Research on the Influence of Land Use Classification on Landscape Metrics

  • 1. College of Environmental Sciences, Peking University, Beijing 100871, China;
    2. Center of Digital City and Urban Landscape, Shenzhen Graduate School, Peking University, Shenzhen 518055, China

Received date: 2005-03-22

  Revised date: 2005-09-06

  Online published: 2006-02-25

Supported by

The State Key Basic Research and Development Plan of China, No.G2000046807; National Natural Science Foundation of China, No.40471002


Landscape pattern analysis based on landscape metrics is a basic content of the research on landscape ecology. More and more researches proved that not only scale effects and the precision of remote sensed data had significant influence on landscape metrics, but also the difference of land use classification would make the change of landscape metrics. However, we still have not found out how land use classification affects landscape metrics and associated influence mechanism. In this paper, we chose Bao'an of Shenzhen city as an experimental area, to analyze the characteristics of the change of 24 landscape metrics associated with the change of land use classification. The results showed that land use classification indeed influenced landscape metrics. And based on the shape of the land use classification effect curves and the predictability of these relations, the 24 landscape metrics can be divided into three groups. The first group included 12 indices, i.e., number of patches (NP), patch density (PD), edge density (ED), mean patch size (MPS), landscape shape index (LSI), mean patch shape index (MSI), perimeter-area fractal dimension (PAFRAC), mean patch fractal dimension (MPFD), aggregation index (AI), Shannon's diversity index (SHDI), Simpson's diversity index (SIDI), and modified Simpson's diversity index (MSIDI). The behavior of this group of indices with the change of the number of land use types was very predictable with simple function relations in regression analysis, which were mainly logarithm function, S function, and inverse function. The second group included seven indices, i.e., patch size standard deviation (PSSD), patch size coefficient of variation (PSCV), largest patch index (LPI), area-weighted mean patch shape index (AWMSI), area-weighted mean patch fractal dimension (AWMPFD), landscape division index (DIVISION), and patch cohesion index (COHESION). The behavior of this group was not easy to predict with significant subsection. And function relations used in regression analysis mainly included S function, linear function, inverse function and compound function. The third group included five indices, i.e., contagion index (CONT), landscape dominance index (DI), Shannon's evenness index (SHEI), Simpson's evenness index (SIEI), and modified Simpson's evenness index (MSIEI). The behavior of this group could not be predicted. Significant influence of the changing land use classification on landscape metrics indicated that only landscape with the same land use classification could be used for comparing landscape pattern characteristics.

Cite this article

PENG Jian, WANG Yanglin, ZHANG Yuan, YE Minting, WU Jiansheng . Research on the Influence of Land Use Classification on Landscape Metrics[J]. Acta Geographica Sinica, 2006 , 61(2) : 157 -168 . DOI: 10.11821/xb200602005


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