Urban growth and land cover change in Chiang Mai and Taipei: Results from the SLEUTH model

Abstract

SLEUTH is a modified cellular automata (CA) model which consists of an urban growth sub model and a land cover change transition sub model. The urban growth model is the main component of SLEUTH, which is tightly coupled with the land cover change transition model. This model was chosen to calibrate urban growth in two Asian cities, Chiang Mai (Thailand) and Taipei (Taiwan). These cities are undergoing rapid land use/land cover change as a result of robust economic and population growth. SLEUTH cellular automata model contains characteristics that offer advantages for modelling physical dynamics. Because of its cellular data structure, it can be integrated with raster-based remote sensing data. Calibration of the SLEUTH model requires gridded inputs of topographic slope, land use, zones excluded from growth, urban spatial extents, transportation networks and terrain hillshading. These data were developed from remote sensing and from topographic maps using GIS, and were prepared at three different resolutions. This research applied the SLEUTH cellular automata model to explore land use dynamics of the two Asian cities over several decades. Three phases of calibration were used, corresponding to progressively higher spatial resolutions whilst the ranges of SLEUTH parameters were narrowed. The result yielded five key parameters characterizing historical urban growth patterns and suggesting a basis for exploring future scenarios of land-use change. We discuss the lessons learned from applying SLEUTH model to Chiang Mai and Taipei compared to other SLEUTH applications. The study reveals that: (1) urban development in Chiang Mai is best captured by Xmean and edge growth regression scores, whereas Taipei is best simulated by fitting using the Lee Sallee shape correspondence index; (2) the SLEUTH model can be applied to study urban land use dynamics in both countries, when some adaptations for spatial accuracy and scale sensitivity are made. With a suitably calibrated model, the next phase of research is to use it to explore alternative futures by adjusting some of the key parameters. It is also intended to use the urban extents from other years in the calibration to observe the model sensitivity to temporal data sets. At this stage the model moves from a descriptive tool to one that could help facilitate deliberation about alternative futures about the urban growth patterns for Chiang Mai and Taipei. The historical understanding provided by the model would need to be communicated in graphics, but with care many of the cellular automata ideas are intuitive and visually exciting for researchers to explore (Lebel, et. al., 2005). In conclusion, applying the SLEUTH model to Taiwan and Thailand can increase the potential use of remote sensing and GIS data for the land cover change modeling. The result will be very useful to city planners, resource managers, policy makers and others in the related fields.