Stochastic generating models for simulating hierarchically structured multi-cover landscapes

For simulating hierarchically structured raster maps of landscapes that consist of multiple land cover types, we extend the concept of neutral landscape models to provide a general Markovian model. A stochastic transition matrix provides the probability rules that govern landscape fragmentation processes by assigning finer resolution land cover categories, given coarser resolution categories. This matrix can either be changed or remain the same at different resolutions. The probability rules may be defined for simulating properties of an actual landscape or they may be specified in a truly neutral manner to evaluate the effects of particular transition probability rules. For illustration, model parameters are defined heuristically to simulate properites of actual watershed-delineated landscapes in Pennsylvania. Three landscapes were chosen; one is mostly forested, one is in a transitional state between mostly forested and a mixture of agriculture, urban and suburban land, while the third is fully developed with only remnant forest patches that are small and disconnected. For each landscape type, a small sample of raster maps are simulated in a Monte Carlo fashion to illustrate how an empirical distribution of landscape measurements can be obtained.