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Open AccessArticle

Assessing the Impact of the Built-Up Environment on Nighttime Lights in China

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Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
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University of Chinese Academy of Sciences, Beijing 100049, China
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State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
4
Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH 44691, USA
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Department of Geography and the Environment, University of North Texas, Denton, TX 76203, USA
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Guangxi Key Laboratory for Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1712; https://doi.org/10.3390/rs11141712
Received: 15 June 2019 / Revised: 13 July 2019 / Accepted: 17 July 2019 / Published: 19 July 2019
Figuring out the effect of the built-up environment on artificial light at night is essential for better understanding nighttime luminosity in both socioeconomic and ecological perspectives. However, there are few studies linking artificial surface properties to nighttime light (NTL). This study uses a statistical method to investigate effects of construction region environments on nighttime brightness and its variation with building height and regional economic development level. First, we extracted footprint-level target heights from Geoscience Laser Altimeter System (GLAS) waveform light detection and ranging (LiDAR) data. Then, we proposed a set of built-up environment properties, including building coverage, vegetation fraction, building height, and surface-area index, and then extracted these properties from GLAS-derived height, GlobeLand30 land-cover data, and DMSP/OLS radiance-calibrated NTL data. Next, the effects of non-building areas on NTL data were removed based on a supervised method. Finally, linear regression analyses were conducted to analyze the relationships between nighttime lights and built-up environment properties. Results showed that building coverage and vegetation fraction have weak correlations with nighttime lights (R2 < 0.2), building height has a moderate correlation with nighttime lights (R2 = 0.48), and surface-area index has a significant correlation with nighttime lights (R2 = 0.64). The results suggest that surface-area index is a more reasonable measure for estimating light number and intensity of NTL because it takes into account both building coverage and height, i.e., building surface area. Meanwhile, building height contributed to nighttime lights greater than building coverage. Further analysis showed the correlation between NTL and surface-area index becomes stronger with the increase of building height, while it is the weakest when the regional economic development level is the highest. In conclusion, these results can help us better understand the determinants of nighttime lights. View Full-Text
Keywords: nighttime lights; GLAS; LiDAR; land cover; built-up environment nighttime lights; GLAS; LiDAR; land cover; built-up environment
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Wang, C.; Qin, H.; Zhao, K.; Dong, P.; Yang, X.; Zhou, G.; Xi, X. Assessing the Impact of the Built-Up Environment on Nighttime Lights in China. Remote Sens. 2019, 11, 1712.

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