Abstract
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.
Highlights
While the majority of Vietnam’s landscape has been dominated by agriculture for several centuries, these traditionally rural areas have been quickly converted for more modern land uses during the last two and a half decades
Using qualitative and quantitative GIS analyses, we corroborated their effectiveness in detecting LCLU changes associated with the urban transition
We found similar spatial patterns in the results of both methods, and we showed that higher rates of change in night-time lights (NTL) coincided with areas classified as change in the
Summary
While the majority of Vietnam’s landscape has been dominated by agriculture for several centuries, these traditionally rural areas have been quickly converted for more modern land uses during the last two and a half decades. One of the most evident effects is urbanization, a loss of agricultural lands as well as open space, leading to urban sprawl. This urban transition process, known as peri-urbanization, has blurred the distinction between rural and urban areas, prompting the need for more effective methods to characterize this land-cover/land-use change (LCLUC) phenomenon. Remote sensing offers useful tools for LCLUC studies, yet there are still several challenges to overcome, especially in rapidly developing tropical nations like Vietnam. Cloud cover is problematic in the humid tropics and mountainous uplands, resulting in remote sensing data gaps. We expect the majority of changes from rural to urban land use would occur around Vietnam’s two main population centers—the low-lying floodplains of the
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