Nighttime Stable Light Data Research Articles

Analysing the spatial structure of urban growth across the Yangtze River Middle reaches urban agglomeration in China using NPP-VIIRS night-time lights data

The spatial structure of urban agglomeration reveals the self-organization process of internal factors from the outside. As China ushers into a critical stage with the boosting of urbanization and the booming of the economy, measuring the spatial structure of urban agglomerations is vital to urban planning, regional development, etc. Nighttime stable light data from the National Polar–Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) night-time light (NTL) series dataset offers a novel source that potentially simulates spatial variations in socio-economic activities. The present study aimed to scientifically guide wide applications of the NPP-VIIRS data to define and measure the evolution of urban agglomeration from a spatial–temporal perspective. Yangtze River Middle Reaches (YRMR) Urban Agglomeration (UA) in Central China was taken as the case study region to systematically identify and measure its spatial configuration of urban growth. The light index model and the method of the rank clock were performed to quantify the dynamics of rank-order distributions. In the meantime, spatial statistics and location coefficient index were used to identify the nature and heterogeneity of spatial structure. According to our findings, NPP-VIIRS data can offer insights into the applications to analyze urbanization processes and reveal the dynamics of urban expansion. The proposed framework will help understand the nature and unbalance of spatial configurations of urban economic growth within urban agglomeration and lay a theoretical basis for the policy-making of regional spatial planning.

An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration

Abstract. The nighttime light (NTL) satellite data have been widely used to investigate the urbanization process. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference in their spatial resolutions and sensor design requires a cross-sensor calibration of these two datasets for analyzing a long-term urbanization process. Different from the traditional cross-sensor calibration of NTL data by converting NPP-VIIRS to DMSP-OLS-like NTL data, this study built an extended time series (2000–2018) of NPP-VIIRS-like NTL data through a new cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL data (2013–2018). The proposed cross-sensor calibration is unique due to the image enhancement by using a vegetation index and an auto-encoder model. Compared with the annual composited NPP-VIIRS NTL data in 2012, our product of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel and city levels with R2 of 0.87 and 0.95, respectively. We also found that our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our extended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial pattern and temporal consistency which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socioeconomic activities for a longer time period compared to existing products. The extended time series (2000–2018) of nighttime light data is freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020).

A Saturated Light Correction Method for DMSP-OLS Nighttime Stable Light Data by Remote and Social Sensing Data

The defense meteorological satellite program- operational line-scan system nighttime stable light (NTL) data have been widely used to evaluate the intensity of human activities. However, the sensor's defects lead to the saturation phenomenon, which greatly limits the reliability of the research results based on NTL data. Thus, this article has attempted to propose a new spectral index—the points of interest, road network and EVI adjusted NTL index (PREANTLI) to effectively correct saturated pixels. To evaluate the desaturation effect, the PREANTLI was compared with existing saturation correction data across three aspects: the capacity to display differences in light intensities; the similarity with National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data; and the capacity to estimate the gross domestic product (GDP) and electric power consumption (EPC). The results showed that the PREANTLI can more easily identify light intensity differences than other indexes. The PREANTLI presents a strong linear correlation with the NPP/VIIRS data and socioeconomic statistics (GDP and EPC) at provincial and municipal scales. It is worth noting that, on the pixel scale, the correlation between PREANTLI and NPP/VIIRS ( R 2 = 0.63) is far higher than that of the other two existing saturation correction index whose R2 are both below 0.45. Thus, the PREANTLI can be considered as a reasonable index that is not only easy to calculate but can also better alleviate light intensity saturation and emphasize light differences within a city than other indexes.

