| dc.description.abstract | Urban growth can be caused by the concentration of population, the development of transportation network and government policies. The urban development is one of the major forces to drive land use and land cover change (LUCC), and to funtionalize this change, LUCC models are tools to analyze and predict the consequences of land use change. The Cellular Automata Markov (CA-Markov) model is the most commonly used one, which simulates the land cover change with a set of linearly combined driving factors and cell-based expansion process. However, land cover with patches is commonly observed, and the driving forces can be spatially heterogeneous. This study aims to develop a new predictive model (Patch-based CA-Markov model, PBCA-Markov model), by integrating patch-based transition rules of land cover change and a machine-learning algorithm to assess the non-linear land development processes. The Taoyuan City, the sixth major city of Taiwan, is selected as the study area, because the city has the highest population growth rate in Taiwan since 1984, and also has experienced a significant increase of urban land in recent years. Therefore, a rapid development of the city could reshape the landscape in near future.
This study analyzed the land cover changes of Taoyuan City from 1984 to 2014, with using Landsat TM and OLI data, and different land cover types were classified and mapped through support vector machine classifier. The land cover results are used for model calibration and validation, and results show the PBCA-Markov generates higher overall accuracies than the CA-Markov. This study focuses on the prediction of urban areas and found that some urban land patches can be only successfully predicted by PBCA-Markov, while CA-Markov mainly predicts expansion areas along the urban fringe. This study suggests the proposed PBCA-Markov can be useful for the region urban planning and decision making practices. | en_US |