Built-up Area Change Analysis in Hanoi Using Support Vector Machine Classification of Landsat Multi-Temporal Image Stacks and Population Data

Abstract

Abstract:In 1986, the Government of Vietnam implemented free market reforms known as Doi Moi (renovation) that provided private ownership of farms and companies, and encouraged deregulation and foreign investment. Since then, the economy of Vietnam has achieved rapid growth in agricultural and industrial production, construction and housing, and exports and foreign investments, each of which have resulted in momentous landscape transformations. One of the most evident changes is urbanization and an accompanying loss of agricultural lands and open spaces. These rapid changes pose enormous challenges for local populations as well as planning authorities. Accurate and timely data on changes in built-up urban environments are essential for supporting sound urban development. In this study, we applied the Support Vector Machine classification (SVM) to multi-temporal stacks of Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images from 1993 to 2010 to quantify changes in built-up areas. The SVM classification algorithm produced a highly accurate map of land cover change with an overall accuracy of 95%. The study showed that most urban expansion occurred in the periods 2001-2006 and 2006-2010. The analysis was strengthened by the incorporation of population and other socio-economic data. This study provides state authorities a means to examine correlations between urban growth, spatial expansion, and other socio-economic factors in order to not only assess patterns of urban growth but also become aware of potential environmental, social, and

Quantifying urban growth patterns in Hanoi using landscape expansion modes and time series spatial metrics

“…Nong et al [ 33 ] used multi-temporal image stacks of Landsat images to classify land cover in Hanoi into seven classes: agriculture, urban footprint, forest, water, and three change classes (agriculture to built-up) between the time periods of 1993–2001, 2001–2006, and 2006–2010. There is concern that seasonal variations in spectral reflectance could potentially result in confusion between built-up areas and fallow farmland at certain time periods in the remote sensing classification because both built-up areas and fallow farmland show high reflectance in the visible-infrared wavelength regions.…”

“…There is concern that seasonal variations in spectral reflectance could potentially result in confusion between built-up areas and fallow farmland at certain time periods in the remote sensing classification because both built-up areas and fallow farmland show high reflectance in the visible-infrared wavelength regions. However, Nong et al [ 33 ] have successfully distinguished changes within agricultural land use types and changes from agricultural land to built-up land using multi-temporal image stacks of Landsat images for land cover classification of Hanoi City. The approach considers that if a field is converted to built-up land, this change should be “confirmed” in subsequent satellite images because the transformation from agriculture to built-up land is generally unidirectional.…”

“…The approach considers that if a field is converted to built-up land, this change should be “confirmed” in subsequent satellite images because the transformation from agriculture to built-up land is generally unidirectional. We used the existing land cover data developed in Nong et al [ 33 ] to study the spatial characteristics of urban land expansion in Hanoi. Specifically, we focused on the spatial characteristics of the urban footprint in 1993, and urban patches that were converted from agriculture to built-up land in three periods: (i) 1993–2001, (ii) 2001–2006, and (iii) 2006–2010 ( Fig 2 ).…”

Mapping the Expansion of Boom Crops in Mainland Southeast Asia Using Dense Time Stacks of Landsat Data

“…To our knowledge, a multi-date composite change detection technique as performed in this study has only been applied to map urban expansion [43,44,74,75]. These studies show that the supervised classification of a dense time stack of Landsat images performs well when mapping abrupt, but also subtle and gradual land-cover changes related to urban expansion.…”

The Use of Spatial Metrics and Population Data in Mapping the Rural-Urban Transition and Exploring Models of Urban Growth in Hanoi, Vietnam

“…Third, the development of transport systems, telecommunications and mass media across regions have facilitated spatial mobility and enhanced social contacts between rural and urban areas. According to the 1999 and 2009 national population censuses, the population of Hanoi grew by more than a million people during this decade; over 50% of this growth occurred in communes classified as peri-urban (Nong et al, 2015). Migrants from rural areas or neighbouring provinces to Hanoi often chose to reside in sub-urban areas due to cheaper housing and living cost, and a higher concentration of small to medium manufacturing enterprises.…”