The urban transition that has emerged over the past quarter century poses new challenges for mapping land cover/land use change (LCLUC). The growing archives of imagery from various earth-observing satellites have stimulated the development of innovative methods for change detection in long-term time series. We tested two different multi-temporal remote sensing datasets and techniques for mapping the urban transition. Using the Red River Delta of Vietnam as a case study, we compared supervised classification of dense time stacks of Landsat data with trend analyses of an annual series of night-time lights (NTL) data from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). The results of each method were corroborated through qualitative and quantitative GIS analyses. We found that these two approaches can be used synergistically, combining the advantages of each to provide a fuller understanding of the urban transition at different spatial scales.