Population spatialization in China based on night-time imagery and land use data

• Published in: International journal of remote sensing Vol. 32; no. 24; pp. 9599 - 9620
• Main Authors: Zeng, Chuiqing, Zhou, Yi, Wang, Shixin, Yan, Fuli, Zhao, Qing
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.01.2011
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; census data; developing countries; Earth sciences; Earth, ocean, space; environmental protection; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; image analysis; Internal geophysics; land use; light intensity; linear models; model validation; population density; population distribution; regression analysis; socioeconomic development; Teledetection and vegetation maps; urban planning; China; Far East; Asia; environment protection; Variable; data; Night; socioeconomic impacts; Predictor; Linear regression; Spatial analysis; urban planning; indicators; System; Luminous intensity; Census; Type; land use; Meteorological satellite; Regression model; imagery; Defense mechanism; Third World; Problem; programs; populations; land cover
• ISSN: 1366-5901; 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2011.569581

Population is a key indicator of socioeconomic development, urban planning and environmental protection, particularly for developing countries like China. But, census data for any given area are neither always available nor adequately reflect the internal differences of population. The authors tried to overcome this problem by spatializing the population across China through utilizing integer night-time imagery (Defense Meteorological Satellite Program/Operational Linescan System, DMSP/OLS) and land-use data. In creating the population linear regression model, night-time light intensity and lit areas, under different types of land use, were employed as predictor variables, and census data as dependent variables. To improve model performance, eight zones were created using night-time imagery clustering and shortest path algorithm. The population model is observed to have a coefficient of determination (R²) ranging from 0.80 to 0.95 in the research area, which remained the same in different years. A comparison of the results of this study with those of other researchers shows that the spatialized population density map, prepared on the basis of night-time imagery, reflects the population distribution character more explicitly and in greater detail.