Maps are representations of reality, and they contain errors that are spatially autocorrelated.
Land cover is visualized using categorical coverage maps where data quality is determined by whether the category labeled at each geographic location in the database matches what really exists on the ground. This research begins to address the implications of spatial uncertainty that are a function of map scale for common landscape ecology applications. Information from the confusion matrix and the autocorrelation properties of the truth map are then used to generate potential realizations of a coarser resolution map. To estimate the influence of spatial error on ecological applications we apply a habitat suitability model for an endangered bird that is dependent on the coastal sage scrub class, the California gnatcatcher, and some commonly used landscape pattern metrics. The habitat model is based on patch size, patch density, and distance between patches.