Using Vegetation Indices to Characterize Vegetation Cover Change in the Urban Areas of Southern China

Vegetation is essential for the provision of ecosystem services. To ensure that ecosystem services are provided, urban planning must consider vegetation cover in both urban core areas and in peripheral areas. This study explored changes in vegetation cover in both urban core and peripheral areas (buffers of 5, 10 and 15 km from the urban core areas) and the relationships between these changes and urban development. To compare the differences in vegetation cover between the urban core and peripheral areas, we developed two vegetation indices, the vegetation influence strength index (VISI) and the vegetation restoration index (VRI), using normalized difference vegetation index datasets and nighttime stable light data from 50 cities in southern China. Using these two indices, we clustered the cities into four types based on their vegetation cover characteristics. The city types varied among the different regions of the study area and cities of the same type tended to occur together geographically. In addition, the two indices showed temporal changes during the study period: A downtrend from 1992 to 2000, an uptrend from 2000 to 2005, and stability from 2005 to 2013. Furthermore, in all cities, the range of the VISI was larger in the peripheral areas than in the urban core areas. However, the range of the VRI was smaller in the peripheral areas than in the urban core areas. As the distance to the urban core areas increased, the fluctuation range of both the VISI and VRI decreased, suggesting that there was less vegetation disturbance farther from the urban core areas. We also showed that changes in vegetation cover were related to differences in urbanization rates, gross domestic products, population densities, and stages of urban development among the cities. These results provide scientific support for urban planning and sustainable development in rapid urbanization areas.

Modeling the spatiotemporal dynamics of industrial sulfur dioxide emissions in China based on DMSP-OLS nighttime stable light data.

Due to the rapid economic growth and the heavy reliance on fossil fuels, China has become one of the countries with the highest sulfur dioxide (SO2) emissions, which pose a severe challenge to human health and the sustainable development of social economy. In order to cope with the serious problem of SO2 pollution, this study attempts to explore the spatial temporal variations of industrial SO2 emissions in China utilizing the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime stable light (NSL) data. We first explored the relationship between the NSL data and the statistical industrial SO2 emissions at the provincial level, and confirmed that there was a positive correlation between these two datasets. Consequently, 17 linear regression models were established based on the NSL data and the provincial statistical emissions to model the spatial-temporal dynamics of China’s industrial SO2 emissions from 1997 to 2013. Next, the NSL-based estimated results were evaluated utilizing the prefectural statistical industrial SO2 emissions and emission inventory data, respectively. Finally, the distribution of China’s industrial SO2 emissions at 1 km spatial resolution were estimated, and the temporal and spatial dynamics were explored from multiple scales (national scale, regional scale and scale of urban agglomeration). The results show that: (1) The NSL data can be successfully applied to estimate the dynamic changes of China’s industrial SO2 emissions. The coefficient of determination (R2) values of the NSL-based estimation results in most years were greater than 0.6, and the relative error (RE) values were less than 10%, when validated by the prefectural statistical SO2 emissions. Moreover, compared with the inventory emissions, the adjusted coefficient of determination (Adj.R-Square) reached 0.61, with the significance at the 0.001 level. (2) During the observation period, the temporal and spatial dynamics of industrial SO2 emissions varied greatly in different regions. The high growth type was largely distributed in China’s Western region, Central region, and Shandong Peninsula, while the no-obvious-growth type was concentrated in Western region, Beijing-Tianjin-Tangshan and Middle south of Liaoning. The high grade of industrial SO2 emissions was mostly concentrated in China’s Eastern region, Western region, Shanghai-Nanjing-Hangzhou and Shandong Peninsula, while the low grade mainly concentrated in China’s Western region, Middle south of Liaoning and Beijing-Tianjin-Tangshan. These results of our research can not only enhance the understanding of the spatial-temporal dynamics of industrial SO2 emissions in China, but also offer some scientific references for formulating feasible industrial SO2 emission reduction policies.

Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery

Climate warming due to carbon emissions has been recognized as a threat to food security, human health and natural ecosystem, and carbon emission reduction is a challenging job for each country in the world. In this study, an ensemble structure-based neural network model (NNEnsemble) is proposed to analyze the nonlinear relationship between the Defense Meteorological Satellite Program Operational Line-Scan System (DMSP-OLS) nighttime stable light (NSL) data, and province-scale statistical data on carbon emissions. Given the challenge of obtaining urban-scale carbon emission data, a weighted coefficient strategy by using the NSL data were employed to analyze the carbon emissions at the urban scale. The performance of the proposed method was found to be superior to that of comparable methods with respect to various evaluation indices. Under these circumstances, a hot spot and Standard deviational ellipse analysis of three northeast provinces in China was conducted from 1998 to 2013. The results can promote a better understanding of the spatio-temporal characteristics of carbon emissions in three northeastern Chinese provinces. Moreover, the developed application software based on NNEnsemble can serve as a basis for the development of carbon emission mitigation policies for other provinces.

