Cao, C., Shao, X., & Uprety, S. (2013). Detecting Light Outages After Severe Storms Using the S-NPP/VIIRS Day/Night Band Radiances. IEEE Geosci. Remote Sensing Lett., 10(6), 1582–1586.
Abstract: Power outages after a major storm affect the lives of millions of people and cause massive light outages. The launch of the Suomi National Polar-orbiting Partnership satellite with the Visible Infrared Imaging Radiometer Suite (VIIRS) significantly enhances our capability to monitor and detect light outages with the well-calibrated day/night band (DNB) and to use light loss signatures as indication of regional power outages. This study explores the use of the DNB in quantifying light outages due to the derecho storm in the Washington DC metropolitan area in June 2012 and Hurricane Sandy at the end of October 2012 on the East Coast of U.S. The results show that the DNB data are very useful in detecting power outages by quantifying light loss, but it also has some challenges due to clouds, lunar illumination, and straylight effect. Comparison of light outage and recovery trend determined from DNB data with power company survey shows reasonable agreement, demonstrating the usefulness of DNB in independently verifying and complementing the statistics from power companies.
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Cao, C., Zong, Y., Bai, Y., & Shao, X. (2015). Preliminary study for improving the VIIRS DNB low light calibration accuracy with ground based active light source. Proc. SPIE 9607, Earth Observing Systems XX, 2015, , 96070D.
Abstract: There is a growing interest in the science and user community in the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) low light detection capabilities at night for quantitative applications such as airglow, geophysical retrievals under lunar illumination, light power estimation, search and rescue, energy use, urban expansion and other human activities. Given the growing interest in the use of the DNB data, a pressing need arises for improving the calibration stability and absolute accuracy of the DNB at low radiances. Currently the low light calibration accuracy was estimated at a moderate 15%-100% while the long-term stability has yet to be characterized. This study investigates selected existing night light point sources from Suomi NPP DNB observations and evaluates the feasibility of SI traceable nightlight source at radiance levels near 3 nW·cm−2·sr−1, that potentially can be installed at selected sites for VIIRS DNB calibration/validation. The illumination geometry, surrounding environment, as well as atmospheric effects are also discussed. The uncertainties of the ground based light source are estimated. This study will contribute to the understanding of how the Earth’s atmosphere and surface variability contribute to the stability of the DNB measured radiances, and how to separate them from instrument calibration stability. It presents the need for SI traceable active light sources to monitor the calibration stability, radiometric and geolocation accuracy, and point spread functions of the DNB. Finally, it is also hoped to address whether or not active light sources can be used for detecting environmental changes, such as aerosols.
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Cao, X., Chen, J., Imura, H., & Higashi, O. (2009). A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data. Remote Sensing of Environment, 113(10), 2205–2209.
Abstract: Mapping urban areas at regional and global scales has become an urgent task because of the increasing pressures from rapid urbanization and associated environmental problems. Satellite imaging of stable anthropogenic lights from DMSP-OLS provides an accurate, economical, and straightforward way to map the global distribution of urban areas. To address problems in the thresholding methods that use empirical strategies or manual trial-and-error procedures, we proposed a support vector machine (SVM)-based region-growing algorithm to semi-automatically extract urban areas from DMSP-OLS and SPOT NDVI data. Several simple criteria were used to select SVM training sets of urban and non-urban pixels, and an iterative classification and training procedure was adopted to identify the urban pixels through region growing. The new method was validated using the extents of 25 Chinese cities, as classified by Landsat ETM+ images, and then compared with two common thresholding methods. The results showed that the SVM-based algorithm could not only achieve comparable results to the local-optimized threshold method, but also avoid its tedious trial-and-error procedure, suggesting that the new method is an easy and simple alternative for extracting urban extent from DMSP-OLS and SPOT NDVI data.
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Cao, X., Hu, Y., Zhu, X., Shi, F., Zhuo, L., & Chen, J. (2019). A simple self-adjusting model for correcting the blooming effects in DMSP-OLS nighttime light images. Remote Sensing of Environment, 224, 401–411.
Abstract: Night-time light (NTL) data from the Defense Meteorological Satellite Program (DMSP) Operation Linescan System (OLS) provide important observations of human activities; however, DMSP-OLS NTL data suffer from problems such as saturation and blooming. This research developed a self-adjusting model (SEAM) to correct blooming effects in DMSP-OLS NTL data based on a spatial response function and without using any ancillary data. By assuming that the pixels adjacent to the background contain no lights (i.e., pseudo light pixels, PLPs), the blooming effect intensity, a parameter in the SEAM model, can be estimated by pixel-based regression using PLPs and their neighboring light sources. SEAM was applied to all of China, and its performance was assessed for twelve cities with different population sizes. The results show that SEAM can largely reduce the blooming effect in the original DMSP-OLS dataset and enhance its quality. The images after blooming effect correction have higher spatial similarity with Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) images and higher spatial variability than the original DMSP-OLS data. We also found that the average effective blooming distance is approximately 3.5 km in China, which may be amplified if the city is surrounded by water surfaces, and that the blooming effect intensity is positively correlated to atmospheric quality. The effectiveness of the proposed model will improve the capacity of DMSP-OLS images for mapping the urban extent and modeling socioeconomic parameters.
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Cao, Y., Zhang, J., Yang, M., Guo, B., Liu, M., Yang, L., et al. (2018). Analysis of Lighting Changes in the Tourist City Edogawa Using Nighttime Light Data. J Indian Soc Remote Sens, 46(10), 1617–1623.
Abstract: When assessing remote sensing data, nighttime light data have shortcomings that can be attributed to sensor limitations and the influence of the natural environment. Signal leakage errors in nighttime light data were identified in this study. A regression model was created to reduce signal leakage error by selecting sampling points in coastal area. Lighting variations in Edogawa between 2008 and 2013 were compared based on the Defense Meteorological Satellite Program’s nighttime light data. The lighting variation characteristics in Edogawa from 1992 to 2012 at 5-year intervals were also analyzed. The results show that the 2002 FIFA World Cup held in Japan led Edogawa’s light digital number values to peak in 2002. The annual Edogawa lighting changes from 2007 to 2013 were also explored. The 2008 global financial crisis led to the lowest compounded night light index and average digital number in Edogawa during these 7 years.
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