Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

Monitoring Turkey's nighttime light and environmental change

Nighttime lighting (NTL), population growth, and climate change are critical concerns for Turkey. The intensity of nighttime lights in Turkey has significantly increased in recent years, closely associated with rapid population growth and urban expansion. Areas with higher population density exhibit greater nighttime light presence. Nighttime lighting is directly linked to energy consumption and greenhouse gas (GHG) emissions, contributing significantly to global climate change. The rise in nighttime lighting in Turkey exacerbates climate change effects. In this study, data on NTL were gathered from the NOAA/V21 satellite for 2013-2021, the NOAA/CMCFG satellite for average DMSP-OLS radiance values from 2013 to 2023, and the NOAA/VNP46A2 satellite for BRDF-corrected DMSP-OLS NTL data from 2013 to 2023. Night temperature values were extracted from NOAA and MODIS images, and their correlation with NTL data was analyzed. A moderate relationship was observed between NTL and night land surface temperature (LST) (R, 0.32; p-value < 0.05). Population and greenhouse gas emission data were sourced from the Turkish Statistical Institute (TurkStat). Carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorinated gases (F-gases) are direct greenhouse gases. A strong correlation was found between NTL and greenhouse gases (R, 0.8; p-value < 0.05). Population density emerges as a significant determinant of nighttime light intensity. These findings underscore the substantial correlation between nighttime light intensity in Turkey, population dynamics, and GHG emissions. The study suggests that NTL data can inform the development of sustainable environmental policies. Mitigating greenhouse gas emissions necessitates controlling population growth and energy consumption, pivotal steps toward environmental sustainability.

The synergy between protected area effectiveness and economic growth

Protected areas conserve biodiversity and ecosystem functions but might impede local economic growth. Understanding the global patterns and predictors of different relationships between protected area effectiveness and neighboring community economic growth can inform better implementation of the Kunming-Montreal Global Biodiversity Framework. We assessed 10,143 protected areas globally with matched samples to address the non-random location of protected areas. Our results show that protected areas resist human-induced land cover changes and do not limit nightlight increases in neighboring settlements. This result is robust, using different matching techniques, parameter settings, and selection of covariates. We identify four types of relationships between land cover changes and nightlight changes for each protected area: "synergy," "retreat," and two tradeoff relationships. About half of the protected areas (47.5%) retain their natural land cover and do so despite an increase of nightlights in the neighboring communities. This synergy relationship is the most common globally but varies between biomes and continents. Synergy is less frequent in the Amazon, Southeast Asia, and some developing areas, where most biodiversity resides and which suffer more from poverty. Smaller protected areas and those with better access to cities, moderate road density, and better baseline economic conditions have a higher probability of reaching synergy. Our results are promising, as the expansion of protected areas and increased species protection will rely more on conserving the human-modified landscape with smaller protected areas. Future interventions should address local development and biodiversity conservation together to achieve more co-benefits.

Modelling monthly-gridded carbon emissions based on nighttime light data

Timely and accurate implementation of carbon emissions (CE) analysis and evaluation is necessary for policymaking and management. However, previous inventories, most of which are yearly, provincial or city, and incomplete, have failed to reflect the spatial variations and monthly trends of CE. Based on nighttime light (NTL) data, statistical data, and land use data, in this study, a high-resolution (1 km × 1 km) monthly inventory of CE was developed using back propagation neural network, and the spatiotemporal variations and impact factors of CE at multiple administrative levels was evaluated using spatial autocorrelation model and spatial econometric model. As a large province in terms of both economy and population, Guangdong is facing the severe emission reduction challenges. Therefore, in this study, Guangdong was taken as a case study to explain the method. The results revealed that CE increased unsteadily in Guangdong from 2013 to 2022. Spatially, the high CE areas were distributed in the Pearl River Delta region such as Guangzhou, Shenzhen, and Dongguan, while the low CE areas were distributed in West and East Guangdong. The Global Moran's I decreased from 2013 to 2022 at the city and county levels, suggesting that the inequality of CE in Guangdong steadily decreased at these two administrative levels. Specifically, at the city level, the Global Moran's I gradually decreased from 0.4067 in 2013 to 0.3531 in 2022. In comparison, at the county level, the trend exhibited a slower decline, from 0.3647 in 2013 to 0.3454 in 2022. Furthermore, the analysis of the impact factors revealed that the relationship between CE and gross domestic product was an inverted U-shaped, suggesting the existence of the inverted U-shaped Environmental Kuznets Curve for CE in Guangdong. In addition, the industrial structure had larger positive impact on CE at the different levels. The method developed in this study provides a perspective for establishing high spatiotemporal resolution CE evaluation through NTL data, and the improved inventory of CE could help understand the spatial-temporal variations of CE and formulate regional-monthly-specific emission reduction policies.

"Urban-Satellite" estimates in the ABCD Study: Linking Neuroimaging and Mental Health to Satellite Imagery Measurements of Macro Environmental Factors

While numerous studies over the last decade have highlighted the important influence of environmental factors on mental health, globally applicable data on physical surroundings are still limited. Access to such data and the possibility to link them to epidemiological studies is critical to unlocking the relationship of environment, brain and behaviour and promoting positive future mental health outcomes. The Adolescent Brain Cognitive Development (ABCD) Study is the largest ongoing longitudinal and observational study exploring brain development and child health among children from 21 sites across the United States. Here we describe the linking of the ABCD study data with satellite-based "Urban-Satellite" (UrbanSat) variables consisting of 11 satellite-data derived environmental indicators associated with each subject's residential address at their baseline visit, including land cover and land use, nighttime lights, and population characteristics. We present these UrbanSat variables and provide a review of the current literature that links environmental indicators with mental health, as well as key aspects that must be considered when using satellite data for mental health research. We also highlight and discuss significant links of the satellite data variables to the default mode network clustering coefficient and cognition. This comprehensive dataset provides the foundation for large-scale environmental epidemiology research.

Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study

BACKGROUND

Urbanization greatly affects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases. Schistosomiasis, a common parasitic disease transmitted by the snail Oncomelania hupensis, is mainly found in areas with population aggregations along rivers and lakes where snails live. Previous studies have suggested that factors related to urbanization may influence the infection risk of schistosomiasis, but this association remains unclear. This study aimed to analyse the effect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China.

METHODS

County-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected. The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The geographically and temporally weighted regression model (GTWR) was used to quantify the influence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled. The regression coefficient of urbanization was tested for significance (α = 0.05), and the influence of urbanization on schistosomiasis infection risk was analysed over time and across space based on significant regression coefficients. Variables studied included climate, soil, vegetation, hydrology and topography.

RESULTS

The mean regression coefficient for urbanization (0.167) is second only to the leached soil area (0.300), which shows that the urbanization is the most important influence factors for schistosomiasis infection risk besides leached soil area. The other important variables are distance to the nearest water source (0.165), mean minimum temperature (0.130), broadleaf forest area (0.105), amount of precipitation (0.073), surface temperature (0.066), soil bulk density (0.037) and grassland area (0.031). The influence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year. During the study period, the significant coefficient of urbanization level increased from - 0.205 to - 0.131.

CONCLUSIONS

The influence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous. The urbanization does reduce the risk of schistosomiasis infection to some extend, but the strength of this influence decreases with increasing urbanization. Additionally, the effect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors. This study provides scientific basis for understanding the influence of urbanization on schistosomiasis, and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

Association of exposure to artificial light at night during adolescence with blood pressure in early adulthood

Artificial light at night (ALAN) is related to various diseases, such as cancer, obesity, and coronary heart disease. However, its impact on blood pressure in adolescents is not well understood. To investigate this, we conducted a cross-sectional study with a nationwide sample of college students in China, who were freshmen from four disperse universities during Sep. and Oct. 2018. Mean levels of ALAN at participants' residential addresses during 2013-2018 were estimated using time-varying satellite data. The association of the 6-y average of ALAN with blood pressure was estimated by using generalized linear mixed models. A total of 17 046 participants (18.2 ± 0.7 y of age, 46.79% female) from 2,412 counties and cities were included in the final analysis. After a full adjustment for potential confounders, ALAN was positively associated with systolic blood pressure (β = 0.20, p = 0.032) and pulse pressure (β = 0.28, p  = 0.001), but there was no association between ALAN and diastolic blood pressure (β = -0.08, p = 0.213). In the sensitivity analysis, the results consistent with the main analysis were observed. The blood pressure of males and those with a BMI ≤24 kg/m2 were more susceptible to ALAN exposure. Our findings highlight the importance of ALAN management for blood pressure control, particularly among male and normal-weight individuals.