Assessment of Night-Time Lighting for Global Terrestrial Protected and Wilderness Areas

Protected areas (PAs) play an important role in biodiversity conservation and ecosystem integrity. However, human development has threatened and affected the function and effectiveness of PAs. The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night-time stable light (NTL) data have proven to be an effective indicator of the intensity and change of human-induced urban development over a long time span and at a larger spatial scale. We used the NTL data from 1992 to 2013 to characterize the human-induced urban development and studied the spatial and temporal variation of the NTL of global terrestrial PAs. We selected seven types of PAs defined by the International Union for Conversation of Nature (IUCN), including strict nature reserve (Ia), wilderness area (Ib), national park (II), natural monument or feature (III), habitat/species management area (IV), protected landscape/seascape (V), and protected area with sustainable use of natural resources (VI). We evaluated the NTL digital number (DN) in PAs and their surrounding buffer zones, i.e., 0–1 km, 1–5 km, 5–10 km, 10–25 km, 25–50 km, and 50–100 km. The results revealed the level, growth rate, trend, and distribution pattern of NTL in PAs. Within PAs, areas of types V and Ib had the highest and lowest NTL levels, respectively. In the surrounding 1–100 km buffer zones, type V PAs also had the highest NTL level, but type VI PAs had the lowest NTL level. The NTL level in the areas surrounding PAs was higher than that within PAs. Types Ia and III PAs showed the highest and lowest NTL growth rate from 1992 to 2013, respectively, both inside and outside of PAs. The NTL distributions surrounding the Ib and VI PAs were different from other types. The areas close to Ib and VI boundaries, i.e., in the 0–25 km buffer zones, showed lower NTL levels, for which the highest NTL level was observed within the 25–100 km buffer zone. However, other types of PAs showed the opposite NTL patterns. The NTL level was lower in the distant buffer zones, and the lowest night light was within the 1–25 km buffer zones. Globally, 6.9% of PAs are being affected by NTL. Conditions of wilderness areas, e.g., high latitude regions, Tibetan Plateau, Amazon, and Caribbean, are the least affected by NTL. The PAs in Europe, Asia, and North America are more affected by NTL than South America, Africa, and Oceania.

City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data

Accelerating urbanization has created tremendous pressure on the global environment and energy supply, making accurate estimates of energy use of great importance. Most current models for estimating electric power consumption (EPC) from nighttime light (NTL) imagery are oversimplified, ignoring influential social and economic factors. Here we propose first classifying cities by economic focus and then separately estimating each category’s EPC using NTL data. We tested this approach using statistical employment data for 198 Chinese cities, 2015 NTL data from the Visible Infrared Imaging Radiometer Suite (VIIRS), and annual electricity consumption statistics. We used cluster analysis of employment by sector to divide the cities into three types (industrial, service, and technology and education), then established a linear regression model for each city’s NTL and EPC. Compared with the estimation results before city classification (R2: 0.785), the R2 of the separately modeled service cities and technology and education cities increased to 0.866 and 0.830, respectively. However, the results for industrial cities were less consistent due to their more complex energy consumption structure. In general, using classification before modeling helps reflect factors affecting the relationship between EPC and NTL, making the estimation process more reasonable and improving the accuracy of the results.

