A dynamic modeling approach to simulating socioeconomic effects on landscape changes

Modeling and simulating the effects of human factors on landscape change remain as challenges for ecological studies. In this paper, we present a dynamic landscape simulation (DLS) approach to elucidate human-induced landscape changes for a 5104 km study area within the Chicago metropolitan region. The DLS consists of an urban growth simulation submodel and a land-cover simulation submodel. This approach simulates urban land-use expansion by incorporating socioeconomic and demographic data and predicts changes in the landscape as a result of urban expansion. A utility function of spatial choice and a methodology for the construction of that utility function were developed to execute the process. The approach, with dynamic adjustment of transition structures (i.e. the transition potentials, threshold and rate), overcomes the shortcomings of static and statistical models that use a constant transition probability in simulation modeling. It also allows selected economic principles to be integrated into landscape simulation. In this study, historical land-cover and census data were applied to derive transition thresholds and transition rates of the land cover changes. Comparison of the 1997 land-cover maps derived by a DLS simulation and by the classification of Landsat Thematic Mapper (TM) remotely sensed data indicated that a 62.3% overall agreement was achieved among the changed areas. Landscape simulations of the study area from 1997 to 2020 at 5 year time interval were prepared. The results depicted the trends of landscape change in this large urban setting area. © 2001 Elsevier Science B.V. All rights reserved.