Effects of Changing Spatial Scale on the Results of Statistical Analysis with Landscape Data: A Case Study

The effect of spatial scale on spatial analysis has long, but sporadically, been recognized in human geography and more recently and acutely in landscape ecology. As the number of studies directly and systematically addressing scale effects is still limited, it remains unclear how results of different statistical analyses are affected by changing scale for different landscapes, or whether or not such effects can be predicted and, if so, in what situations. However, it is certain that erroneous conclusions may result if scale effects are not considered explicitly in spatial analysis with area-based data. With widespread use of remote sensing data and GIS, a better understanding of the issue of scale effects is much needed. The main purpose of this study, therefore, was to examine how results of statistical analysis respond to a systematic change in the scale of analysis. Specifically, we investigated how the relationship between landscape metrics (local landcover diversity and richness indices) and independent variables (TM bands and vegetation indices) would change with different sample sizes and mathematical representations of variables. The landscape under study is the Minden area of Nevada in the western Great Basin. Four different sample sizes (19x19, 15x15, 11x11, and 5x5 pixels) and four different representation forms (variance, mean, variance-mean ratio, and coefficient of variation) of the variables were used in all statistical analyses. We systematically examined the effects of changing sample size and representations of variables on the results of regression, analysis of variance, and correlation analysis. The results indicated that the relationship between landscape metrics and TM bands and vegetation indices was affected considerably by the change of sample size. Both the R value and the level of statistical significance of the relationship tended to increase as sample size increased. In addition, the