Abstract
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.
Highlights
Artificial light at night can describe human settlements and monitor human activities [1,2,3]
Linear regression analyses were conducted to examine the relationship between Nighttime light (NTL) data and each built-up environment property, i.e., building height, building coverage, vegetation fraction, and surface-area index
The scatter plots of DMSP/OLS radiance-calibrated NTL data and built-up environment properties are shown in Figure 4, together with R2 values and root-mean-square error (RMSE) values
Summary
Artificial light at night can describe human settlements and monitor human activities [1,2,3]. Nighttime light (NTL) is the fraction of artificial nighttime light emitted upwards which is detected by sensors, and it has been widely used to study many socioeconomic activities [4,5,6,7,8,9]. Mapped urban areas of the United States and China accurately and efficiently using Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL data based on a cluster-based method. Amaral et al [11] estimated urban population in the Brazilian Amazon using DMSP/OLS nighttime light area by a linear regression model. Cinzano et al [19] presented the first atlas of artificial night-sky brightness, and found about one fifth of the global population have lost naked-eye visibility of the Milky Way due to atmospheric scattering of artificial light
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