Ecology and Society (Jun 2002)
A Fractal Landscape Realizer for Generating Synthetic Maps
Abstract
A fractal landscape realizer has been developed that generates synthetic landscape maps to user specifications. The alternative landscape realizations are not identical to the actual maps after which they are patterned, but are similar statistically (i.e., the areas and fractal character of each category are replicated). A fractal or self-affine pattern generator is used to provide a spatial probability surface for each category in the synthetic map. The Fractal Realizer arbitrates contentions among categories in a way that makes it possible to preserve the fractal patterns of all the categories in the resulting synthetic landscape. Each synthetic landscape is one equally likely realization from among an infinite ensemble of possible fractal landscape combinations. Synthetic landscapes produced by the Fractal Realizer have been tested using a variant of the Turing Test. More than 100 map experts were presented with a series of 20 selections of paired maps, and asked to distinguish the real map from the synthetic realization. The resulting population of scores was not significantly different from a random binomial, suggesting that the experts were unable to discern the synthetic maps from the actual ones. Statistical landscape indices computed for 25 different synthetic realizations are compared with the values computed for the actual maps. The Fractal Realizer can be used as a stochastic generator of synthetic input maps to a spatially explicit simulation model to test the effects of landscape rearrangement on the uncertainty of model parameter estimates. The sensitivity of stochastic spatial simulations to prescribed input landscapes can be evaluated by supplying them with a series of synthetic maps that obey particular statistical characteristics and by monitoring changes in selected output responses. Statistically similar input landscapes with different spatial arrangements can be generated and supplied to spatial models as a hedge against pseudoreplication.
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