Directional Analysis of Urban Expansion Based on Sub-pixel and Regional Scale: A Case Study of Main Districts in Guangzhou, China

Multi-scale data have had a wide-ranging level of performance in the area of urban change monitoring. Herein we investigate the correlation between the impervious surface fraction (ISF) and the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime stable light (NTL) data with respect to the urban expansion in the main districts of Guangzhou. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Image (OLI) data from 1988 to 2015 were used to extract the ISF using the linear spectral mixture analysis model and normal difference build-up index at the sub-pixel scale. DMSP/OLS NTL data from 1992 to 2013 were calibrated to illustrate the urban nighttime light conditions at the regional scale. Urban expansion directions were identified by statistics and kernel density analysis for the ISF study area at the sub-pixel scale. In addition, the correlation between the ISF and DMSP/OLS NTL data were illustrated by linear regression analysis. Furthermore, Profile Graph in ArcGIS was employed to illustrate the urban expansion from the differences in correlation in different directions. The conclusions are as follows: 1) The impervious surface (IS) in the study area has expanded to the northeast and the east, starting with the old urban zones, and the high-density IS area has increased by 321.14 km2. 2) The linear regression analysis reveals a positive correlation between the ISF and the DMSP/OLS NTL data. The multi-scale data changes are consistent with the actual urban planning of Guangzhou. 3) The DMSP/OLS NTL data overestimate the urban extent because of its saturation and blooming effects, causing its correlation with ISF to decrease. The pattern of urban expansion influences the saturation and blooming effects of the DMSP/OLS NTL data.

A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data

Timely and reliable estimation of electricity power consumption (EPC) is essential to the rational deployment of electricity power resources. Nighttime stable light (NSL) data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) have the potential to model global 1-km gridded EPC. A processing chain to estimate EPC includes: (1) NSL data correction; and (2) regression model between EPC statistics and NSL data. For the global gridded EPC estimation, the current approach is to correct the global NSL image in a uniform manner and establish the linear relationships between NSL and EPC. However, the impacts of local socioeconomic inconsistencies on the NSL correction and model establishment are not fully considered. Therefore, in this paper, we propose a novel locally adaptive method for global EPC estimation. Firstly, we set up two options (with or without the correction) for each local area considering the global NSL image is not saturated everywhere. Secondly, three directions (forward, backward, or average) are alternatives for the inter-annual correction to remove the discontinuity effect of NSL data. Thirdly, four optional models (linear, logarithmic, exponential, or second-order polynomial) are adopted for the EPC estimation of each local area with different socioeconomic dynamic. Finally, the options for each step constitute all candidate processing chains, from which the optimal one is adaptively chosen for each local area based on the coefficient of determination. The results demonstrate that our product outperforms the existing one, at global, continental, and national scales. Particularly, the proportion of countries/districts with a high accuracy (MARE (mean of the absolute relative error) ≤ 10%) increases from 17.8% to 57.8% and the percentage of countries/districts with inaccurate results (MARE > 50%) decreases sharply from 23.0% to 3.7%. This product can enhance the detailed understanding of the spatiotemporal dynamics of global EPC.

ESTIMATING INDUSTRIAL STRUCTURE CHANGES IN CHINA USING DMSP – OLS NIGHT-TIME LIGHT DATA DURING 1999–2012

Abstract. The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) night-time light imagery has been proved to be a powerful tool to monitor economic development with its relatively high spatial resolution at large scales. Night-time lights caused by human activities derived from DMSP-OLS satellite imagery are widely used in socioeconomic parameter estimations and urbanization monitoring. In this paper, DMSP-OLS night-time stable light data from 1999 to 2012 are utilized to analyze inter-annual variation in GDP of per unit light intensity (RGDP) in China. Furthermore, RGDP was compared with statistical data of the tertiary industry structure for 28 provincial regions. The results show that the provincial RGDP decreased abruptly in 2001–2002, 2008–2009 and 2011–2012, which is consistent with the proportional growth of the tertiary industry in GDP. These results indicate that the changes in RGDP can reflect tertiary industry structural changes in China's province-level regions.

