Landscape structure assessment with image grey-values and object-based classification at three spatial resolutions

The analysis of landscape pattern through remote sensing data is relatively widespread in landscape ecology and landscape planning. However, the lack of comparability of results between different image-processing methods and across spatial resolutions limits the potential usefulness of landscape pattern indices. In this study, 96 sampling plots in Switzerland were investigated covering land-use intensities ranging from old-growth forest to intensive agricultural landscapes. The sampling plots were captured using fused Landsat ETM-IRS, Quickbird and aerial photograph data. In order to quantify landscape patterns, seven patch indices (derived by object-oriented classification) and six grey-value indices were extracted from the sampling plots. Principal component analysis was applied to the datasets, with the amount of variance in the first four axes compared among the sampling plots. Biplots of indices and sampling plots derived from all datasets were investigated with respect to land-use intensity patterns. PCA results indicated that increasing spatial resolution corresponded to a slight increase in explained variance. Moreover, image grey-values explained more variance between the sampling plots than segmented patch indices. Furthermore, biplots of grey-value indices were capable of grouping sampling plots according to the land-use intensity gradient, while segmented patch indices failed to adequately represent these.