Monitoring Population Evolution in China Using Time-Series DMSP/OLS Nightlight Imagery

Abstract

Accurate and detailed monitoring of population distribution and evolution is of great significance in formulating a population planning strategy in China. The Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) nighttime lights time-series (NLT) image products offer a good opportunity for detecting the population distribution owing to its high correlation to human activities. However, their detection capability is greatly limited owing to a lack of in-flight calibration. At present, the synergistic use of systematically-corrected NLT products and population spatialization is rarely applied. This work proposed a methodology to improve the application precision and versatility of NLT products, explored a feasible approach to quantitatively spatialize the population to grid units of 1 km × 1 km, and revealed the spatio-temporal characteristics of population distribution from 2000 to 2010. Results indicated that, (1) after inter-calibration, geometric, incompatibility and discontinuity corrections, and adjustment based on vegetation information, the incompatibility and discontinuity of NTL products were successfully solved. Accordingly, detailed actual residential areas and luminance differences between the urban core and the peripheral regions could be obtained. (2) The population spatialization method could effectively acquire population information at per km 2 with high accuracy and exhibit more details in the evolution of population distribution. (3) Obvious differences in spatio-temporal characteristics existed in four economic regions, from the aspects of population distribution and dynamics, as well as population-weighted centroids. The eastern region was the most populous with the largest increased magnitude, followed by the central, northeastern, and western regions. The population-weighted centroids of the eastern, western, and northeastern regions moved along the southwest direction, while the population-weighted centroid of the central region moved along the southeast direction.(4) The population distribution and dynamics in nine-level population density types were significantly different. In the period of 2000-2010, the population in the basic no-man and high concentration types presented a net decrease. The population in seven other regions all increased with a net increase ranging from 25 km 2 (the moderate concentration type) to 245,668 km 2 (the general transition type).Except those in the core concentration and extremely sparse types, the population-weighted centroids in all other population density types moved along the southwest direction.

Long-Term Monitoring of the Impacts of Disaster on Human Activity Using DMSP/OLS Nighttime Light Data: A Case Study of the 2008 Wenchuan, China Earthquake

“…The most commonly used methods are shown in Table 2. Most of the existing calibration methods follow Elvidge's paradigm, but use different calibration sites [18,19,21]. In addition, Zhang and Li have used a globally-consistent and regionally-consistent bias, respectively, to calibrate the NTL images [22,24].…”

“…When dealing with a rapidly-developing local region, this dynamic becomes a challenge. Liu and Yu use the same intercalibration method as Elvidge's [19,21]. Although they all adopt a post-adjustment after intercalibration to improve the consistency of the time series NTL data, the defect that samples are selected manually is still not resolved.…”

Monitoring Population Evolution in the Pearl River Delta From 2000 to 2010

“…Hence, it is relatively easy to spatialize persons in 2000 and 2010 to 1 km *1 km based on the population spatialization method proposed before (Yu et al, 2018). Firstly, the polynomial regression functions were built according to the relationship between the total population and sum DN values at county level.…”

“…Detailed information of population distribution and dynamics is of great significance on exploring the relationship between human activities and urbanization, land use changes, as well as the ecological effects (Yu et al, 2018). However, the most authoritative and reliable population datasets in China we can get are census data, which cannot describe the internal differences of population distribution in each administrative unit (Zeng et al, 2011).…”

“…Most scholars, such as Sutton (1997)， Lo (2001, Zhuo et al (2009), Zeng et al (2011 and Yu et al (2018), are devoted to studying population spatialization methods at national or regional scales using NLT products. However, studies on population distribution in populous areas like urban agglomerations are relatively scarce.…”