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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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Chang, Y., Wang, S., Zhou, Y., Wang, L., & Wang, F. (2020). A Novel Method of Evaluating Highway Traffic Prosperity Based on Nighttime Light Remote Sensing. Remote Sensing, 12(1), 102.
Abstract: As the backbone and arteries of a comprehensive transportation network, highways play an important role in improving people’s living standards and promoting economic growth. However, globally, there is limited quantifiable data evaluating the highway traffic state, characteristics, and performance. From the 1960s to the present, remote sensing has been regarded as the most effective technology for long-term and large-scale monitoring of surface information. However, how to reflect the dynamic “flow” information of traffic with a static remote sensing image has always been a difficult problem that is hard to solve in the field. This study aims to construct a method of evaluating highway traffic prosperity using nighttime remote sensing. First, based on nighttime light data that indicate social and economic activities, a highway-oriented method was proposed to extract highway nighttime light data from 2015 annual nighttime light data of the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (SNPP-VIIRS). Subsequently, Pearson correlation analysis was used to fit the relationship between freeway traffic flow volume and freeway nighttime light at the provincial level. The results showed that Pearson Correlation Coefficient of freeway nighttime light and freeway traffic flow volume for coach and truck are 0.905 and 0.731, respectively, which are higher than between freeway traffic flow volume for coach and truck and total nighttime light (0.593 and 0.516, respectively). A new index—Highway Nighttime Traffic Prosperity Index (HNTPI)—was proposed to evaluate highway traffic across China. The results showed that HNTPI has a strong correspondence with socio-economic parameters. The Pearson Correlation Coefficient of HNTPI and gross domestic product (GDP) per capita, consumption per capita, and population are 0.772, 0.895, and 0.968, respectively. There is a huge spatial heterogeneity in China nighttime traffic, the prosperity degree of highway traffic in developed coastal areas is obviously higher than that inland. The national general highway is the most prosperous highway at night and the national general highway nighttime prosperity of Shanghai reached 22.34%. This research provides basic data for the long-term monitoring and evaluation of regional traffic operation at night and research on the correlation between regional highway construction and the economy.
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Chen, H., Xiong, X., Geng, X., & Twedt, K. (2019). Stray-light correction and prediction for Suomi National Polar-orbiting Partnership visible infrared imaging radiometer suite day-night band. J. Appl. Rem. Sens., 13(02), 1.
Abstract: The Suomi National Polar-orbiting Partnership visible infrared imaging radiometer suite instrument has successfully operated since its launch in October 2011. Stray-light contamination is much larger than prelaunch expectations, and it causes a major decrease in quality of the day-night band night imagery when the spacecraft is crossing the Northern or Southern day-night terminators. The stray light can be operationally estimated using Earth-view data that are measured over dark surfaces during the new moon each month. More than 7 years of nighttime images have demonstrated that the stray-light contamination mainly depends on the Earth–Sun–spacecraft geometry, so its intensity is generally estimated as a function of the satellite zenith angle. In practice, stray-light contamination is also detector- and scan-angle-dependent. Previous methods of stray-light prediction generally rely on using the known stray light level from the same month in the previous year, when the Earth–Sun–spacecraft geometries had been similar. We propose a new method to predict stray-light contamination. The Kullback–Leibler similarity metric is used as a method to combine data from multiple years with appropriate adjustments for degradation and geometry drifts in order to calculate a fused stray-light contamination correction. The new method provides an improved prediction of stray-light contamination compared to the existing methods and may be considered for future use in the real-time NASA Level-1B products.
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