Spatiotemporal changes of aerosol optical depth and its response to urbanization: a case study of Jinan City, China, 2009-2018

With the insidiously growing impact of urban development on the environment, the issue of air quality has attracted extensive attention nationally and globally. It is of great significance to study the influence of urbanization on air quality for the rational development of cities. MODIS-MAIAC (Moderate Resolution Imaging Spectroradiometer-Multi-Angle Implementation of Atmospheric Correction) Aerosol optical depth (AOD) product, DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night-light were used to explore the spatiotemporal variation and correlation between AOD and urbanization development before and after the promulgation of environmental governance policies in Jinan City from 2009 to 2018. Results show that (1) the spatial distribution of AOD in Jinan had the characteristics of high in the north and low in the south, high in the west and low in the east, and low in some parts of the central region; there was a significant seasonal variation in time, with the highest AOD in summer and the lowest in winter. During 2009-2013, the annual average variation of AOD increased by 20.6%, while during 2014-2018, it decreased by 35.3%; (2) The distribution of night-light in Jinan City has progressively expanded, mirroring the city's ongoing development. The spatial distribution of aerosols in urban areas was relatively low compared to the surrounding areas of the city. (3) From 2009 to 2013, there existed a significant positive correlation between the spatial and temporal distribution of AOD and night-light. However, from 2014 to 2018, with the implementation of environmental governance policies, this relationship shifted to a significant negative correlation between the spatial and temporal distribution of AOD and night-light. Through an analysis of the correlation between urban development and aerosol depth in Jinan City over the past decade, it can be concluded that urban development does not inevitably result in elevated AOD levels. Notably, the Jinan government has achieved remarkable results in controlling the atmospheric environment, as evidenced by recent years' improvements.

Interaction patterns between economic growth and atmospheric environment in China under the "carbon neutrality" target

Clarifying the interaction patterns between economic growth and atmospheric environment (EG-AE) in China is important to achieve the "carbon neutrality" target. A conceptual framework of air pollutant emission in urban economic growth (APEUEG) was proposed to explore the interaction patterns in China from 2007 to 2017. The empirical analysis revealed that a N-shaped EKC exists between aerosol optical depth (AOD) and gross domestic product (GDP), with inflection points of $5000 and $27,000, respectively. Therefore, we speculated that when GDP per capita of a city exceeded $5000, the AOD gradually decreased. However, when GDP per capita of a city gained over $27,000, the economic growth and the atmospheric environment would be coordinated steadily. The interaction of EG-AE experienced three stages-pollution, improvement, and coordination-in China. Spatially, the interaction patterns of EG-AE presented five clusters, which were associated with the spatial distribution of city levels. China's prefecture-level cities have undergone the cluster of low AOD-low GDP (LL), the cluster of high AOD-high GDP (HH), and the cluster of low AOD-high GDP (LH), as urban level improves. By 2017, about 44% of Chinese cities had not completed the coordinated development yet. We found that policymakers should formulate differentiated urban greener economic development policies to reduce APEUEG.

Spatiotemporal evolution and influence mechanism of the carbon footprint of energy consumption at county level in the Yellow River Basin

Implementing emission reduction policies at county level is important to realize high-quality development in the Yellow River Basin and achieve national "carbon peaking" and "carbon neutrality" goals. Based on remote-sensing data of night light, net primary productivity, and land use, the present study utilized the light‑carbon conversion and carbon footprint measurement models to quantify the carbon footprint of energy consumption. An exploratory spatiotemporal data analysis method was implemented to analyze the spatiotemporal evolution path. Panel quantile regression and spatiotemporal transition-nested models were used to reveal the influence mechanism of the spatiotemporal evolution of the carbon footprint. The following results were obtained. (1) The carbon footprint of counties increased from 2001 to 2020. Counties with high‑carbon footprint diffused around the "one center and two axes". Carbon-deficit counties exhibited a diffused trend towards the west. In 2020, 506 counties exhibited carbon deficits, and the carbon balance of the ecosystem was severely unbalanced. (2) The carbon footprint showed evident path dependence and Matthew effect. The high‑carbon footprint lock-in area comprising 177 counties is a challenging zone for governance. The 86 counties that exhibit carbon footprint changes are the key zones to drive the carbon footprint changes in the Basin. The change direction of the county's carbon footprint type, with evident spatial correlation characteristics, is in accordance with adjacent counties. (3) The carbon footprint spatiotemporal transition types and influence mechanisms in counties exhibited significant differences, with the coexistence of low-carbon footprint driving, low-carbon footprint restriction, high-carbon footprint driving and high-carbon footprint restriction modes. As the influence mechanisms of different modes and the paths to achieve "dual carbon" goals are different, the governance of different modes should focus on optimizing and strengthening restriction factors or controlling and improving of driving factors.

Estimate of population density and diagnosis of main factors of spatial heterogeneity in the metropolitan scale, western China

We estimated the population density and quantified its characteristics using remote sensing, census data, and Geographic Information System (GIS). The interactive influence of these factors on population density was quantified based on geographic detectors to identify the differentiation mechanisms in the Chengdu metropolitan area of China. We identified the key factors that contribute to population density growth. The models used to simulate population density had the highest R² values (>0.899). Population density tended to increase with time, with a multicentre spatial agglomeration pattern; the centre of gravity of the spatial distribution tended to move from the southeast to the northwest. Industry proportions, Normalised Difference Vegetation Index (NDVI), land use, distance to urban centers or construction land, and GDP per capita can satisfactorily explain population density changes. The combined impact of these elements on population density variation exhibited mutual and non-linear strengthening, with the mutual effect of the two elements intensifying the impact of each individual element. Our study identified the key driving forces that contribute to the differentiation of population density, which can provide valuable support for the development of effective regional and targeted population planning guidelines.

Assessment of lake area in response to climate change at varying elevations: A case study of Mt. Tianshan, Central Asia

Changes in lake area (water surface area) are often considered accurate and sensitive representations of climate change. However, the role that elevation plays in this dynamic is somewhat unclear; studies remain inconclusive as to whether lake responses are consistent across elevation gradients. Here, we used Landsat and keyhole satellite images to quantify lake area changes from the 1960s to 2020 at different elevations in Central Asia's Tianshan Mountains and relate them to both climatic and anthropogenic factors. The results revealed that all low-elevation lakes showed a decreasing trend, and the total area of all monitored low-elevation lakes was reduced by 18.50 %. The total area of the mid-elevation lakes decreased by 0.16 %, while the total area of the high-elevation glacial lakes increased by 4.35 %. Lakes are recharged by a variety of influxes including glacial meltwater and precipitation. Notably, human activities (urban and agricultural water consumption) were the dominant factors in the shrinkage of low-elevation lakes. Climatic factors were the main driving factors of mid-elevation lake changes, and these lakes appeared to be more sensitive to temperature changes than lakes at other elevations. In addition, significant warming dominated area changes in high-elevation proglacial and unconnected glacial lakes. Overall, those results emphasized that when using lakes to reconstruct paleoclimates or predict lake evolution, it is necessary to consider how elevation gradients and recharge types may affect lake sensitivity to variations in climatic and anthropogenic activity.

