Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China

Yu, Bailang; Shi, Kaifang; Hu, Yingjie; Huang, Chang; Chen, Zuoqi; Wu, Jianping

doi:10.1109/jstars.2015.2399416

Cited by 231 publications

(128 citation statements)

References 31 publications

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“…Ma et al [25] investigated correlations of DNB nighttime light radiance with GDP, population, electrical power consumption and paved road areas, and this work indicated that these parameters had a significantly positive linear relation with nighttime light radiance. The application of VIIRS DNB nighttime data, beyond correlating with socio-economic data [26][27][28][29][30][31][32], also can be used to detect social insurgency [33]. Recent work by Li et al [26] compared the capabilities of using DNB and DMSP-OLS data to model the gross regional product (GRP) in China.…”

Section: Introductionmentioning

confidence: 99%

Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China

et al. 2015

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Abstract:Nighttime light imagery offers a unique view of the Earth's surface. In the past, the nighttime light data collected by the DMSP-OLS sensors have been used as an efficient means to correlate regional and global socio-economic activities. With the launch of the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite in 2011, the day-night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard represents a major advancement in nighttime imaging capabilities, because it surpasses its predecessor DMSP-OLS in radiometric accuracy, spatial resolution and geometric quality. In this paper, four variables (total night light, light area, average night light and log average night light) are extracted from nighttime radiance data observed by the VIIRS-DNB composite in 2013 and nighttime digital number (DN) data from the DMSP-OLS stable dataset in 2012, respectively, and correlated with 12 socio-economic parameters at the provincial level in mainland China during the corresponding period. Background noise of DNB composite data is removed using either a masking method or an optimal threshold method. In general, the correlation of these socio-economic data with the total night light and light area of VIIRS-DNB composite data is better than with the DMSP-OLS stable data. The correlations between total night light of denoised DNB composite data and built-up area, gross regional product (GRP) and power consumption are higher than 0.9 and so are the correlations between the light area of denoised DNB composite data and city and town population, built-up area, GRP, power consumption and waste water discharge. However, the correlations of socio-economic data with the average night light and log average night light of VIIRS-DNB composite data are not as good as with the DMSP-OLS stable data. To quantitatively analyze the reasons for the correlation difference, a cubic regression method is developed to correct the saturation effect of the DMSP stable data, and we artificially convert the pixel value of the DNB composite into six bits to match the DMSP stable data format. The correlation results between the processed data and socio-economic data show that the effects of saturation and quantization are two of the reasons for the correlation difference. Additionally, on this basis, we estimate the total night light ratio between saturation-corrected DMSP stable data and finite quantization DNB composite data, and it is found that the ratio is~11.28˘4.02 for China. Therefore, it appears that a different acquisition time is the other reason for the correlation difference.

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Section: Introductionmentioning

confidence: 99%

Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China

et al. 2015

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“…To date, many regression models have been used for delineating the quantitative relationships between socioeconomic variables and VIIRS nighttime light signals [12,13,27,28,31,32]. Because lighting variables and socioeconomic parameters are different for diverse research scales, a single regression model may not be able to estimate the GDP accurately at different levels of administrative unit.…”

Section: Regression Model and Simulation Of Gdpmentioning

confidence: 99%

GDP Spatialization and Economic Differences in South China Based on NPP-VIIRS Nighttime Light Imagery

et al. 2017

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Accurate data on gross domestic product (GDP) at pixel level are needed to understand the dynamics of regional economies. GDP spatialization is the basis of quantitative analysis on economic diversities of different administrative divisions and areas with different natural or humanistic attributes. Data from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar-orbiting Partnership (NPP) satellite, are capable of estimating GDP, but few studies have been conducted for mapping GDP at pixel level and further pattern analysis of economic differences in different regions using the VIIRS data. This paper produced a pixel-level (500 m × 500 m) GDP map for South China in 2014 and quantitatively analyzed economic differences among diverse geomorphological types. Based on a regression analysis, the total nighttime light (TNL) of corrected VIIRS data were found to exhibit R 2 values of 0.8935 and 0.9243 for prefecture GDP and county GDP, respectively. This demonstrated that TNL showed a more significant capability in reflecting economic status (R 2 > 0.88) than other nighttime light indices (R 2 < 0.52), and showed quadratic polynomial relationships with GDP rather than simple linear correlations at both prefecture and county levels. The corrected NPP-VIIRS data showed a better fit than the original data, and the estimation at the county level was better than at the prefecture level. The pixel-level GDP map indicated that: (a) economic development in coastal areas was higher than that in inland areas; (b) low altitude plains were the most developed areas, followed by low altitude platforms and low altitude hills; and (c) economic development in middle altitude areas, and low altitude hills and mountains remained to be strengthened.

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“…Frolking et al [63] concluded that verticality of built-up infrastructure had a sharp increase for Chinese cities and that it was not linearly reflected by nighttime satellite imageries. Further refinements can be made by using much more detailed nighttime data (e.g., VIIRS) [64][65][66] and high resolution land cover and land use data. Although this study focuses on the benefits of NTE, NTE may also bring some negative effects, such as the increase of criminal activities, noise, and environmental pollutions.…”

Section: Limitation and Future Workmentioning

confidence: 99%

The Dynamic Analysis between Urban Nighttime Economy and Urbanization Using the DMSP/OLS Nighttime Light Data in China from 1992 to 2012

Shao

et al. 2017

Remote Sensing

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Along with rapid urbanization, nighttime activities from places, such as restaurants, pubs and bars, and theatres, have created enormous economic and social benefits. The nighttime economy (NTE), as a newly developed social phenomenon, has been used to describe economic activities at night. However, few studies have investigated urban nighttime economy and its relation to urbanization from nighttime light (NTL) data perspective. To fill this gap, this study proposed a nighttime light economy index (NLEI). The correlation analysis was performed between the NLEI and economic indicators at both the city and provincial levels in China from 1992 to 2012 using the DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) time series data. Results revealed that correlations between the NLEI and all kinds of economic indicators were statistically significant. It was observed that both the urbanization and nighttime economy levels increased greatly from 1992 to 2012 in China. Cities and provinces in east China displayed relatively higher annual growth rates of NLEI compared to those in southwest and northwest China. Based on the quadrant map of urbanization and nighttime economy levels, most of the provincial capitals and provinces in east China were in the advanced coordination pattern while those in west China in the low-level coordination pattern.

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Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China

Cited by 231 publications

References 31 publications

Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China

Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China

GDP Spatialization and Economic Differences in South China Based on NPP-VIIRS Nighttime Light Imagery

The Dynamic Analysis between Urban Nighttime Economy and Urbanization Using the DMSP/OLS Nighttime Light Data in China from 1992 to 2012

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