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Zhao N.,Texas Tech University | Zhou Y.,Pacific Northwest National Laboratory | Samson E.L.,Mayan Esteem Project
IEEE Transactions on Geoscience and Remote Sensing | Year: 2014

The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime lights imagery has proven to be a powerful remote sensing tool to monitor urbanization and assess socioeconomic activities at large scales. However, the existence of incompatible digital number (DN) values and geometric errors severely limit application of nighttime light image data on multiyear quantitative research. In this paper, we extend and improve previous studies on intercalibrating nighttime lights image data to obtain more compatible and reliable nighttime lights time-series (NLT) image data for China and the U.S. through four steps, namely, intercalibration, geometric correction, steady-increase adjustment, and population data correction. We then use gross domestic product (GDP) data to test the processed NLT image data indirectly and find that sum light (summed DN value of pixels in a nighttime light image) maintains apparent increase trends with relatively large GDP growth rates but does not increase or decrease with relatively small GDP growth rates. As nighttime light is a sensitive indicator for economic activity, the temporally consistent trends between sum light and GDP growth rate imply that brightness of nighttime lights on the ground is correctly represented by the processed NLT image data. Finally, through analyzing the corrected NLT image data from 1992 to 2008, we find that China experienced apparent nighttime lights development in 1992-1997 and 2001-2008, respectively, and the U.S. showed nighttime lights decay in large areas after 2001. © 2014 IEEE. Source

Tian J.,Texas State University | Zhao N.,Mayan Esteem Project | Samson E.L.,Beijing Institute of Technology | Wang S.,Hubei University
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

Since economic reforms in 1978, China's huge growth has led to a rapid increase in demand for freight traffic. Timely assessments of past and current amounts of freight traffic are basis for predicting future demands of freight traffic and appropriately allocating transportation resources. Sum lights (summed digital number (DN) value of pixels of nighttime light imagery) for years 2000, 2004, and 2008 respectively are extracted from corresponding Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) stable lights annual image composites. The sum lights are then regressed on total freight traffic (TFT), railway freight traffic (RFT), and highway freight traffic (HFT), respectively, at the province level. Results show that sum light strongly correlates to TFT and HFT, so sum light can be used as a proxy for TFT and HFT at the province level. However, due to lack of strong correlations between RFT and GDP, sum light is not appropriate to be as a proxy of RFT. Finally we disaggregate each province/municipality's HFT to each pixel in proportion to the DN value of the pixel of the nighttime light image to produce a Chinese HFT map of 2008 with 1 km x 1 km resolution. Compared to traditional census-based freight traffic data, the freight traffic data derived from the nighttime light imagery contain more spatial information. © 2013 IEEE. Source

Zhao N.,Texas Tech University | Samson E.L.,Mayan Esteem Project | Currit N.A.,Texas State University
Photogrammetric Engineering and Remote Sensing | Year: 2015

Brightness of nighttime lights has been used as an indicator for spatial disaggregation of CO2 emission based on an assumed linear relationship between the digital number (DN) values of nighttime light imagery and the amount of CO2 emissions. However, reliability of the linear relationship of these variables has not been thoroughly examined. In this study we find that the actual overall correlations are exponential rather than linear. More specific analyses showed that the DN values of nighttime light imagery first behaves linearly (from 3 to 50) and then exponentially (from 51 to 63), correlating to the amount of CO2emissions. Regardless of the use of a linear or piecewise function, some featured limitations are evident as we developed the methodology. Among significant limitations, CO2emissions were not visualized in unlit areas and a lack of variation existed in regions with the same DN values of nighttime light imagery. Lastly CO2 emissions in urban core areas were grossly under-estimated. © 2015 American Society for Photogrammetry and Remote Sensing. Source

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