Multi-scale analysis of China's transportation carbon emissions based on nighttime light data

This study explores the spatial and temporal evolution characteristics of transportation carbon emissions from multiple scales. Based on the integrated DMSP/OLS-NPP/VIIRS nighttime light data, a transportation carbon emission estimation model was constructed, and the spatial and temporal evolution characteristics of transportation carbon emissions in 30 provinces and some counties in China from 2000 to 2019 were analyzed. The main findings are as follows: (1) The goodness-of-fit of the estimation model improved from 51.2 to 87.15% by introducing the GDP variables. (2) At the provincial scale, the provinces with high carbon emissions from transportation were mainly distributed in the eastern region, with the highest value increasing from 19,171.6 million tons in 2000 to 71,545.98 million tons in 2019. The spatial distribution has a significant and positive spatial spillover effect, and the H-H aggregation was mainly distributed in the east-central region, showing a trend of expansion from the coast to the inland. Trend analysis showed that Shandong, Guangdong, Shanghai, and Jiangsu were areas with a rapid growth of high carbon emissions. (3) The county scale displayed a northeast-southwest evolutionary pattern, with the center of gravity in Henan. The spatial distribution showed a significant spatial agglomeration phenomenon. Trend analysis indicated that the transportation carbon emissions in 184 counties need to be controlled urgently, which was the focus of carbon emission reduction. This paper theoretically enriches the measurement method of transportation carbon emissions and overcomes the problem of insufficient spatial information of statistical data. In practice, it provides a scientific basis for accurate emission reduction and low-carbon development of transportation.

Light at night and cause-specific mortality risk in Mainland China: a nationwide observational study

BACKGROUND

While epidemiological studies have found correlations between light at night (LAN) and health effects, none has so far investigated the impacts of LAN on population mortality yet. We aimed to estimate the relative risk for mortality from exposure to LAN in Mainland China.

METHODS

This time-stratified case-crossover nationwide study used NPP-VIIRS to obtain daily LAN data of Mainland China between 2015 and 2019. The daily mortality data were obtained from the Disease Surveillance Point System in China. Conditional Poisson regression models were applied to examine the relative risk (RR) for mortality along daily LAN in each county, then meta-analysis was performed to combine the county-specific estimates at the national or regional level.

RESULTS

A total of 579 counties with an average daily LAN of 4.39 (range: 1.02-35.46) were included in the main analysis. The overall RRs per 100 nW/cm²/sr increases in daily LAN were 1.08 (95%CI: 1.05-1.11) for all-cause mortality and 1.08 (95%CI: 1.05-1.11) for natural-cause mortality. A positive association between LAN and all natural cause-specific mortality was observed, of which the strongest effect was observed on mortality caused by neuron system disease (RR = 1.32, 95%CI: 1.14-1.52). The results were robust in both younger and old, as well as in males and females. The more pronounced effect of LAN was observed in median LAN-level regions. Combined with an exposure-response curve, our study suggests a non-linear association between LAN and mortality in China.

CONCLUSIONS

Our study shows LAN is associated with mortality in China, particularly for neuron system disease-related mortality. These findings have important implications for public health policy establishment to minimize the health consequences of light pollution.

Dynamic simulation of land use and land cover and its effect on carbon storage in the Nanjing metropolitan circle under different development scenarios

Land use and land cover (LULC) change is a pattern of alteration of the Earth’s land surface cover by human society and have a significant impact on the terrestrial carbon cycle. Optimizing the distribution of LULC is critical for the redistribution of land resources, the management of carbon storage in terrestrial ecosystems, and global climate change. We integrated the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model to simulate and assess future LULC and ecosystem carbon storage in the Nanjing metropolitan circle in 2030 under four scenarios: natural development (ND), economic development (ED), ecological protection (EP), and collaborative development (CD). The results showed that (1) LULC and carbon storage distribution were spatially heterogenous in the Nanjing metropolitan circle for the different scenarios, with elevation, nighttime lights, and population being the main driving factors of LULC changes; (2) the Nanjing metropolitan circle will experience a carbon increase of 0.50 Tg by 2030 under the EP scenario and losses of 1.74, 3.56, and 0.48 Tg under the ND, ED, and CD scenarios, respectively; and (3) the CD scenario is the most suitable for the development of the Nanjing metropolitan circle because it balances ED and EP. Overall, this study reveals the effects of different development scenarios on LULC and ecosystem carbon storage, and can provide a reference for policymakers and stakeholders to determine the development patterns of metropolitan areas under a dual carbon target orientation.

The impacts of land cover spatial combination on nighttime light intensity in 2010 and 2020: a case study of Fuzhou, China

As human activities highly depend on the land resources and changed the land cover (LC) condition, the relationship between LC and nighttime light (NTL) intensity has been widely analyzed to support the foundation of NTL applications and help explain the drivers of urban economic development. However, previous studies always paid attention to the effect of each LC type on NTL intensity, with limited consideration of the joint effects of any two LC types. To fill this gap, this study measured the land cover spatial combination (LCSC) by using a spatial adjacency matrix, and then analyzed its impacts on NTL intensity based on an extreme gradient boosting (XGBoost) regression model with the assistant of sharpley additive explanations (SHAP) method. Our results presented that the LCSC can better (R2 of 82.4% and 98.1% in 2010 and 2020) explain the relationship between LC and NTL intensity with the traditional LC metrics (e.g., area and patch count), since the LCSC is much more sensitive to the diverse land functions. It is noteworthy that the impacts, as well as their dynamics, of LCSC between any two LC types on NTL intensity are various. LCSC associated with artificial surface contributed more to NTL intensity. In detail, the LCSC of water/wetland and artificial surface can increasingly promote the NTL intensity while the LCSC of grassland/forest and artificial surface has a decreasing or inverse U-shaped contribution to NTL intensity. Whereas LCSC associated with non-artificial surface were not conducive to the increase in NTL intensity due to high vegetation density. We also provided three implications to help further urbanization process and discussed the applications of LCSC.

Slope climbing of urban expansion worldwide: Spatiotemporal characteristics, driving factors and implications for food security

The tendency of global urban expansion to be slope climbing has partly become possible with scarce cropland resources in plains. However, the scientific understanding of the quantity, intensity, pattern, and effect of the slope climbing of urban expansion (SCE) is minimal globally. In this study, we have attempted to quantify and evaluate global SCE from Suomi National Polar-orbiting Partnership (SNPP)-Visible Infrared Imaging Radiometer Suite (VIIRS)-like data and other auxiliary data. Results revealed that global SCE areas unevenly increased from 22,760 km² to 90,720 km² from 2000 to 2020, with an annual growth rate of 21.72%, in which low-environment cost type areas increased from 21,550 km² to 84,010 km² while high-environment cost type (HEC) areas increased from 1210 km² to 6710 km². One remarkable phenomenon is that China's SCE areas in 2020 were more than 11 times those in 2000. In addition, global SCE intensity increased by about 3.4-fold from 2000 to 2020 and the rapid growth of HEC intensity is concentrated in Asia and North America. SCE is mostly affected by urban population growth and terrain. Economic development also promotes its development to a certain extent. We also noted that global SCE potentially made a considerable contribution to saved cropland, saving about 46,747 km² with a theoretical increased grain yield of 25,020 × 10³ t. Our study provides timely and transparent monitoring of global SCE and offers new insights into sustainable urban development.

Using a combination of nighttime light and MODIS data to estimate spatiotemporal patterns of CO2 emissions at multiple scales

Accurate mapping spatiotemporal patterns of CO₂ emissions and understanding its driving factors are very important, it is useful for the scientific and rational formulation of carbon emission reduction policies. Nevertheless, due to data availability issues, most studies have been limited to the global and national scales, and the models used were relatively simple. In this paper, we used the 500 m Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) data and the 250 m Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS NDVI) and proposed an improved CO₂ emissions index (ICEI) to calculate CO₂ emissions. Compared with the total nighttime light (NTL), the average regression coefficient (R²) can be improve from 0.73 to 0.78. We also used the coefficient of variation, spatial autocorrelation, and geographically weighted regression models to analyze the temporal and spatial variation mode of CO₂ emissions, as well as the associated correlation and heterogeneity, at three different administrative unit scales during 2012-2019. Our experimental results demonstrate that: (1) the improved index (ICEI) is better than the traditional variable (NTL) in estimating CO₂ emissions; (2) the highest CO₂ emissions are primarily gathered in the developed coastal areas in eastern China; and (3) at the provincial level, the added value of the secondary industry is the most significant factor, whereas the added value of the tertiary industry is negatively correlated with CO₂ emissions.

