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Yangon, the former capital of Myanmar, is the major economic areas of the country. Also, the urban areas have significantly increased. However, Yangon has problems with disasters such as flood and earthquake. To support disaster risk management in Yangon, Myanmar, the estimation of urban expansion is required to understand the mechanism of urban expansion and predict urban areas in the future. This research proposed a methodology to develop urban expansion modeling based the dynamic statistical model using Landsat Time-Series and GeoEye Images. Multispectral Landsat images from 1978 to 2009 were classified to provide land cover change with a long period. By observing land cover from the past to the present, the class translation matrix was obtained. Stereo GeoEye Images in 2013 were employed to extract the heights of buildings. By using the heights of buildings, the multi-centers of urban areas cloud be detected. The urban expansion modeling based on the dynamic statistical model was defined to refer to three factors; (1) the distances from the multi-centers of the urban areas, (2) the distances from the roads, and (3) the class translation. The estimation of urban expansion was formulated in term of the dynamic statistical model by using the maximum likelihood estimator. The relevant equations to estimate urban expansion are expressed in this research. The prediction of urban expansion was defined by the combination of the estimation of urban expansion and the estimated parameters in the future. In the experiments, the results indicated that our model of urban expansion estimated urban growth in both estimation and prediction steps with efficiency.
