An analysis of land use change and the workers’ perception towards changes from 2007 – 2017: A case in Nam Tu Liem District, Hanoi, Vietnam
Main Article Content
Abstract
Land use change has been assessed widely using Remote Sensing (RS) and Geographic Information System (GIS) techniques. The analysis of land use change was done by detecting land cover change. A study about land cover change, along with the self-employed workers’ perception towards changes between 2007 and 2017 were carried out in Nam Tu Liem District, Hanoi, Vietnam. The result of the study shows that the built-up lands have increased and remained to be the dominant land cover types in 2017. The agriculture has been declining mainly due to conversion into built-up land. Other land type including water, bare land, and vegetation have shown slight changes throughout the years. Overall changes from 2007 to 2017 shown that built-up land gained the most and agriculture land lost the most. On the other hand, the perception study’s major findings indicate that about two-thirds (69%) of respondents are aware of changes. However, almost one-third (31%) are unaware of the said topic. There are several factors that may affect the awareness of self-employed workers which will be cursory discussed in the study. This study in Nam Tu Liem District is a first step to determine and understand the major driving factors and their impacts on the land use changes in the area. A detailed land use/cover change study and a larger population size for perception studies are recommended in order for the government to formulate policies to achieve sustainable development.
Keywords:
Land use change, urbanization, change detection, remote sensing, geographic information system, awareness.
References
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[4] General Statistics Office of Vietnam. The 2014 Viet Nam Intercensal Population and Housing Survey: Major Findings. Statistical Documentation and Service Center, Hanoi, Vietnam, 2014.
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[10] P. Journeaux, E. van Reenen, T. Manjala, S. Pike, and I. Hanmore. Analysis of Drivers and Barriers to Land Use Change. Hamilton, NZ: AgFirst Waikato Ltd., 2017.
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[18] H. Zhang, Z.-F. Qi, X.-Y. Ye, Y.-B. Cai, W.-C. Ma, and M.-N. Chen. Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai China. Applied Geography, 2013, 44: 121–133.
[19] M. Moore, P. Gould, and B. S. Keary. Global Urbanization and Impact on Health. International Journal of Hygiene and Environmental Health 2003, 206: 269–278.
[20] D. A. Leon. Cities, urbanization and health. International Journal of Epidemiology, 2008, 37(1): 4–8.
[21] Japan International Cooperation Agency. The Comprehensive Urban Development Programme in Hanoi Capital City of the Socialist Republic of Vietnam (HAIDEP): Master Plan Study. Final Report, 2007.
[22] K. J. H. Williams and J. Schirmer. Understanding the relationship between social change and its impacts: The experience of rural land use change in south-eastern Australia. Journal of Rural Studies, 2012, 28(4): 538–548.
[23] Nhan Dan. Hanoi has two new districts: Bac Tu Liem, Nam Tu Liem. [Online], 2013. Retrieved from:http://en.nhandan.com.vn/society/item/2186002-hanoi-has-two-new-districts-bac-tu-liem-nam-tu-liem.html
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[29] P. S. Chavez Jr. An improved dark-object subtraction technique for atmospheric scattering correction for multispectral data. Remote Sensing of Environment 1988, 24: 459–479.
[30] C. Song, C. E. Woodcock, K. C. Seto, M. P. Lenne, and S. A. Macomber. Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? Remote Sensing of Environment, 2001, 75(2): 230–244.
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[32] G. A. Banko. Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and of Methods Including Remote Sensing Data in Forest Inventory. Interim Report IR-98-081. International Institute for Applied Systems Analysis (IIAA). Laxenburg, Austria, 1998.
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[35] J. R. Anderson, E. E. Hardy, J. T. Roach, and R. E. Witmer. A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Professional Paper, No. 964. USGS, Washington, D.C., 1976.
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