So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale

Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, statistical and machine learning methods have been developed to estimate population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises a digital elevation model, local climate zone, land use proportions, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated approaches in the field of population estimation.

Association of outdoor artificial light at night with mental health among China adults: a prospective ecology study

Multiple environmental changes are related to mental disorders. However, research on the association between artificial light at night (ALAN) and mental health in China is limited, particularly at the national level. We used a "difference-in-differences" design and logistic regression to explore the relationship between ALAN changes and scores on self-assessed mental health. Participants were drawn from the China Family Panel Studies of adults in 2012 and 2018. The final analysis was based on 21,036 adults from 25 provinces throughout China. The brighter the ALAN, the worse was the mental health, and this connection was unaffected by particulate matter 2.5 (PM_2.5) or temperature. ALAN sensitivity may differ among populations. Our findings suggest that exposure to brighter ALAN is associated significantly with worse mental health among Chinese adults. Environmental policies that reduce ALAN could improve the mental health of the Chinese public.

What Constitutes the High-Quality Soundscape in Human Habitats? Utilizing a Random Forest Model to Explore Soundscape and Its Geospatial Factors Behind

Soundscape is the production of sounds and the acoustic environment, and it emphasizes peoples' perceiving and experiencing process in the context. To this end, this paper focuses on the Pearl River Delta in China, and implements an empirical study based on the soundscape evaluation data from the Participatory Soundscape Sensing (PSS) system, and the geospatial data from multiple sources. The optimal variable set with 24 features are successfully used to establish a random forest model to predict the soundscape comfort of a new site (F1 = 0.61). Results show that the acoustic factors are most important to successfully classify soundscape comfort (averaged relative importance of 17.45), subsequently ranking by built environment elements (11.28), temporal factors (9.59), and demographic factors (9.14), while landscape index (8.60) and land cover type (7.71) seem to have unclear importance. Furthermore, the partial dependence analysis provides the answers about the appropriate threshold or category of various variables to quantitatively or qualitatively specify the necessary management and control metrics for maintaining soundscape quality. These findings suggest that mainstreaming the soundscape in the coupled natural-human systems and clarifying the mechanisms between soundscape perception and geospatial factors can be beneficial to create a high-quality soundscape in human habitats.

Can Economic Growth and Environmental Protection Achieve a "Win-Win" Situation? Empirical Evidence from China

Achieving a "win-win" situation regarding economic growth and environmental protection has become a common goal for sustainable development in all countries around the world. As the world's largest developing country and the second largest economy, China has been striving to maintain economic growth while improving environmental quality to achieve its sustainable development goals. Applying the decoupling approach, a model widely used to quantify the relationship between the environment and the economy, this study analyzed the relationship between the economy and the environment, examining the decoupling performance of economic growth and environmental impacts in 30 Chinese provinces, autonomous regions, and municipalities to investigate whether economic growth and environmental protection have achieved a "win-win" situation. Nighttime light (NTL) data were used to measure the performance of economic growth. In addition, an environmental pressure index (EPI) assessment framework covering 6 primary and 11 secondary indicators was constructed to measure the environmental quality of China over time. First, NTL data proved to be a valid data source for assessing decoupling performance; second, environmental pressure at both the national and provincial levels significantly decreased during the study period; third, the relationship between the economy and the environment has been further improved, and economic growth and environmental protection have achieved a "win-win" situation. These findings offer an in-depth analysis of the decoupling of the economy and the environment in China and serve as a guide for future implementation strategies for sustainable development in various regions.

Interaction and mediation effects of economic growth and innovation performance on carbon emissions: Insights from 282 Chinese cities

China is under rapid urbanization and consequently facing increasing carbon emissions (CE). Economic growth (EG) and innovation performance (IP), as two critical indicators of urbanization, are considered the driving forces of CE. Although economy and innovation are entangled and can jointly affect CE in reality, the measured effects of economy and innovation on CE are often treated separately in traditional studies. We adopted a three-part research framework including the total, interaction and mediation effect tests to elucidate how EG and IP affected CE in China from 2005 to 2015 based on insights from 282 Chinese cities. The empirical results showed that both economy and innovation contributed to CE, although the contribution has reduced over the 11 years. In particular, the interaction effect between economy and innovation for North China, Northeast China, and Southwest China was -4.201, -8.442, and - 3.897, respectively, in 2015, meaning that these regions adversely affect CE. In addition, we found that the economy helps reduce CE via innovation. When considering the changes of economy and innovation, their mediation effect on CE changes varied in different regions, attributable to the level of economy and innovation as well as the stocks of energy resources. Therefore, future planning for low-carbon transition should regard the economy and innovation together. Based on this principle, we propose five detailed policies. Overall, this study is valuable not only for further understanding the triangle relationship among economy, innovation, and CE, but also for reaching low-carbon goals.

Spatial and Temporal Changes of Urban Built-Up Area in the Yellow River Basin from Nighttime Light Data

Nighttime light (NTL) images obtained by the Visible Infrared Imaging Radiometer (VIIRS) mounted on the National Polar-orbiting Partnership (NPP) could objectively represent human activities and instantly identify urban shapes on a temporal and spatial scale. From 2013 to 2020, the built-up areas of eight provincial capital cities were extracted using NPP/VIIRS NTL data to examine the dynamic changes in city expansion and socioeconomic development in the Yellow River Basin during the urbanization process. The spatial characteristics of urban built-up area expansion were generated using the eight-quadrant analysis method and combined with the statistical data of population and (gross domestic product) GDP to analyze the correlations between the light intensity of built-up areas, population and GDP; this enables an understanding of the changes in population and economy in the development of urban built-up area expansion. The findings show that: (1) unbalanced city development existed in the Yellow River Basin’s upper, middle, and lower reaches, and the expansion and light intensity of cities in the upper reaches were slower than those in the middle and lower reaches; (2) the spatial differentiation of urban expansion was significant between each of the reaches in the Yellow River Basin, and greatly influenced by natural geographical elements; and (3) positive correlation exists between light intensity, population, and GDP in the built-up areas of the middle and lower reaches, while the correlations in the upper reaches were not stable. In conclusion, light data indirectly reflects urban development and could be used as a substitute variable for socioeconomic development indicators under certain conditions.

Using Multi-Source Geospatial Information to Reduce the Saturation Problem of DMSP/OLS Nighttime Light Data

The DMSP/OLS Nighttime light (NTL) data directly reflect the spatial distribution and light intensity of artificial lighting from the Earth’s surface at night, and has become an emerging instrument for urbanization research, including in the monitoring of urban expansion, assessment of socio-economic vitality, and estimation of energy consumption and population. However, due to the imperfect sensor design of DMSP/OLS, the dynamic range of the digital number (DN) of NTL is limited (0, 63), leading to a significant saturation problem when describing the actual light intensity, especially in dense urban areas with high light intensity. This saturation problem masks spatial differences in light intensity and weakens the reliability of DMSP/OLS NTL data. Therefore, this study proposes a novel desaturation indicator that combines NDBI and POI, the Building and POI Density-Adjusted Nighttime Light Index (BPANTLI), to regulate the DMSP/OLS NTL saturation problem based on the spatial characteristics of urban structures and human activity intensity. The proposed method is applied to three urban agglomerations with the most severe light saturation issues in China. The geographical detector model is firstly utilized to quantify the effectiveness of NDBI and POI in reflecting the difference in light intensity distribution from the NTL potential saturation region (NTL DN value (53, 63)) and NTL unsaturation region (NTL DN value (0, 52)), so as to clarify the feasibility of developing the BPANTLI. The applicability of BPANTLI is validated through three aspects—comparison of the desaturation capacity and the performance of delineating light intensity; verification of the consistency of BPANTLI with radiometric calibration nighttime light product (RCNTL) and NPP/VIIRS data; and assessing the accuracy of the BPANTLI in estimating socio-economic parameters (GDP, electricity consumption, population density). The results indicate that the BPANTLI possesses superior capability in regulating the NTL saturation problem, achieving good performance in distinguishing inner-urban structures. The regulated results reveal a remarkably improved correspondence with the RCNTL and NPP/VIIRS data, providing a more realistic picture of the light intensity distribution. It is worth noting that, given the advantages of NDBI and POI vector data in spatial resolution, the BPANTLI established in this study can overcome the limitation of the spatial resolution of DMSP/OLS nighttime lighting data and achieve dynamic transformation of the spatial resolution. The higher spatial resolution desaturation results allow for a better characterization of the light intensity distribution. Moreover, the BPANTLI-regulated light intensity significantly improves the accuracy of estimating electricity consumption, GDP, and population density, which provides a valuable reference for urban socio-economic activity assessment. Thus, the BPANTLI proposed in this study can be considered as a reasonable desaturation method with a high application value.

