Landscape structure analysis of Kansas at three scales

Recent research in landscape ecology has sought to define the underlying structure of landscape pattern as quantified by landscape pattern metrics. One method used by researchers to address this question involves statistical data reduction techniques. In this study, principal components analysis (PCA) was performed on 27 landscape pattern metrics derived from a Kansas land cover data base at three spatial resolutions: 30 m, 100 m, and 1 km. A hexagonal sampling grid was used to subset the landscape and FRAGSTATS software was used to calculate landscape pattern metrics. The PCA reduced the number of variables from 27 to 5. A five-component PCA solution explained between 81 and 89% of the variation in the data set. The components were interpreted as overall landscape texture, patch shape and size, cropland and grassland class-specific metrics, patch interspersion, and a nearest neighbor attribute. These five dimensions were identified at each resolution level. The first component was stable throughout the resolution levels, whereas the order of importance changed for the latter four components. That most components consistently appeared at each resolution level supports the use of the same subset of pattern metrics for landscape monitoring in the region at different resolutions. The individual metrics emerging as most important were similar to those noted in other research and include the modified Simpson’s diversity index, the area-weighted mean patch fractal dimension, the interspersion and juxtaposition index, and the largest patch index for grasslands.