Assessing effects of input uncertainty in structural landscape classification

This paper presents the results of a study aimed at assessing the effects of input uncertainty on the outcome of a raster-based model for structural landscape classification. The model uses a DEM and a land-cover map as input, and calculates four structural indices from these data. The first two indices determine the openness of the landscape, the other two determine the degree of landscape homogeneity. By combining both aspects, nine different landscape types are defined. Applying Monte Carlo simulation, the effect of DEM error, uncertainty in land-cover classification, and the combined effect of both sources of uncertainty on the outcome of the landscape model are assessed. Special attention is paid to the spatial structure of uncertainty in both data sources.