Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2173-2177, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2173-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2173-2177, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2173-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

MONITORING POPULATION EVOLUTION IN THE PEARL RIVER DELTA FROM 2000 TO 2010

S. Yu1,2, F. Liu1, and Z. Zhang1 S. Yu et al.
  • 1Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China

Keywords: DMSP/OLS, Population Spatialization, Time Series, Remote Sensing, The Pearl River Delta, Population Evolution

Abstract. On behalf of more populous and developed regions in China, urban agglomerations have become important carries loading active economic activities and generous social benefits, and experienced sharper population increase, which results in great threat on local eco-environment construction. Therefore, exact and detailed population monitoring and analyzing, especially on the long sequence and multi frequency, is of great significance. The nighttime light time-series (NLT) products has been proven to be one of the most useful remotely sensed imagery to acquire persons at 1 km × 1 km scales. However, the existed problems, such as light saturation and blooming, greatly limit the accuracy of estimated results. Furthermore, it’s difficult to spatialize population at km2 level due to the lack of basic data in non-census years. In order to solve all problems mentioned above, the populous Pearl River Delta was selected as the study area. A new residential extent extraction index (REEI) was proposed to solve light saturation and blooming problems. Population spatialization methods in census and non-census years were applied to acquire detailed population distribution from 2000 to 2010. Results showed the feasibility of the proposed methods in this work. During the decade, population was denser in the central PRD and sparser in the eastern, western and northern PRD. The speed of population increase was various in nine cities, but faster in 2000–2005 than 2005–2010.