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Zhang, K., Zhong, X., Zhang, G., Li, D., Su, Z., Meng, Y., et al. (2019). Thermal Stability Optimization of the Luojia 1-01 Nighttime Light Remote-Sensing Camera's Principal Distance. Sensors (Basel), 19(5), 990.
Abstract: The instability of the principal distance of the nighttime light remote-sensing camera of the Luojia 1-01 satellite directly affects the geometric accuracy of images, consequently affecting the results of analysis of nighttime light remote-sensing data. Based on the theory of optical passive athermal design, a mathematical model of optical-passive athermal design for principal distance stabilization is established. Positive and negative lenses of different materials and the mechanical structures of different materials are matched to optimize the optical system. According to the index requirements of the Luojia 1-01 camera, an image-telecentric optical system was designed under the guidance of the established mathematical model. In the temperature range of -20 degrees C to +60 degrees C, the principal distance of the system changes from -0.01 mum to +0.28 mum. After on-orbit testing, the geometric accuracy of the designed nighttime light remote-sensing camera is better than 0.20 pixels and less than index requirement of 0.3 pixels, which indicating that the principal distance maintains good stability on-orbit and meets the application requirements of nighttime light remote sensing.
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Zhou, N., Hubacek, K., & Roberts, M. (2015). Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data. Applied Geography, 63, 292–303.
Abstract: Over the last quarter of a century, analyzing the pace of urbanization and urban economic growth in South Asia has become increasingly important. However, a key challenge relates to the absence of spatially disaggregated national accounts data – in particular, the absence of GDP data for sub-national administrative units and individual cities. The absence of such data limits the scope for detailed empirical analysis of spatial patterns of economic growth, particularly across individual urban settlements or cities. This paper aims to test the suitability of DMSP-OLS Nighttime Lights (NTL) data as a proxy for GDP to analyze detailed spatial patterns of urban economic growth across South Asia over the period 1999–2010. It will help to build an understanding of the nature and heterogeneity of spatial patterns of urban economic growth within the region and contribute to the development of a framework for the usage of NTL to investigate such patterns. Geographic Information System (GIS) is employed to identify the cities and urban agglomerations together with their NTL data in South Asia, and spatial statistics are used to analyze the spatial and temporal patterns of NTL growth. This paper adopts descriptive and inferential statistics to determine the quantitative relationship between NTL and population, urban size, and proximity to the coast. This paper reveals that the inter-annually calibrated NTL data is a good proxy for changes in national and sub-national GDP. In South Asia, the urban NTL hot spots are around major cities with populations between 1.3 and 2.6 million in 1999 and 0.5 to 1.3 million in 2010. Cities in the region have also become more clustered and connected forming urban agglomerations. NTL per unit of land in such clusters tends to be higher than in single cities in South Asia. India, Pakistan, and Sri Lanka tend to have higher NTL (economic) growth on average, while Nepal and Bangladesh have lower growth or declining NTL. There exists a very strong positive linear relation between distance to the coast and the total NTL within that distance, which leads to similar NTL growth rates among inland and coastal cities.
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Zhu, Y., Xu, D., Saleem, A., Ma, R., & Cheng, J. (2019). Can Nighttime Light Data Be Used to Estimate Electric Power Consumption? New Evidence from Causal-Effect Inference. Energies, 12(16), 3154.
Abstract: Nighttime light data are often used to estimate some socioeconomic indicators, such as energy consumption, GDP, population, etc. However, whether there is a causal relationship between them needs further study. In this paper, we propose a causal-effect inference method to test whether nighttime light data are suitable for estimating socioeconomic indicators. Data on electric power consumption and nighttime light intensity in 77 countries were used for the empirical research. The main conclusions are as follows: First, nighttime light data are more appropriate for estimating electric power consumption in developing countries, such as China, India, and others. Second, more latent factors need to be added into the model when estimating the power consumption of developed countries using nighttime light data. Third, the light spillover effect is relatively strong, which is not suitable for estimating socioeconomic indicators in the contiguous regions between developed countries and developing countries, such as Spain, Turkey, and others. Finally, we suggest that more attention should be paid in the future to the intrinsic logical relationship between nighttime light data and socioeconomic indicators.
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Zhuo, L., Zheng, J., Zhang, X., Li, J., & Liu, L. (2015). An improved method of night-time light saturation reduction based on EVI. International Journal of Remote Sensing, 36(16), 4114–4130.
Abstract: Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) night-time light (NTL) data have been widely applied to studies on anthropogenic activities and their interactions with the environment. Due to limitations of the OLS sensor, DMSP NTL data suffer from a saturation problem in central urban areas, which further affects studies based on nocturnal lights. Recently, the vegetation-adjusted NTL urban index (VANUI) has been developed based on the inverse correlation of vegetation and urban surfaces. Despite its simple implementation and ability to effectively increase variations in NTL data, VANUI does not perform well in certain rapidly growing cities. In this study, we propose a new index, denoted enhanced vegetation index (EVI)-adjusted NTL index (EANTLI), that was developed by reforming the VANUI algorithm and utilizing the EVI. Comparisons with radiance-calibrated NTL (RCNTL) and the new Visible Infrared Imager Radiometer Suite (VIIRS) data for 15 cities worldwide show that EANTLI reduces saturation in urban cores and mitigates the blooming effect in suburban areas. EANTLI’s similarity to RCNTL and VIIRS is consistently higher than VANUI’s similarity to RCNTL and VIIRS in both spatial distribution and latitudinal transects. EANTLI also yields better results in the estimation of electric power consumption of 166 Chinese prefecture-level cities. In conclusion, EANTLI can effectively reduce NTL saturation in urban centres, thus presenting great potential for wide-range applications.
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