Regional Social Development Gap and Regional Coordinated Development Based on Mixed-Methods Research: Evidence From China

Due to the continuous acceleration of the global urbanization process, the unbalanced development of regional cities has become an unavoidable reality under the rapid economic and social development of China. Unbalanced social development will affect coordinated and sustainable economic development, regional ethnic unity, and political and social stability. This research uses data from the 2011–2015 period, 2016–2020 period, and various special development plans of 35 large and medium cities, combines qualitative analysis and quantitative analysis, establishes a comprehensive evaluation model, and conducts cluster analysis, using standard deviation. The coefficient of variation aims to measure and study whether the gap in China’s regional social development has continued to widen over the past decade. This study found that: (1) From the overall national perspective, there are obvious differences in the level of social development in the development plans of 35 large and medium-sized cities. The social development level of each large and medium-sized city has been improved to a certain extent, and the social development gap between cities has also been reduced to a certain extent. (2) From the 2011–2015 period to the 2016–2020 period, the social development gap between the three regions of my country’s eastern, central, and western regions has declined. (3) The trend of social development disparity within the three major regions of the eastern, central, and western regions is not the same. The internal social development gap in the eastern region shows a downward trend, while the internal social development gap in the central and western regions shows an upward trend. This study provides a valuable reference for rapidly urbanizing developing countries in the process of globalization.

Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic

Monitoring the fine spatiotemporal distribution of urban GDP is a critical research topic for assessing the impact of the COVID-19 outbreak on economic and social growth. Based on nighttime light (NTL) images and urban land use data, this study constructs a GDP machine learning and linear estimation model. Based on the linear model with better effect, the monthly GDP of 34 cities in China is estimated and the GDP spatialization is realized, and finally the GDP spatiotemporal correction is processed. This study analyzes the fine spatiotemporal distribution of GDP, reveals the spatiotemporal change trend of GDP in China's major cities during the current COVID-19 pandemic, and explores the differences in the economic impact of the COVID-19 pandemic on China's major cities. The result shows: (1) There is a significant linear association between the total value of NTL and the GDP of subindustries, with R2 models generated by the total value of NTL and the GDP of secondary and tertiary industries being 0.83 and 0.93. (2) The impact of the COVID-19 pandemic on the GDP of cities with varied degrees of development and industrial structures obviously varies across time and space. The GDP of economically developed cities such as Beijing and Shanghai are more affected by COVID-19, while the GDP of less developed cities such as Xining and Lanzhou are less affected by COVID-19. The GDP of China's major cities fell significantly in February. As the COVID-19 outbreak was gradually brought under control in March, different cities achieved different levels of GDP recovery. This study establishes a fine spatial and temporal distribution estimation model of urban GDP by industry; it accurately monitors and assesses the spatial and temporal distribution characteristics of urban GDP during the COVID-19 pandemic, reveals the impact mechanism of the COVID-19 pandemic on the economic development of major Chinese cities. Moreover, economically developed cities should pay more attention to the spread of the COVID-19 pandemic. It should do well in pandemic prevention and control in airports and stations with large traffic flow. At the same time, after the COVID-19 pandemic is brought under control, they should speed up the resumption of work and production to achieve economic recovery. This study provides scientific references for COVID-19 pandemic prevention and control measures, as well as for the formulation of urban economic development policies.

Carbon Emissions Estimation and Spatiotemporal Analysis of China at City Level Based on Multi-Dimensional Data and Machine Learning

Carbon emissions caused by the massive consumption of energy have brought enormous pressure on the Chinese government. Accurately and rapidly characterizing the spatiotemporal characteristics of Chinese city-level carbon emissions is crucial for policy decision making. Based on multi-dimensional data, including nighttime light (NTL) data, land use (LU) data, land surface temperature (LST) data, and added-value secondary industry (AVSI) data, a deep neural network ensemble (DNNE) model was built to analyze the nonlinear relationship between multi-dimensional data and province-level carbon emission statistics (CES) data. The city-level carbon emissions data were estimated, and the spatiotemporal characteristics were analyzed. As compared to the energy statistics released by partial cities, the results showed that the DNNE model based on multi-dimensional data could well estimate city-level carbon emissions data. In addition, according to a linear trend analysis and standard deviational ellipse (SDE) analysis of China from 2001 to 2019, we concluded that the spatiotemporal changes in carbon emissions at the city level were in accordance with the development of China’s economy. Furthermore, the results can provide a useful reference for the scientific formulation, implementation, and evaluation of carbon emissions reduction policies.

Economic Sanctions and Regional Differences: Evidence from Sanctions on Russia

The objective of this study was to analyze the relationship between economic sanctions and regional differences within Russia from three perspectives: regional favoritism of the political elite, industry development, and trade costs. Using the nighttime lights in Russia, we found a correlation between economic sanctions and regional differences. First, as sanctions increased, the lights of Moscow, St. Petersburg, and provincial capitals were brighter than those of the rest of the country. Second, the lights of manufacturing cities were brighter as sanctions increased. However, under the influence of sanctions, the lights of mining areas of Russia were dimmer than those of other areas. Finally, there were relatively more economic activities in areas close to the Chinese border. The lights of Blagoveshchensk were brighter than that of the rest of the country. In addition, the relationship between economic sanctions and the brightness of lights had the characteristics of stages. There was a negative correlation with the brightness of Russian lights in the early stages of economic sanctions. However, this negative correlation disappeared in the later stages.

Impact of Urban Form on CO2 Emissions under Different Socioeconomic Factors: Evidence from 132 Small and Medium-Sized Cities in China

The accurate estimation of the impact of urban form on CO2 emissions is essential for the proposal of effective low-carbon spatial planning strategies. However, few studies have focused on the relationship between urban form and CO2 emissions in small and medium-sized cities, and it is especially unclear whether the relationship varies across cities with different socioeconomic characteristics. This study took 132 small and medium-sized cities in the Yangtze River Delta in China to explore how urban form affects CO2 emissions, considering the socioeconomic factors of industrial structure, population density, and economic development level. First, nighttime light data (DMSP-OLS and NPP-VIIRS) and provincial energy data were used to calculate CO2 emissions. Second, four landscape metrics were used to quantify the compactness and complexity of the urban form based on Chinese urban land-use data. Finally, panel data models were established to analyze whether and how different socioeconomic factors impacted the relationship between urban form and CO2 emissions. The results showed that the three socioeconomic factors mentioned above all had obvious influences on the relationship between urban form and per capita CO2 emissions in small and medium-sized cities. The effect of compactness on per-capita CO2 emissions increased with a rise in the proportion of the tertiary industry, population density, and per-capita GDP. However, compactness shows no effects on per-capita CO2 emissions in industrial cities and low-development-level cities. The effect of complexity on per-capita CO2 emissions only increased with the rise in population density. The results may support decision-makers in small and medium-sized cities to propose accurate, comprehensive, and differentiated plans for CO2 emission control and reduction.

