作者:Xin, X (Xin, Xin)[ 1,2 ] ; Liu, B (Liu, Bin)[ 1 ] ; Di, KC (Di, Kaichang)[ 1 ] ; Zhu, Z (Zhu, Zhe)[ 3 ] ; Zhao, ZY (Zhao, Zhongyuan)[ 4 ] ; Liu, J (Liu, Jia)[ 1 ] ; Yue, ZY (Yue, Zongyu)[ 1 ] ; Zhang, G (Zhang, Guo)[ 5 ]

INTERNATIONAL JOURNAL OF REMOTE SENSING

卷: 38  期: 21  页: 6110-6128

DOI: 10.1080/01431161.2017.1312623

出版年: 2017

摘要

Real-time urban expansion information is important for understanding the socio-economic activities and construction policies in urban areas. Night-time light data, which are available at annual and monthly temporal resolutions, can facilitate better analysis of socioeconomic activities. In this study, we proposed a novel calibration method for Defense Meteorological Satellite Program's Operational Line-scan System (DMSP/OLS) data based on Rational Function Model (RFM). Stable lit pixels were employed to validate the effectiveness of the proposed model. The deference of mean square error shows that RFM method is better than traditional quadratic polynomials method for 76% of the data sets. Urban areas from 1992 to 2013 were extracted based on the calibrated data. A correlation analysis and multiple linear regression analysis between socio-economic factors andDMSP/OLS data were performed for Wuhan, China. The results of correlation factors showed that the correlation coefficient between night-time lights and socio-economic factors was higher than 0.85. Population produced the highest correlation coefficient among all the socio-economic factors. Multiple linear regression analyses were also performed, and the results showed that population and urban area could enhance the R-2 in Wuhan, and population density could enhance the R-2 in a comparative city Ordos. The development driving forces of the city could be reflected in multiple linear regression analysis of the night-time light data and socioeconomic factors. Moreover, we investigated the relationship between construction policy and urban expansion using the timeseries night-time light data, and found that the night-time light data could also reflect the construction policy and monitor the urban expansion effectively.

通讯作者地址: Liu, B (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

[ 2 ] Univ Chinese Acad Sci, Beijing, Peoples R China

[ 3 ] Texas Tech Univ, Dept Geosci, Lubbock, TX 79409 USA

[ 4 ] Wuhan Resources & Planning Bur, Dept Surveying & Mapping, Wuhan, Hubei, Peoples R China

[ 5 ] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Hubei, Peoples R China