4/7/2023 0 Comments Baseline gridsIt is a possible value, an indispensable input parameter, which describes the temporal behavior of land use types or the status of the grid cells. The conversion rule in DLS determines which conversions are allowed for a certain kind of land use or where, identified by a number of grid cells, conversions of land uses which results in direct land use changes could occur. DLS is implemented as a user friendly software tool which provides users with options to define land use change scenarios and format the input parameters and edit the regression results between land uses and their influencing factors by including a shell with menu bars, view windows, etc. Besides, scenarios of land use changes are developed and the changes of land uses are spatially disaggregated into each grid cell in accordance with the estimated relationships between land uses and their influencing factors. At the bottom level, parameters identifying the technical changes, lifestyle changes, economic growth, population growth and urbanization are also incorporated in the model framework of DLS. Given the complexity of land systems, which is determined and represented by the interactions between land and land users, DLS, at the very top level, integrates five dimensions of influencing factors – variability of geophysical conditions, environmental changes, trade environment changes, institutional changes and policies closely related with land management ( Figure 1). That is, it requires a level of computing skill beyond simple spreadsheet programming and by now some agent-based software frameworks have been developed to ease the task of the social scientist or business analyst in building agent-based models. In addition, an agent-based model requires programming in an object-orientated language such as Java. The reduction of the complexity inherent in land systems to more simple relationships in agent-based modelling would lead to large bias contained in the simulation results. In agent-based modeling, land use changes are regarded depending on characteristics of a region that are of socio-economic and biophysical origin and are affected by the behaviors of the land stakeholders and their decisions. However, the impacts from time variant variables on the land uses changes are sometime overlooked in the simulation process. Cellular automata models consist of an environment in which the interactions occur among individuals, which are defined by behavioural rules and characteristics of grid cells of land uses. The consideration of co-linearity between the explanatory variables, however, is always ignored in semi-empirical models. Semi-empirical models use statistical techniques to derive the mathematical relationships between variables, identifying land use changes and sets of explanatory variables of land use changes. Most of the models created for land project change uses can be categorized into three types: semi-empirical models, cellular automata models and agent-based models. A number of previous investigations have focused specifically on this field. It is of great importance to simulate the dynamics of land systems, which can greatly benefit decisions making about land management and land use planning. The dynamics of a land system is a comprehensive process which operates over a range of scales in space and time and is driven by more than one variable that can influence the actions of the agents of land uses.
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