Measuring the urban land use efficiency of three urban agglomerations in China under carbon emissions

On the basis of DMSP/OLS and NPP-VIIRS night light images, this study realized carbon emission estimations based on the municipal level from 1999 to 2017, compensating for the characteristics of incomplete statistical data and different statistical calibers. On this basis, the epsilon-based measure (EBM) super-efficiency model and the global Malmquist-Luenberger (GML) index are used to measure the urban land use efficiency (ULUE) and urban land total factor productivity (ULTFP) of the three urban agglomerations under the carbon emission constraints from 1999 to 2017. The following conclusions are drawn through research. (1) The correlation coefficient between the total value of night light pixels and energy consumption carbon emissions was relatively high in the three major urban agglomerations during 1999-2017, and they all passed the significance test of 1%. (2) The ULUE of the three major urban agglomerations generally shows a downward trend and then an upward trend, and spatial heterogeneity is obvious. The spatial distribution of the average level of ULUE is Pearl River Delta Urban Agglomeration (PRDUA) > Yangtze River Delta Urban agglomeration (YRDUA) > Beijing-Tianjin-Hebei Urban agglomeration (BTHUA). (3) The ULTFP of the three major urban agglomerations are all showing an increasing trend, but the geometric mean of URTFP in the PRDUA, BTHUA, and YRDUA decreases successively. Technological progress is the main driving force to promote the progress of ULTFP in each urban agglomeration. (4) The kernel density estimation shows a significant gap in ULUE between the three major urban agglomerations in China, and a phenomenon of polarization or multipolarization is observed. The main reason is the hysteresis of technology diffusion.

Monitoring Light Pollution with an Unmanned Aerial Vehicle: A Case Study Comparing RGB Images and Night Ground Brightness

There are several tools and methods to quantify light pollution due to direct or reflected light emitted towards the sky. Unmanned aerial vehicles (UAV) are still rarely used in light pollution studies. In this study, a digital camera and a sky quality meter mounted on a UAV have been used to study the relationship between indices computed on night images and night ground brightness (NGB) measured by an optical device pointed downward towards the ground. Both measurements were taken simultaneously during flights at an altitude of 70 and 100 m, and with varying exposure time. NGB correlated significantly both with the brightness index (−0.49 ÷ −0.56) and with red (−0.52 ÷ −0.58) and green band indices (−0.42 ÷ −0.58). A linear regression model based on the luminous intensity index was able to estimate observed NGB with an RMSE varying between 0.21 and 0.46 mpsas. Multispectral analysis applied to images taken at 70 m showed that increasing exposure time might cause a saturation of the colors of the image, especially in the red band, that worsens the correlation between image indices and NGB. Our study suggests that the combined use of low cost devices such as UAV and a sky quality meter can be used for assessing hotspot areas of light pollution originating from the surface.

Developing Relative Spatial Poverty Index Using Integrated Remote Sensing and Geospatial Big Data Approach: A Case Study of East Java, Indonesia

Poverty data are usually collected through on-the-ground household-based socioeconomic surveys. Unfortunately, data collection with such conventional methods is expensive, laborious, and time-consuming. Additional information that can describe poverty with better granularity in scope and at lower cost, taking less time to update, is needed to address the limitations of the currently existing official poverty data. Numerous studies have suggested that the poverty proxy indicators are related to economic spatial concentration, infrastructure distribution, land cover, air pollution, and accessibility. However, the existing studies that integrate these potentials by utilizing multi-source remote sensing and geospatial big data are still limited, especially for identifying granular poverty in East Java, Indonesia. Through analysis, we found that the variables that represent the poverty of East Java in 2020 are night-time light intensity (NTL), built-up index (BUI), sulfur dioxide (SO2), point-of-interest (POI) density, and POI distance. In this study, we built a relative spatial poverty index (RSPI) to indicate the spatial poverty distribution at 1.5 km × 1.5 km grids by overlaying those variables, using a multi-scenario weighted sum model. It was found that the use of multi-source remote sensing and big data overlays has good potential to identify poverty using the geographic approach. The obtained RSPI is strongly correlated (Pearson correlation coefficient = 0.71 (p-value = 5.97×10−7) and Spearman rank correlation coefficient = 0.77 (p-value = 1.58×10−8) to the official poverty data, with the best root mean square error (RMSE) of 3.18%. The evaluation of RSPI shows that areas with high RSPI scores are geographically deprived and tend to be sparsely populated with more inadequate accessibility, and vice versa. The advantage of RSPI is that it is better at identifying poverty from a geographical perspective; hence, it can be used to overcome spatial poverty traps.

Spatial Features of Urban Expansion in Vietnam Based on Long-Term Nighttime Lights Data

As a developing country, Vietnam has experienced rapid economic development since the 21st century. It is therefore becoming increasingly important to understand its spatial pattern of urban expansion. However, the key challenge in this endeavor lies in the lack of national accounting data for the sub-administrative divisions of Vietnam at the national level, especially longitudinal data over a long time series. The nighttime lights (NTL) data can objectively reflect the scope and intensity of human development and construction in urban built-up areas, which can effectively support the empirical analysis of the urban expansion process in Vietnam. This paper uses the intercalibration model to correct and fit the long time series of DMSP/OLS and VIIRS/NPP NTL data. Based on this, the data for the urban built areas of Vietnam from 2000 to 2018 are further extracted. The results are as follows. (1) The main urban expansion in Vietnam is concentrated in the southern Mekong Delta and the northern Red River Delta, represented by Ho Chi Minh City and Hanoi City, respectively. (2) Vietnam’s urban NTL has significant high–high clustering characteristics in the north-south delta regions. The main urban expansion hotspots were concentrated around Ho Chi Minh City before 2012, the northern region represented by Hanoi City was gradually transformed into a critical area that gathering urban expansion hotspots after 2012. (3) The cities with significant influence and high coupling degree of industrialization and globalization on the urbanization of Vietnam are concentrated in Ho Chi Minh City, Hanoi, and some northern delta provinces, showing that the impact of industrialization and globalization on urbanization in Vietnam is still limited to some regions. In addition, the results show that the size of the population and the level of industrialization are the main drivers of urban expansion in Vietnam, while the level of foreign investment shows little significance. The results are helpful for promoting the application of long time series NTL data in urban expansion and for further analyzing the urban pattern changes in Vietnam and the influencing factors behind them.

Global Identification of Unelectrified Built-Up Areas by Remote Sensing

Access to electricity (the proportion of the population with access to electricity) is a key indica for of the United Nations’ Sustainable Development Goal 7 (SDG7), which aims to provide affordable, reliable, sustainable, and modern energy services for all. Accurate and timely global data on access to electricity in all countries is important for the achievement of SDG7. Current survey-based access to electricity datasets suffers from short time spans, slow updates, high acquisition costs, and a lack of location data. Accordingly, a new method for identifying the electrification status of built-up areas based on the remote sensing of nighttime light is proposed in this study. More specifically, the method overlays global built-up area data with night-time light remote sensing data to determine whether built-up areas are electrified based on a threshold night-time light value. By using our approach, electrified and unelectrified built-up areas were extracted at 500 m resolution on a global scale for the years 2014 and 2020. The acquired results show a significant reduction in an unelectrified built-up area between 2014 and 2020, from 51,301.14 km2 to 22,192.52 km2, or from 3.05% to 1.32% of the total built-up area. Compared to 2014, 117 countries or territories had improved access to electricity, and 18 increased their proportion of unelectrified built-up area by >0.1%. The identification accuracy was evaluated by using a random sample of 10,106 points. The accuracies in 2014 and 2020 were 97.29% and 98.9%, respectively, with an average of 98.1%. The outcomes of this method are in high agreement with the spatial distribution of access to electricity data reported by the World Bank. This study is the first to investigate the global electrification of built-up areas by using remote sensing. It makes an important supplement to global data on access to electricity, which can aid in the achievement of SDG7.

Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images

The accurate estimation of electricity consumption and its spatial distribution are important in electricity infrastructural planning and the achievement of the United Nations Sustainable Development Goal 7 (SDG7). Electricity consumption can be estimated based on its correlation with nighttime lights observed using remote sensing imagery. Since night-light images are easily affected by cloud cover, few previous studies have estimated electricity consumption in cloudy areas. Taking Cambodia as an example, the present study proposes a method for denoising night-light images in cloudy areas and estimating electricity consumption. The results show that an exponential model is superior to linear and power function models for modelling the relationship between total night-light data and electricity consumption in Cambodia. The month-specific substitution method is best for annual night-light image synthesis in cloudy areas. Cambodia’s greatest electricity consumption occurs in its four most economically developed cities. Electricity consumption spreads outwards from these cities along the main transport routes to a large number of unelectrified areas.