Detecting spatiotemporal dynamics of PM2.5 emission data in China using DMSP-OLS nighttime stable light data

Haze pollution in China is getting worse with the rapid growth of economy and urbanization rate, which is harmful to human health and closely related with China's sustainable development. In response to PM2.5 pollution problem in China, this study first analyzed the correlation between the intercalibrated Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) sensor nighttime stable light (NSL) data and statistical PM2.5 emissions at the provincial level from 1992 to 2012 respectively and the results demonstrated that there was a positive correlation between the intercalibrated DMSP-OLS NSL data and PM2.5 emissions. Then linear regression analysis was proposed to simulate spatiotemporal dynamics of PM2.5 emission at the 1 km resolution level in China by the intercalibrated DMSP-OLS NSL data and PM2.5 emission. Spatiotemporal dynamics of PM2.5 emission were analyzed from national scale down to regional and urban agglomeration scales. The results clearly showed that the variations of PM2.5 emissions in different regions and urban agglomerations were different and large. The high growth type and high grade of PM2.5 emissions were mainly located in the Eastern region, Central region, Shandong Peninsula and Beijing-Tianjin-Tangshan, with significant lower concentrations in the Western region, Northeastern region, Sichuan-Chongqing and Middle south of Liaoning. Considering the spatial and temporal patterns of PM2.5 emissions between the four economic regions, the mitigation strategies for Eastern and Central China should mainly focus on the industry structure adjustments, while Western and Northern China should pay more attention to the optimizations of regional energy structures and improvements of energy efficiencies. The results of this study is not only beneficial to understand accurately the regional discrepancies of spatiotemporal PM2.5 emission dynamics, but also helpful for proposing mitigation policies in air pollution control and providing scientific support for regional sustainable development.

Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data

Delay in publication of energy statistics prevents a timely assessment of progress towards meeting targets for energy saving and emission reduction in China. This makes it difficult to meet the requirements to rapidly monitor and evaluate energy consumption for each province. In this study, an alternative approach is provided to estimate the energy consumption by using satellite remote sensing data. We develop spatio-temporal geographically weighted regression models to simulate energy consumption of provinces in China based on the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) global stable night-time light data. The models simulate China’s energy consumption accurately with the goodness of fit higher than 99%. Generally, the national average annual energy consumption is 2.8 billion tonnes of coal equivalent in China between 2000 and 2013, which is close to the actual value with errors smaller than 0.1%. From both temporal and spatial dimensions, the relative errors are smaller than 5.5% at the provincial level. Therefore, the use of satellite night-time light data provides a useful reference in monitoring and assessing provincial energy consumption in China.

Regional inequalities in comprehensive development in China from 1992 to 2013: Comparative estimation based on statistical and DMSP/OLS data

AbstractRegional inequality has attracted considerable interest among academics and policy makers. However, few studies have focused on regional inequality in comprehensive development. This study quantitatively estimated the regional inequalities and imbalances in China at multiple scales using gross domestic product (GDP), population, and the inter‐calibrated Defense Meteorological Satellite Program’s Operational Line‐Scan System (DMSP/OLS) nighttime stable light (NSL) data for 1992–2013. The results are as follows. First, the GDP‐derived inequalities exhibited an inverted U‐shaped pattern, while the NSL‐derived continued to decline. Second, for both GDP and NSL data, the inequality within the western region contributed most to the within‐region inequalities, while the eastern region contributed least. Third, the highest inter‐provincial inequality was observed in the developed area, while disparity within the underdeveloped area was the lowest. Finally, at the provincial level, the imbalances in comprehensive development generally decreased from 1992 to 2013. Moreover, a correlation analysis between the GDP‐ and NSL‐derived inequalities showed that there were some similarities in inequalities during 1992–2013, especially in the eastern region. These findings highlight that the regional inequalities in economic development and comprehensive development exhibit different tendencies, and hence it is crucial for both scholars and government to focus on this issue.

