Abstract
For landscapes that are cast as categorical raster maps, we present an entropy based method for obtaining a multiresolution characterization of spatial pattern. The result is a conditional entropy profile which reflects the rate of information loss as map resolution is degraded by increasing the pixel size through a resampling filter. We choose a random filter because of desirable properties that simplify calculations. Neutral landscapes that are simulated by stochastic generating models provide a way to evaluate the behavior of conditional entropy profiles under known hierarchically scaled generating mechanisms. When the random filter is used, we provide a method to directly compute the conditional entropy profile for specified generating models. Such profiles can provide benchmarks for comparing results obtained from raster maps of actual landscapes that are classified from satellite images. These profiles appear to capture much of the information about a landscape pattern that is otherwise obtained by a suite of landscape measurements which characterize different aspects of spatial pattern.
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