Stochastic generating models for simulating hierarchically structured multi-cover landscapes

For simulating hierarchically structured raster maps of landscapes that con sist of multiple land cover types, we extend the concept of neutral landsca pe models to provide a general Markovian model. A stochastic transition mat rix provides the probability rules that govern landscape fragmentation proc esses by assigning finer resolution land cover categories, given coarser re solution categories. This matrix can either be changed or remain the same a t different resolutions. The probability rules may be defined for simulatin g properties of an actual landscape or they may be specified in a truly neu tral manner to evaluate the effects of particular transition probability ru les. For illustration, model parameters are defined heuristically to simulate pr operites of actual watershed-delineated landscapes in Pennsylvania. Three l andscapes were chosen; one is mostly forested, one is in a transitional sta te 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 em pirical distribution of landscape measurements can be obtained.