Analyzing the Relationship between Developed Land Area and Nighttime Light Emissions of 36 Chinese Cities

Abstract

The satellite-observed nighttime light emission (NTLE) data provide a new method for scrutinizing the footprint of human settlements. Changing NTLEs can be attributed to the direct/ indirect influences of highly complex factors that are beyond the ability of simple statistical models to distinguish. Besides, the relatively coarse resolution of the NTLE products combined with light from human settlements may produce misleading results, as the relationship between spatiotemporal heterogeneity in the growth of developed land (e.g., urban and rural residences, shopping centers, industrial parks, mining plants, and transportation facilities) and the associated NTLEs has not been adequately analyzed. In this study, we developed a total nighttime brightness index (TotalNTBI) to measure the NTLEs with the defense meteorological satellite program/operational linescan system (DMSP/OLS) nighttime light data enhanced by sharpening the edges of the pixels. Thirty-six key cities in China were selected to investigate the relationship between the total developed land area and the associated TotalNTBI from 2000 to 2013 using panel regression and a simplified structural equation model (SEM). The results show that the overall trend in TotalNTBI agreed well with that of the total developed land area (mean adjusted R 2 = 0.799). The panel regression models explained approximately 71.8% of the variance of total developed land area and 92.4% of the variance in TotalNTBI. The SEM revealed both the direct and indirect influences of independent variables on the total developed land area and the associated TotalNTBI. This study may provide useful information for decision-makers and researchers engaged in sustainable land development, urban management, and regional developmental inequality, focusing on recent issues, such as retrospective analysis of human footprint with sharpened nighttime NTLE products, the loss of natural and semi-natural land due to the sprawling developed land area indicated by intensively lit area, and the low efficiency of land development indicated by the anomalies of developed land area and associated NTBIs. global change, as the role of humans will affect the earth's future [2,3] and eventually determine the fate of human society.Compared with the conventional census approach with its shortcomings of data scarcity, bias, and uncertainty, the remote sensing of nighttime light emissions (NTLEs) provides a new view method for scrutinizing the footprint of human activities. Nighttime light emissions signals captured by on-board sensors, such as those onboard the defense meteorological satellite program/operational linescan system (DMSP/OLS) in 1992-2013 and the Visible Infrared Imaging Radiometer Suite (VIIRS) since 2012, have accumulated 26 years of nighttime light data. Together with survey and census data, archived nighttime light data effectively reflects the trajectory of human settlements associated with socioeconomic evolution, with a focus on population estimation [4,5], monitoring urban expansion and peri-urb...

Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation

“…However, this approach of a single-parameter (just an exponent parameter of RH to be solved) formula, similar to a fixed-effect model, did not consider the influence of uncertainty deriving from the measurement errors and/or other confounders and thus might not capture the influence from such uncertainty on the results. The other empirical conversion methods considered more influential factors such as particle size [23] and visibility [25] although such covariates are not available for the case study. Many of these existing methods used a conversion formula of a fixed format and used empirical values to fill in several parameters to finish the conversion.…”

“…Compared with the existing methods [15,[20][21][22][23][24][25][26]30,31], although just having a modest improvement (4% for the empirical method) for the test correlation between the simulated GAC and PM 2.5 , this proposed method provides a more flexible framework to fuse the influence of multiple scaling, shift, and other potential factors on the conversion that involves complex atmospheric and chemical processes of aerosols; this method is convenient to train and use in support of the AD tool, and makes the conversion easily adjusted or improved using the data of additional influential factors if available. The polynomial conversion from proxy GAC to PM 2.5 was conducted to employ the stable RSE loss function to obtain a slightly better correlation than linear conversion (0.58 vs. 0.56).…”

“…For the conversion of satellite AOD to ground extinction coefficient, there have been two types of methods [20,21]: empirical relationship between satellite AOD and PM 2.5 , and using available vertical profile information. For empirical conversion, planetary boundary layer height has been used as the divisor to adjust satellite AOD given AOD obtained through the integration of aerosol extinction coefficient by boundary mixing height in most studies [15,[21][22][23][24][25]. Several studies [20,26] employed chemical transport models (CTM) such as the Community Multiscale Air Quality (CMAQ) to simulate the relationship between modeled PM 2.5 and AOD and then used such a relationship as a linear converter of satellite AOD.…”

“…With relative humidity, the hygroscopic growth factor of aerosols was considered in the conversion [15,22,25,30]. Furthermore, Li et al [23] also conducted particle correction of AOD for PM 2.5 and Zeng et al [25] also used visibility for vertical correction. For available vertical profile information in conversion, cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) can provide the extinction profile data for conversion [31] with limited spatiotemporal coverage [17] and coarse spatial resolution of 2 • (latitude) × 5 • (longitude) [27].…”

The Extraction of Urban Built-Up Areas by Integrating Night-Time Light and POI Data—A Case Study of Kunming, China

“…Moreover, night-time light data can be applied in the study of urban space [11] because urban activities are closely correlated with electricity consumption [12], and many studies have proven that the intensity of urban light has a high correlation with urban population distribution [13]. Therefore, night-time light data are currently mainly used in urban expansion [14], urban morphology and structure [15], and social and economic status decisions [16].…”