Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Linking socio-economic factors to urban growth by using night timelight imagery from 1992 to 2012: A case study in Beijing
2015 (English)Other (Other academic)
Abstract [en]

In recent decades, the night lights data of the Earth’s surface derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) have been used to detect the human settlements and human activities, because the DMSP/OLS data is able to supply the information about the urban areas  and non-urban areas on the Earth which means it is more suitable for urban studies than usual satellite imagery data.   The urban development is closed linked to the human society development. Therefore, studies of urban development will help people to understand how the urban changed and predict the urban change. The aim of this study was to detect Beijing’s urban development from 1992 to 2012, and find the contributions to the urban sprawl from socio-economic factors. Based on this objective, the main dataset used in this thesis was night lights images derived from the DMSP/OLS which was detected from  1992 to 2012. Due to the lacking of on-board calibration on OLS, and the over-glow of the lights resources, the information about the night lights cannot be extracted directly. Before any process, the night lights images should be calibrated. There is a method to calibrate the night light images which is called intercalibration. It is a second order regression model based method to find the related digital number values. Therefore, intercalibration was employed, and the threshold values were determined to extract urban areas in this study. Threshold value is useful for diffusing the over-glow effect, and finding the urban areas from the DMSP/OLS data. The methods to determine the threshold value in this thesis are empirical threshold method, sudden jump detection method, statistic data comparison method and k-mean clustering method. In addition, 13 socio-economic factors which included gross domestic product, urban population, permanent population, total energy consumption and so on were used to build the regression model. The contributions from these factors to the sum of the Beijing’s lights were found based on modeling.   The results of this thesis are positive. The intercalibration was successful and all the DMSP/OLS data used in this study were calibrated. And then, the appropriate threshold values to extract the urban areas were figured out. The achieved urban areas were compared to the satellite images and the result showed that the urban areas were useful. During the time certain factors used in this study, such as mobile phone users, possession of civil vehicles, GDP, three positively highest contributed to urban development were close to 23%, 8% and 9%, respectively.

Place, publisher, year, pages
2015. Vol. Independent thesis Advanced level (degree of Master (Two Years))
Keywords [en]
Beijing; DMSP/OLS; GIS; remote sensing; socio-economic factor, Geosciences, Multidisciplinary, Multidisciplinär geovetenskap
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kau:diva-75000OAI: oai:DiVA.org:kau-75000DiVA, id: diva2:1356946
Note

Student thesis; 15-015; 2015-12-19T11:21:25.024+01:00

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02

Open Access in DiVA

No full text in DiVA

Other links

Electronic full texthttp://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179639
Remote Sensing

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf