自然资源学报 ›› 2016, Vol. 31 ›› Issue (3): 413-424.doi: 10.11849/zrzyxb.20150303

• 资源生态 • 上一篇    下一篇

无锡市景观指数的粒度效应研究

吴未a, b, 范诗薇a, 许丽萍a, 张敏a, 欧名豪a, b   

  1. a. 南京农业大学 土地管理学院,南京 210095;
    b. 南京农业大学 农村土地资源利用与整治国家地方联合工程研究中心,南京 210095
  • 收稿日期:2015-03-27 出版日期:2016-03-15 发布日期:2016-03-15
  • 通讯作者: 欧名豪(1964- ),男,安徽人,教授,博士生导师,主要研究方向为土地利用与规划管理. E-mail:ww@njau.edu.cn
  • 作者简介:吴未(1973- ),男,安徽人,博士,副教授,主要研究方向为景观生态与土地利用规划.E-mail:ww@njau.edu.cn
  • 基金资助:
    国家自然科学基金项目(41571176); 中国博士后基金特别资助项目(2010003592); 江苏高校哲学社会科学研究一般项目(2015SJD096); 南京农业大学中央高校基本科研业务费人文社会科学研究基金配套项目(SKPT2015018)

Grain Size Effect of Landscape Metrics in Wuxi City

WU Weia, b, FAN Shi-weia, XU Li-pinga, ZHANG Mina, OU Ming-haoa, b   

  1. a. College of Land Management, b. National-Local Joint Engineering Research Center for Rural Land Resources Use and Consolidation, Nanjing Agricultural University, Nanjing 210095, China
  • Received:2015-03-27 Online:2016-03-15 Published:2016-03-15
  • Supported by:
    National Natural Science Foundation of China, No.41571176; China Postdoctoral Science Foundation Special Funded Project, No.2010003592; Philosophy and Social Science Research Project in Colleges and Universities in Jiangsu Province, No.2015SJD096; Fundamental Research Funds for the Central Universities, No.SKPT2015018

摘要: 论文以无锡市2010年TM遥感影像数据为基础,构建了基于自然地理和人为干扰两类因素的因子加权叠加模型,以此获取研究区的生态贡献等级斑块,运用ArcGIS 10.0,Fragstats 4.0和CS 2.2软件计算并探讨了快速城市化平原地区不同景观指数随粒度增粗的变化特征及其适宜的研究尺度域.结果表明,随粒度变粗,景观指数受空间粒度的影响显著,存在特定的粒度范围.其中,类型水平,景观水平以及景观连接度指数的尺度域不完全相同,粒度分别为2~30,2~10和2~7个栅格单元,研究区尺度域以2~7个栅格单元即60~210 m为宜;景观连接度指数得到的尺度域更精准,粒度依赖性更明显,适宜于粒度效应研究,但是不同景观连接度指数对粒度变化的敏感程度不同.

Abstract: Wuxi city in the Yangtze River Delta Area is taken as a representative case of urbanizing regions of China with great pressure of biodiversity conservation and environmental protection. The multi-weight factors model based on natural and artificial factors was applied in the process of identifying local ecological contribution pattern patches. Five main factors, i.e., slope, height, land use type, distances to settlements and traffic network, and their relative weights were obtained from previous results. The basic spatial cell unit is 30 m. By the scale the landscape pattern was converted to ArcGrid formats with grain sizes of 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90 and 100 cells. Above two steps were computed in ArcGIS 10.0 environment based on the dataset interpreted from TM images. Landscape metrics were used to detect the grain size effects of ecological pattern. Five landscape connectivity indicators, including number of links (NL), number of components (NC), integral index of connectivity (IIC), probability of connectivity (PC) and importance value of PC (dPC), were computed in ConeforSensinode 2.2 environment. Landscape metrics at class and landscape levels, including total class area (CA), number of patches (NP), patch density (PD), largest patch index (LPI), landscape shape index (LSI), perimeter area fractal dimension (PAFRAC), aggregation index (AI), splitting index (SPLIT), mean shape index (MSI), area-weighted patch fractal dimension (AWMPFD), cohesion index (COHESION), division index (DIVISION), Shannon's diversity index (SHDI), Shannon's evenness index (SHEI), were computed in Fragstats 4.0 environment. The results showed that with the increase of grain size, these metrics changed dramatically, and there existed scale domains of landscape metrics. The scale domains of landscape metrics at class and landscape levels were 2-30 cells and 2-10 cells respectively, and that of landscape connectivity indicators was 2-7 cells. The scale domain of 2-7 cells, i.e. 60-210 m, was recommended. The scale domain of landscape connectivity indicators was more precise compared with those of other landscape metrics. Landscape connectivity indicators were suitable for the research of grain size effect. However, it should be noticed that the response degrees of different landscape connectivity indicators were different.

中图分类号: 

  • Q149