Spatial simulation of landscape changes in Georgia: A comparison of 3 transition models

Abstract

Spatial simulation models were developed to predict temporal changes in land use patterns in a piedmont county in Georgia (USA). Five land use categories were included: urban, cropland, abandoned cropland, pasture, and forest. Land use data were obtained from historical aerial photography and digitized into a matrix based on a 1 ha grid cell format. Three different types of spatial simulation were compared: (1) random simulations based solely on transition probabilities; (2) spatial simulations in which the four nearest neighbors (adjacent ceils only) influence transitions; and (3) spatial simulations in.which the eight nearest neighbors (adjacent and diagonal cells) influence transitions. Models and data were compared using the mean number and size of patches, fractal dimension of patches, and amount of edge between land uses. The random model simulated a highly fragmented landscape having numerous, small patches with relatively complex shapes. The two versions of the spatial model simulated.cropland well, but simulated patches of forest and abandoned cropland were fewer, larger, and more simple than those in the real landscape. Several possible modifications of model structure are proposed. The modefing approach presented here is a potentially general one for simulating human-influenced landscapes.

Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA

“…Here, we present background information on the forest-cover modeling approach employed. Transition probabilities have been used extensively for analysis and modeling of land-use and land-cover change (Burnham, 1973;Bell, 1974;Turner, 1987;Muller and Middleton, 1994). The approach treats state transitions as Markovian random processes that are conditional on the initial state only.…”

“…The matrix P is row-standardized, such that the sum of transition probabilities from a given state is always equal to one. The value of the approach is that the transition matrix, once specified, can be used analytically to project future landscape compositions (Jahan, 1986;Guttorp, 1995) or in simulation modeling to develop alternative landscape scenarios (Burnham, 1973;Turner, 1987). Any set of states (e.g.…”

“…land-use or land-cover classes) can be used, provided their definitions do not change over time, at any scale of analysis. The change matrix is often derived from multiple temporal classifications of land use or land cover (Bell, 1974;Turner, 1987).…”

“…the transition probabilities are often derived empirically from multi-temporal maps with no description of the process (Baker, 1989). This third limitation is particularly acute when land-cover changes are under investigation, for example, from remotely sensed imagery, and when those changes are driven by social and economic processes (Turner, 1987). To address limitations 1 and 3 above, Baker (1989) suggested setting state transition probabilities as a function of exogenous or endogenous variables, which vary in space and time.…”

“…X s ), which can vary in both time (t) and space (x), and p ij . Turner (1987) demonstrated an approach to conditioning the changes to initial states in adjacent sites, in addition to conditioning changes on the initial state, thereby introducing spatial dependence into the simulation (the second limitation).…”