Key Laboratory of Land Use, Ministry of Land and Resources, Beijing 100029, China
  • Received:2012-12-01 Revised:2013-04-01 Online:2013-06-25 Published:2013-06-25

摘要: 分形理论对处理土地利用结构之类的非线性对象具有较强实用价值, 分维值作为该理论的重要测度指标, 其计算常基于栅格数据, 由此导致了土地结构边界信息的损失, 也减弱了分维值对土地利用空间结构特性的揭示作用。本文以苏州市1:10 万土地利用数据为例, 对研究区的土地利用空间格局进行分形分析, 通过分析不同栅格大小的土地利用分维值, 建立分维值与栅格粒度之间的定量关系, 并通过栅格数据推算矢量分维值, 分析推算误差及其原因。结果表明, 苏州市土地利用格局符合分形理论的一般规律;苏州市整体土地利用程度较高, 耕地、建设用地结构较为简单, 而未利用地、林草地结构较复杂, 水域斑块结构复杂性较低;随着栅格粒度的增加, 土地利用分维值呈现增加趋势, 且粒度与分维值之间存在二次函数的定量关系, 可以通过此关系式在一定误差范围内推求矢量数据的土地结构分维值, 斑块数量与整体规模是影响推求精度的重要原因。

关键词: 分维值, 粒度, 数据类型, 苏州, 土地利用

Abstract: Land use system is the product of natural and human activities, and typically as a complex nonlinear dynamical system, its structure is irregular, unstable, complex and non-linear. Fractal theory, as a new technique, has been proved to be practical for analyzing irregular and nonlinear objects. The fractal dimension, one of the most important indices in fractal theory, is often calculated from raster data, but most land-use data are stored as vector data. Conversion of vector data to grid images to calculate fractal dimension may result in inaccurate values. Accuracy of the calculation on raster data is closely related to the grain size of the grid images. Taking a case study of the 1:100000 land use data of Suzhou City in 2008, this paper first analyzed the fractal characteristics of the study area by calculating the fractal dimension, investigated the scale effects of land use fractal dimension by changing the grain size of raster data, and then established a quantitative relationship between fractal dimension and the grain size, and lastly used the math model to calculate fractal dimensions from vector data based on the raster data. The results showed that land use structure of Suzhou City followed the general rules of fractal theory, which proved that this method was suitable for the analysis of the characteristics of land use system in such a rapidly urbanizing area. Furthermore, the overall land use degree of Suzhou City was high; human activities have different effects on the different land types. For example, under the influence of human activities the structures of arable land and construction land were relatively simple, but unused land and forest-grassland are quite complex. The morphology of water was less complex than other land use types, indicating that water was more affected by human activities such as water conservation facilities and irrigation ditches. The effect of the grain size on the fractal dimension in this area showed that the fractal dimension increased with expanding grain size, and the result of statistical analysis suggested that the relationship between fractal dimension and the grain size fit with the quadratic-polynomial-model which provided a bridge between the vector data and raster date for the calculation of the fractal dimension. If the vector data were viewed as raster data of 0 m grain size, vector fractal dimension can be calculated from raster data according to the quadratic-polynomial-model. The difference between the calculated results and the fractal dimension values directly using the vector data was minimal. Thus, fractal dimension of vector land use data (the grain size is 0 m×0 m) could be deduced by this relationship within the margin of error.

Key words: data type, fractal dimension, grain size, land use, Suzhou City