Impact of Power on Uneven Development: Evaluating Built-Up Area Changes in Chengdu Based on NPP-VIIRS Images (2015–2019)

In the context of uneven development studies of China, urban built-up area changes are the index of the impact of power, as the local government is the only party that is able to acquire agricultural land and convert it to construction urban land. Existing studies generally use statistical data to describe the built-up area changes and struggle to meet the requirement of an updated and inexpensive monitoring of uneven development, especially for western cities with tight budgets. Open access NPP-VIIRS (Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite), NDVI (Normalized Difference Vegetation Index), and nighttime LST (Land Surface Temperature) data ranging from 2015 to 2019 were analyzed with a stratified SVM (Support Vector Machine) method in this study to track urban built-up area changes in Chengdu, one of the biggest cities in Western China. The SDE (Standard Deviation Ellipse) and Moran’s I were then applied to evaluate the spatial variations of the built-up area changes. It was revealed that the spatial evolution of built-up area change in Chengdu over the period 2015–2019 demonstrated a “northwest-southeast” spatial expansion pattern, and the change distance in the center of gravity in 2018 and 2019 was greater than that from 2015 to 2017, which reflected the faster uneven development in 2018 and 2019 in Chengdu. The results were verified with finer resolution Landsat-8 OLI images; the high OA (all larger than 92%) and KAPPA (all larger than 0.6) values showed the accuracy of the method. The methodology proposed in this study offers a practical way for cities with tight budgets to monitor uneven development, and this study suggests a further adaption using higher-resolution remote sensing images and field experiments.

Using Multi-Source Nighttime Lights Data to Proxy for County-Level Economic Activity in China from 2012 to 2019

The use of nighttime lights (NTL) data to proxy for local economic activity is well established in remote sensing and other disciplines. Validation studies comparing NTL data with traditional economic indicators, such as Gross Domestic Product (GDP), underpin this usage in applied studies. Yet the most widely cited validation studies do not use the latest NTL data products, may not distinguish between time-series and cross-sectional uses of NTL data, and usually are for aggregated units, such as nation-states or the first sub-national level, yet applied studies increasingly focus on smaller and lower-level spatial units. To provide more updated and disaggregated validation results, this study examines relationships between GDP and NTL data for 2657 county-level units in China, observed each year from 2012 to 2019. The NTL data used were from three sources: the Defense Meteorological Satellite Program (DMSP), whose time series was recently extended to 2019; and two sets of Visible Infrared Imaging Radiometer Suite (VIIRS) data products. The first set of VIIRS products is the recently released version 2 (V.2 VNL) annual composites, and the second is the NASA Black Marble annual composites. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of economic activity changes over time, and also considered different levels of spatial aggregation.

Nightlights and Subnational Economic Activity: Estimating Departmental GDP in Paraguay

Subnational measures of economic activity are crucial for analyzing inequalities that persist across subnational regions and for tracking progress towards sustainable development within a country. Eighteen of the Sustainable Development Goals (SDG) indicators require having estimates of Gross Domestic Product (GDP), making subnational GDP estimates crucial for local SDG monitoring. However, many countries do not produce official subnational GDP estimates. Using Paraguay as an example, we show how nightlights imagery from the Visible Infrared Imaging Radiometer Suite’s Day-Night Band (VIIRS-DNB) and data from neighboring countries can be used to produce subnational GDP estimates. We first estimate the relationship between VIIRS and economic activity in South American countries at the first subnational administrative level, employing various econometric models. Results suggest that nightlights are strongly predictive of subnational GDP variation in South American countries with available data. We assess various models’ goodness-of-fit using both cross-validation against other countries’ subnational GDP data and comparing predictions against an input–output accounting of Paraguay’s subnational GDP. Finally, we use the preferred model to produce a time series of department-level GDP in Paraguay.

Association of exposure to artificial light at night with atopic diseases: A cross-sectional study in college students

The impact of artificial light at night (ALAN) exposure on health has become increasingly prominent. However, little is known about the effect of ALAN exposure on atopic diseases. In this study, a cross-sectional analysis of incoming students was conducted in 5 geographically disperse universities which locate in Changsha (south), Wuhan (central), Xiamen (east), Urumchi (west), and Hohhot (north), respectively. All incoming students who consented to participate were recruited, followed by a health examination and a questionnaire survey. Prevalent atopic diseases were diagnosed by clinicians. Mean ALAN (nanoWatts/cm²/sr) during their adolescence was obtained from the remote sensing observed nighttime light data matching with their residence information, which was obtained from survey. Mixed generalized linear models (log-binomial) were used to estimate the associations, in terms of prevalence ratio (PR) with 95% confidence interval (CI). A total of 20106 participants were included in the analysis. Based on previous work, we chose factors including socioeconomic status, behavioural factors, major air pollutants, and air climatic parameters for adjustment. After full adjustment, the PR for atopic diseases was 1.35 (95% CI: 1.27-1.42; P < 0.001). The effect size of ALAN was the largest for asthma (PR = 1.80; 95% CI: 1.48-2.19; P < 0.001), followed by atopic rhinitis (PR = 1.42; 95% CI: 1.33-1.51; P < 0.001), and atopic dermatitis (PR = 1.20; 95% CI: 1.06-1.35; P = 0.003). Subgroup analyses by covariates showed consistent results. This study revealed that exposure to ALAN during adolescence may contribute to a higher risk of atopic diseases in young adulthood.

The Effect of the Human Footprint and Climate Change on Landscape Ecological Risks: A Case Study of the Loess Plateau, China

The increase in ecological risks caused by human activities has become a global concern in recent years. The Landscape Ecological Risk Index based on the theory of landscape ecology is more suitable for assessing large-scale ecological risks. Assessing landscape ecological risks and the mechanisms by which humans directly or indirectly affect them will help to manage and control the regions’ ecological risks through scientific and policy methods. In this study, a new model of landscape ecological risk assessment based on the moving window method is proposed. The Loess Plateau of China is used as an example, and the Human Footprint Index dataset of the Loess Plateau is constructed. Different human footprint factors and climate factors are applied, and the human direct and indirect effects on the landscape ecological risks of the Loess Plateau are explored based on the geographical detector model. The results show that, in 2000, 2010 and 2020, the landscape ecological risks of the Loess Plateau are currently in an unstable state, and the highest value area of the Landscape Ecological Risk Index continues to expand, with values of 113,566.1553 km2, 114,575.6772 km2 and 120,718.5363 km2, respectively. Among all the human footprint factors, the population density factor has the highest effect on the landscape ecological risks of the Loess Plateau. Among the climate factors, both the average temperature factor and the average lagged temperature factor have significant effects on the landscape ecological risks of the Loess Plateau. With the interaction of any two human footprint factors and climate factors, the effect of these factors on the landscape ecological risks of the Loess Plateau is enhanced.

Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future

The Sustainable Development Goals (SDG) framework aims to end poverty, improve health and education, reduce inequality, design sustainable cities, support economic growth, tackle climate change and leave no one behind. To monitor and report the progress on the 231 unique SDGs indicators in all signatory countries, data play a key role. Here, we reviewed the data challenges and costs associated with obtaining traditional data and satellite data (particularly for developing countries), emphasizing the benefits of using satellite data, alongside their portal and platforms in data access. We then assessed, under the maturity matrix framework (MMF 2.0), the current potential of satellite data applications on the SDG indicators that were classified into the sustainability pillars. Despite the SDG framework having more focus on socio-economic aspects of sustainability, there has been a rapidly growing literature in the last few years giving practical examples in using earth observation (EO) to monitor both environmental and socio-economic SDG indicators; there is a potential to populate 108 indicators by using EO data. EO also has a wider potential to support the SDGs beyond the existing indicators.

Using Daily Nighttime Lights to Monitor Spatiotemporal Patterns of Human Lifestyle under COVID-19: The Case of Saudi Arabia

A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society; impacts spread further and reached the entire Islamic society and other societies, too.

Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints

Economic globalization is developing more rapidly than ever before. At the same time, economic growth is accompanied by energy consumption and carbon emissions, so it is particularly important to estimate, analyze and evaluate the economy accurately. We compared different nighttime light (NTL) index models with various constraint conditions and analyzed their relationships with economic parameters by linear correlation. In this study, three indices were selected, including original NTL, improved impervious surface index (IISI) and vegetation highlights nighttime-light index (VHNI). In the meantime, all indices were built in a linear regression relationship with gross domestic product (GDP), employed population and power consumption in southeast China. In addition, the correlation coefficient R2 was used to represent fitting degree. Overall, comparing the regression relationships with GDP of the three indices, VHNI performed best with the value of R2 at 0.8632. For the employed population and power consumption regression with these three indices, the maximum R2 of VHNI are 0.8647 and 0.7824 respectively, which are also the best performances in the three indices. For each individual province, the VHNI perform better than NTL and IISI in GDP regression, too. When taking employment population as the regression object, VHNI performs best in Zhejiang and Anhui provinces, but not all provinces. Finally, for power consumption regression, the value of VHNI R2 is better than NTL and IISI in every province except Hainan. The results show that, among the indices under different constraint conditions, the linear relationships between VHNI and GDP and power consumption are the strongest under vegetation constraint in southeast China. Therefore, VHNI index can be used for fitting analysis and prediction of economy and power consumption in the future.

An Analysis of the Work Resumption in China under the COVID-19 Epidemic Based on Night Time Lights Data

Public emergencies often have an impact on the production and operation of enterprises. Timely and effective quantitative measurement of enterprises’ offline resumption of work after public emergencies is conducive to the formulation and implementation of relevant policies. In this study, we analyze the level of work resumption after the coronavirus disease 2019 (COVID-19)-influenced Chinese Spring Festival in 2020 with night time lights remote sensing data and Baidu Migration data. The results are verified by official statistics and facts, which demonstrates that COVID-19 has seriously affected the resumption of work after the Spring Festival holiday. Since 10 February, work has been resuming in localities. By the end of March, the work resumption index of most cities exceeded 70% and even Shanghai, Nanjing and Suzhou had achieved complete resumption of work. Wuhan only started to resume work in the last week of March due to the more severe outbreak. Although the level of work resumption is gradually increasing in every area, the specific situation of resumption of work varies in different regions. The process of work resumption in coastal areas is faster, while the process is relatively slow in inland cities.

A New Insight into Understanding Urban Vitality: A Case Study in the Chengdu-Chongqing Area Twin-City Economic Circle, China

Addressing the issues caused by urbanization through urban vitality theory has elicited increasing attention in social environment research. However, few studies focus on vitality itself, such as the generative mechanism of urban vitality (GMUV) and the identification of key factors to vitality improvement. Therefore, a new insight into vitality is presented in this study through the exploration of GMUV based on partial least squares structural equation modeling (PLS-SEM). Concretely, the GMUV and the key factors to vitality improvement are analyzed and identified based on nighttime lights data, points of interest, and the statistical data of the Chengdu-Chongqing Area Twin-City Economic Circle in China. The results show that external representations and internal elements constitute the structural basis of the GMUV and that environmental vitality and social vitality are the key factors to enhance vitality. Finally, suggestions on improving regional vitality are provided to urban policymakers. This study may promote a better understanding of vitality, and the proposed vitality evaluation model may serve as a reference for other regions.

Identifying and Classifying Shrinking Cities Using Long-Term Continuous Night-Time Light Time Series

Shrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the lack of using long-term continuous NTL time series and the use of unidimensional indices. In this study, we proposed a novel method to identify and classify shrinking cities by long-term continuous NTL time series and population data, and applied the method in northeastern China (NEC) from 1996 to 2020. First, we established a long-term consistent NTL time series by applying a geographically weighted regression model to two distinct NTL datasets. Then, we generated NTL index (NI) and population index (PI) by random forest model and the slope of population data, respectively. Finally, we developed a shrinking city index (SCI), based on NI and PI to identify and classify city shrinkage. The results showed that the shrinkage pattern of NEC in 1996–2009 (stage 1) and 2010–2020 (stage 2) was quite different. From stage 1 to stage 2, the shrinkage situation worsened as the number of shrinking cities increased from 102 to 162, and the proportion of severe shrinkage increased from 9.2% to 30.3%. In stage 2, 85.4% of the cities exhibited population decline, and 15.7% of the cities displayed an NTL decrease, suggesting that the changes in NTL and population were not synchronized. Our proposed method provides a robust and long-term characterization of city shrinkage and is beneficial to provide valuable information for sustainable urban planning and decision-making.

Extraction of urban built-up area based on the fusion of night-time light data and point of interest data

The accurate extraction of urban built-up areas is an important prerequisite for urban planning and construction. As a kind of data that can represent urban spatial form, night-time light data has been widely used in the extraction of urban built-up areas. As one of the geographic open-source big data, point of interest (POI) data has a high spatial coupling with night-time light data, so researchers are beginning to explore the fusion of the two data in order to achieve more accurate extraction of urban built-up areas. However, the current research methods and theoretical applications of the fusion of POI data and night-time light data are still insufficient compared with the dramatically changing urban built-up areas, which needed to be further supplemented and deepened. This study proposes a new method to fuse POI data and night-time light data. The results before and after data fusion are compared, and the accuracy of urban built-up area extracted by different data and methods is analysed. The results show that the data fusion can avoid the shortage of single data and effectively improve the extraction accuracy of urban built-up areas, which is greatly helpful to supplement the study of data fusion in urban built-up areas, and also can provide decision-making guidance for urban planning and construction.

Spatial Spillover Effect and Influencing Factors of Information Flow in Urban Agglomerations—Case Study of China Based on Baidu Search Index

Cities in an urban agglomeration closely interact with each other through various flows. Information flow, as one of the important forms of urban interactions, is now increasingly indispensable with the fast development of informatics technology. Thanks to its timely, convenient, and spatially unconstrained transmission ability, information flow has obvious spillover effects, which may strengthen urban interaction and further promote urban coordinated development. Therefore, it is crucial to quantify the spatial spillover effect and influencing factors of information flows, especially at the urban agglomeration scale. However, the academic research on this topic is insufficient. We, therefore, developed a spatial interaction model of information flow (SIM-IF) based on the Baidu Search Index and used it to analyze the spillover effects and influencing factors of information flow in the three major urban agglomerations in China, namely Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) in the period of 2014–2019. The results showed that the SIM-IF performed well in all three agglomerations. Quantitative analysis indicated that the BTH had the strongest spillover effect of information flow, followed by the YRD and the PRD. It was also found that the hierarchy of cities had the greatest impact on the spillover effects of information flow. This study may provide scientific basis for the information flow construction in urban agglomerations and benefit the coordinated development of cities.

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional gross domestic product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defense Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. However, applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001–19 time-series of GDP for over 3100 U.S. counties as a benchmark to examine the performance of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries were used to examine the types of economic activity best proxied by NTL data. Comparisons were also made with the predictive performance of earlier NTL data products and at different levels of spatial aggregation.

Optical Design of Imaging Spectrometer Based on Linear Variable Filter for Nighttime Light Remote Sensing

Nighttime light remote sensing has unique advantages on reflecting human activities, and thus has been used in many fields including estimating population and GDP, analyzing light pollution and monitoring disasters and conflict. However, the existing nighttime light remote sensors have many limitations because they are subject to one or more shortcomings such as coarse spatial resolution, restricted swath width and lack of multi-spectral data. Therefore, we propose an optical system of imaging spectrometer based on linear variable filter. The imaging principle, optical specifications, optical design, imaging performance analysis and tolerance analysis are illustrated. The optical system with a focal length of 100 mm, F-number 4 and 43° field of view in the spectrum range of 400–1000 nm is presented, and excellent image quality is achieved. The system can obtain the multi-spectral images of eight bands with a spatial resolution of 21.5 m and a swath width of 320 km at the altitude of 500 km. Compared with the existing nighttime light remote sensors, our system possesses the advantages of high spatial and high spectral resolution, wide spectrum band and wide swath width simultaneously, greatly making up for the shortage of the present systems. The result of tolerance analysis shows our system satisfy the requirements of fabrication and alignment.