Landscape Ecology

, Volume 11, Issue 1, pp 39–49

Effects of changing spatial resolution on the results of landscape pattern analysis using spatial autocorrelation indices


  • Ye Qi
    • Scripps Institution of OceanographyUniversity of California at San Diego
    • Biological Sciences Center, Desert Research InstituteUniversity of Nevada System

DOI: 10.1007/BF02087112

Cite this article as:
Qi, Y. & Wu, J. Landscape Ecol (1996) 11: 39. doi:10.1007/BF02087112


Understanding the relationship between pattern and scale is a central issue in landscape ecology. Pattern analysis is necessarily a critical step to achieve this understanding. Pattern and scale are inseparable in theory and in reality. Pattern occurs on different scales, and scale affects pattern to be observed. The objective of our study is to investigate how changing scale might affect the results of landscape pattern analysis using three commonly adopted spatial autocorrelation indices,i.e., Moran Coefficient, Geary Ratio, and Cliff-Ord statistic. The data sets used in this study are spatially referenced digital data sets of topography and biomass in 1972 of Peninsular Malaysia. Our results show that all three autocorrelation indices were scale-dependent. In other words, the degree of spatial autocorrelation measured by these indices vary with the spatial scale on which analysis was performed. While all the data sets show a positive spatial autocorrelation across a range of scales, Moran coefficient and Cliff-Ord statistic decrease and Geary Ratio increases with increasing grain size, indicating an overall decline in the degree of spatial autocorrelation with scale. The effect of changing scale varies in their magnitude and rate of change when different types of landscape data are used. We have also explored why this could happen by examining the formulation of the Moran coefficient. The pattern of change in spatial autocorrelation with scale exhibits threshold behavior,i.e., scale effects fade away after certain spatial scales are reached (for elevation). We recommend that multiple methods be used for pattern analysis whenever feasible, and that scale effects must be taken into account in all spatial analysis.


landscape patterns spatial analysis spatial autocorrelation scale effect grain size Moran Coefficient Geary Ratio Cliff-Ord statistic

Copyright information

© SBP Academic Publishing bv 1996