The Evolution of the Urban Spatial Pattern in the Yangtze River Economic Belt: Based on Multi-Source Remote Sensing Data

The development of the Yangtze River Economic Belt (YREB) is an important national regional development strategy and a strategic engineering development system. In this study, the evolution of urban spatial patterns in the YREB from 1990 to 2010 was mapped using the nighttime stable light (NSL) data, multi-temporal urban land products, and multiple sources of geographic data by using the rank-size distribution and the Gini coefficient method. Through statistical results, we found that urban land takes on the feature of “high in the east and low in the west”. The study area included cities of different development stages and sizes. The nighttime light increased in most cities from 1992 to 2010, and the rate assumed an obvious growth tendency in the three urban agglomerations in the YREB. The results revealed that the urban size distribution of the YREB is relatively dispersed, the speed of urban development is unequal, and the trend of urban size structure shows a decentralized distribution pattern that has continuously strengthened from 1990 to 2010. Affected by factors such as geographical conditions, spatial distance, and development stage, the lower reaches of the Yangtze River have developed rapidly, the upper and middle reaches have developed large cities, and a contiguous development trend is not obvious. The evolution of urban agglomerations in the region presents a variety of spatial development characteristics. Jiangsu, Zhejiang, and Shanghai have entered a phase of urban continuation, forming a more mature interregional urban agglomeration, while the YREB inland urban agglomerations are in suburbanization and multi-centered urban areas. At this stage, the conditions for the formation of transregional urban agglomerations do not yet exist, and there are many uncertainties in the boundary and spatial structure of each urban agglomeration.

Exploring China's 21-year PM10 emissions spatiotemporal variations by DMSP-OLS nighttime stable light data

With the rapid growth of economy and urbanization rate, haze pollution is becoming more and more serious in China, and it is harmful to human health and China's sustainable development. For exploring spatiotemporal variations of PM10 pollution problem from 1992 to 2012 in China, we first proved that there existed a positive correlation between the intercalibrated Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) sensor nighttime stable light (NSL) data and PM10 emissions in this study. Then linear regression analysis was utilized to model spatiotemporal PM10 emission variations in China by the intercalibrated DMSP-OLS NSL data. PM10 emissions at prefectural level were selected as validation data for verifying the accuracy of spatiotemporal PM10 emissions variations and the results demonstrated that the DMSP-OLS NSL data can simulate PM10 emissions at 1 km resolution well. At last, spatiotemporal PM10 emission dynamics were analyzed from national scale down to regional and urban agglomeration scales. The results clearly noticed that the variations of PM10 emissions in different regions and urban agglomerations were different. The high-growth type of PM10 emissions was mainly located in the Eastern region, Central region, Western region, Shandong Peninsula, Wuhan Metropolitan Area and Beijing-Tianjin-Tangshan, with significant lower concentrations in the Northeastern region, Middle south of Liaoning and Pearl River Delta. The high grade of PM10 emissions was mainly distributed in the Eastern region, Central region and Shandong Peninsula, with significant lower concentrations in the Western region, Northeastern region, Sichuan-Chongqing. Considering the difference of temporal and spatial variations of PM10 emissions in the four regions, the mitigation strategy in the Eastern and Central China should mainly focus on the adjustment of industrial structure, while in Western and Northern China, we should pay attention to the improvement and optimization of regional energy structure and energy efficiency.

A gravity model and exploratory spatial data analysis of prefecture-scale pollutant and CO2 emissions in China

The aim of this study was to identify the spatial characteristics of CO2, industrial SO2 & dust emissions, and industrial wastewater discharge at the prefecture level in China. A gravity model and exploratory spatial data analysis were utilized in conjunction with nighttime stable light data from a remote sensing product to comprehensively investigate the spatial characteristics of emissions. Empirical analysis of this unique data set indicates that: 1) During the sample period, the center of gravity for CO2 and pollutant emissions was concentrated in cities of Henan Province, especially Shangqiu City and Xinyang City. 2) Pollutant and CO2 emissions presented statistically significant spatial autocorrelations at the prefecture level. 3) Most cities exhibited statistically insignificant spatial clustering for pollutant and CO2 emissions over the study period; however, a few cities presented positive spatial autocorrelation as the principal spatial association. This study drew some interesting observations, which will be of valuable use in policy-making and the implementation of emission reduction targets at different administrative levels.

Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road

Fully understanding spatiotemporal patterns of electric power consumption (EPC) is one of the key questions related to sustainable socioeconomic and environmental development in countries along the Silk Road Economic Belt and the 21st-Century Maritime Silk Road (hereinafter referred to as the Belt and Road countries). However, studies about spatiotemporal patterns of EPC in the Belt and Road countries are still scarce due to the lack of reliable data. This study attempted to investigate spatiotemporal patterns of EPC in the Belt and Road countries from multiple perspectives. Firstly, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime stable light data were used to estimate EPC from 1992 to 2013. Subsequently, the mathematical statistic method, standard deviational ellipse, rank size rule, and correlation analysis were employed to evaluate the EPC change in detail. The results reveal that the EPC growth mainly occurs in the developing countries, especially in China. The geographical distribution of EPC in the Belt and Road countries is oriented in the Northwest-Southeast direction between 1992 and 2013. Based on the rank size rule analysis, the slope values of q are −2.392 and −2.175 between 1992 and 2013, with an average R2 value of 0.664, indicating a clear clustering pattern of EPC. It is also proved that GDP is a more important impact factor to EPC than the population. Our findings can offer an effective way to understand spatiotemporal evolution characteristics of EPC in the Belt and Road countries, and provide references for regional socioeconomic development and cooperation.

Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data

The rapid development of global industrialization and urbanization has resulted in a great deal of electric power consumption (EPC), which is closely related to economic growth, carbon emissions, and the long-term stability of global climate. This study attempts to detect spatiotemporal dynamics of global EPC using the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime stable light (NSL) data. The global NSL data from 1992 to 2013 were intercalibrated via a modified invariant region (MIR) method. The global EPC at 1km resolution was then modeled using the intercalibrated NSL data to assess spatiotemporal dynamics of EPC from a global scale down to continental and national scales. The results showed that the MIR method not only reduced the saturated lighted pixels, but also improved the continuity and comparability of the NSL data. An accuracy assessment was undertaken and confined that the intercalibrated NSL data were relatively suitable and accurate for estimating EPC in the world. Spatiotemporal variations of EPC were mainly identified in Europe, North America, and Asia. Special attention should be paid to China where the high grade and high-growth type of EPC covered 0.409% and 1.041% of the total country area during the study period, respectively. The results of this study greatly enhance the understanding of spatiotemporal dynamics of global EPC at the multiple scales. They will provide a scientific evidence base for tracking spatiotemporal dynamics of global EPC.

Determinants of urban expansion and their relative importance: A comparative analysis of 30 major metropolitans in China

Metropolitan has progressively emerged as a new form of urban governance. Understanding the urban expansion process and its determinants at metropolitan level should provide critical references for urban management and planning. Using time-series nighttime stable light data (1993–2012), this paper captures the full continuum of urban expansion process in 30 major metropolitans of China. Greater expansion intensity and faster expansion speed are observed in metropolitans along the eastern coast. Conversely, the metropolitans in northeastern China exhibit lower urban expansion intensity and speed. Potential determinants are selected based on extensive literature review from four categories: economic, social, demographic, and natural. Multivariable linear regression and variances partitioning are employed to identify and compare the determinants of urban expansion and their relative importance across the 30 metropolitans. Results show that urban expansion is not only subjected to different categories of determinants but also influenced by their combinations. More specifically, the identified determinants vary with metropolitans but present some similarities. Economic growth, industrial development, and economic structural transformation are the key economic determinants. Population and labor growth, population urbanization, and population structure act as essential demographic determinants. Primary social determinants include infrastructure construction, energy consumption, and real estate development, while dominant natural determinants are the amount and topology of available land. Cluster analysis is further applied and it generates three major driving forces profiles among the 30 metropolitans, with respect to the relative importance of different categories of determinants and their combinations. The driving force profiles should be useful for managers to set priorities in decision making procedure and develop optimal urban strategies. Our study is believed to advance the understanding of driving mechanism governing urban expansion at metropolitan level and further provide deeper spatial insights for international habitat research.