Forecasting space-time land use change in the paochiao watershed of Taiwan using demand estimation and empirical simulation approaches

This study applies two empirical approaches such as logistic regression, and artificial network (ANN) to combine Conversion of Land Use and its Effects (CLUE-s) model to predict urban sprawl in the Paochiao watershed in Taipei County, Taiwan. The current investigation projected land-use dynamics for the next twenty years using demand prediction models such as the Markov chain and cellular automata model. Accordingly, the empirical models integrating with CLUE-s then simulated land-use patterns for future periods based on the predicted demands. Results show that land-use patterns predicted by the model tended to fragment and intersperse future patterns. The predicted land-use patterns have significant impact on urban sprawl in the Paochiao watershed. The study is an effective means of enhancing land-use monitoring and managing urbanized watersheds. © 2010 Springer-Verlag Berlin Heidelberg.