Modelling Biological Data with Segmented Landscape Objects and Image Grey Values

Landscape structure was investigated on six test sites in Switzerland, selected along a land-use intensity gradient. The test areas were captured with remote sensing images on three spatial resolutions: 1) fused Landsat ETM-IRS and 2) Quickbird satellite images as well as 3) CIR aerial photos. Segmentation and fuzzy classification were implemented on the images to extract landscape patch indices in 96 sampling plots. In addition, or iginal and enhanced grey values were derived in the plots. Abundance of seven breeding bird species, sampled in the plots, was analysed with CCA. The variance of the species data explained by patch indices and grey values was compared across the three spatial resolutions. CCA revealed little explanatory power of remote sensing variables. The explained variance was comparable in case of grey values and patch indices. Increasing spatial resolution of patch indices and grey values did not evidence increasing association to abundance of birds. Presence and absence of E. rubecula was modelled by means of logistic regression. The logistic models were compared based on goodness of fit statistics and discrimination capacities. Logistic regression revealed very high discrimination capacity of both patch indices and grey values in predicting presence and absence of the species. Increasing spatial resolution did not effect the discrimination capacity of the remote sensing variables but resulted in increasing importance of image textural features.