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Article

Evaluating Saturation Correction Methods for DMSP/OLS Nighttime Light Data: A Case Study from China’s Cities

by 1,2, 1,2, 3, 2,* and 4
1
Key Laboratory of Human Environmental Science and Technology, Shenzhen Graduate School, Peking University, Xili Rd., Shenzhen 518055, Guangdong, China
2
College of Urban and Environmental Sciences, Peking University, 5 Yiheyuan Rd., Beijing 100871, China
3
Department of Urban Planning and Design, The University of Hong Kong, Pokfulam, Hong Kong 999077, China
4
School of Government, Peking University, 5 Yiheyuan Rd., Beijing 100871, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(10), 9853-9872; https://doi.org/10.3390/rs6109853
Received: 30 June 2014 / Revised: 26 September 2014 / Accepted: 1 October 2014 / Published: 16 October 2014
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
Remotely sensed nighttime lights (NTL) datasets derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) have been identified as a good indicator of the urbanization process and have been widely used to study such demographic and economic variables as population distribution and density, electricity consumption, and carbon emission. However, one issue must be considered in the application of NTL data, i.e., saturation in the bright cores of urban centers. In this study, we evaluate four correction methods in China’s cities: the linear regression model and the cubic regression model at the regional level, and the Human Settlement Index (HSI) and the Vegetation Adjusted NTL Urban Index (VANUI) at a pixel level. The results suggest that both correction methods at the regional level improve the correlation between NTL data and socioeconomic variables. However, since the methods can only be used on saturated pixels, the correction effects are limited, as the saturated area in Chinese cities is rather small. HSI and VANUI increase the inter-urban variability within certain cities, especially when their vegetation health and abundance is negatively correlated with NTL. However, the indices may induce bias when applied in a large region with a diverse natural environment and vegetation, and the application of HSI with a relatively high sensitivity of HSI to NDVI may be limited as NTL approaches maximum. Proper methods for reducing saturation effects should thus vary with different study areas and research purposes. View Full-Text
Keywords: DMSP/OLS; saturation; correction methods; China DMSP/OLS; saturation; correction methods; China
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MDPI and ACS Style

Ma, L.; Wu, J.; Li, W.; Peng, J.; Liu, H. Evaluating Saturation Correction Methods for DMSP/OLS Nighttime Light Data: A Case Study from China’s Cities. Remote Sens. 2014, 6, 9853-9872. https://doi.org/10.3390/rs6109853

AMA Style

Ma L, Wu J, Li W, Peng J, Liu H. Evaluating Saturation Correction Methods for DMSP/OLS Nighttime Light Data: A Case Study from China’s Cities. Remote Sensing. 2014; 6(10):9853-9872. https://doi.org/10.3390/rs6109853

Chicago/Turabian Style

Ma, Lin, Jiansheng Wu, Weifeng Li, Jian Peng, and Hao Liu. 2014. "Evaluating Saturation Correction Methods for DMSP/OLS Nighttime Light Data: A Case Study from China’s Cities" Remote Sensing 6, no. 10: 9853-9872. https://doi.org/10.3390/rs6109853

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