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[Objective] As a systemic transformation of the economic and social system, the low-carbon energy transition will inevitably impact social issues such as equity and income. Investigating the impact of city low-carbon energy transition on income inequality can provide valuable empirical insights for coordinating the low-carbon transformation of the economy and society and for narrowing income gaps. [Methods] This study used the panel data from 281 prefecture-level cities in China from 2007 to 2020 as the research sample, and established a multidimensional indicator system to calculate city low-carbon energy transition index. Based on the nighttime light data of 2109 districts and counties, the income inequality between different urban income groups was measured. Subsequently, the intrinsic relationship between low-carbon energy transition and income inequality was examined by both theoretical modeling and empirical analysis. [Results] (1) The low-carbon energy transition significantly alleviated income inequality, with each standard deviation increase in the transition index reducing income inequality by approximately 6.1%. (2) Mechanism analysis indicated that the low-carbon energy transition affected income inequality through skill-biased technological progress and the upgrading of the labor force’s skill structure, with the alleviating effect on income inequality mainly stemming from its role in enhancing the skill structure of the labor force. (3) Heterogeneity analysis revealed regional and urban characteristic heterogeneity in the impact of low-carbon energy transition on income inequality. (4) The research using the spatial Durbin model showed that the impact of low-carbon energy transition on income inequality had spatial spillover effects, which can reduce income inequality in neighboring areas. [Conclusion] Renewable energy should be vigorously developed, and regular exchanges and cooperation in energy transition between regions should be strengthened to fully leverage the transition’s role in upgrading the labor force’s skill structure, thereby achieving coordinated development that promotes low-carbon energy transition and narrows income gaps.

Mangrove forests are one of the most biologically diverse and productive ecosystems on Earth. They are important breeding and nursing grounds for amphibians, invertebrates, birds, fish, etc. Light pollution may cause serious degradation of biodiversity in the ecosystem. A report of the long-term holistic views of light pollution changes is essential for sustainable management of mangrove ecosystems. However, to date, such studies have rarely been carried out. This study aimed to monitor the long-term light pollution changes of China’s mangrove forests. To achieve this goal, we used time-series nighttime light (NTL) data to build continuous light pollution data. NTL maps made with DMSP-OLS (DNL) or NPP-VIIRS (VNL) are widely utilized in research on human activity. However, DMSP and VIIRS images are different in spatial resolution, radiation resolution, and data saturation. Thus, this study innovatively set an optimal threshold for generating consistent light pollution data in mangrove areas from 1992-2020. The results showed that: (1) the proportion of light-polluted mangrove forests in China increased from 12% in 1992 to 52% in 2020; (2) the largest net increase occurred in Guangxi with an area of 4,086 ha, followed by Guangdong (3,365 ha) and Hainan (2,944 ha); (3) Zhejiang had the largest proportion of net growth (from 0% in 1992 to 99% in 2020), followed by Hainan (66%) and Fujian (59%). Mangrove forests have been protected and restored for decades in China; this study indicates that the establishment of nature reserves is effective in preventing the light pollution of mangroves and provides the first long-term multi-temporal dataset of light pollution in China’s mangrove forests. This comprehensive information could support related studies and facilitate the development of applicable coastal management strategies in China.

Monitoring human activities in border areas is challenging due to the complex geographical environment and diverse people. China has the longest terrestrial boundary and the highest number of neighboring countries in the world. In this study, a human activity intensity index (HAI) was proposed based on land cover, population density, and satellite-based nighttime light for a long-term macroscopic study. The HAI was calculated at 1 km resolution within the 50 km buffer zone of China's land boundary on each side in 1992, 2000, 2010, and 2020, respectively. Results show that human activity is low in about 90% of the study area. Overall, the HAI on the Chinese side is higher than that on the neighboring side, and the intensity of land use on the Chinese side has increased significantly from 1992 to 2020. Among China's neighbors, India has the highest HAI with the fastest growth. With the changes in the HAI between China and its neighboring countries, four regional evolution patterns are found in the study area: Sino-Russian HAI decline; Sino-Kazakhstan HAI unilateral growth; Indian HAI continuous growth; China and Indochina HAI synchronized growth. Hotspot analysis reveals three spatial evolution patterns, which are unilateral expansion, bilateral expansion, and cross-border fusion. Both the "border effect" and "agglomeration effect" exist in border areas. The HAI changes in border areas not only impact the eco-environment but also affect geopolitics and geoeconomics. The HAI can be used as an instrument for decision-making and cooperation between China and neighboring countries in such areas as ecological protection, border security, and border trade. [ABSTRACT FROM AUTHOR]

• Spatiotemporal patterns and impact factors of LUCE were studied at the county level. • The center of gravity of China's county LUCE was constantly moving to the northwest. • LUCE showed positive spatial autocorrelation and significant spatial agglomeration. • Stable path dependence and spatial locking were observed in county-level LUCE. • The main factors influencing LUCE in China's counties were GDP and carbon intensity. Counties play a foundational role in China's economic progress and the governance of society. Studying land use carbon emissions (LUCE) at the county level is crucial for meeting China's dual carbon targets, which facilitate the decomposition of national emission reduction targets, thereby enabling more refined management and control and enhancing the precision and effectiveness of local-level emission reduction measures. However, prior research into LUCE has mainly concentrated on national and provincial levels. In this study, we calculated the LUCE of China's counties using data for nighttime light, land use, and energy consumption. Based on this foundation, we studied the spatiotemporal patterns and impact factors of LUCE in Chinese counties from 2000 to 2020. The research findings were as follows: (1) LUCE continued to increase over the study period, and its center of gravity (CGY) continued to move to the northwest. (2) There was a positive spatial autocorrelation of LUCE with an obvious spatial heterogeneity and agglomeration. (3) LUCE's local spatial pattern remained stable, with strong spatial integration and dependence, and its spatial correlation was relatively stable, with obvious path-locking characteristics. (4) LUCE was primarily influenced by GDP and carbon intensity. The study's findings have implications for China's carbon reduction strategy. [ABSTRACT FROM AUTHOR]

China has launched a series of ambitious poverty alleviation strategies to end extreme poverty, officially announcing the achievement of this goal in 2020. Currently, these counties persist in their efforts to achieve the goal of rural revitalization. Many studies often showcase the spatial patterns of China's remarkable success in counties out of poverty, but often disregard the relative poverty-returning risk (PRR), specifically within China's 832 extreme poverty counties. Nighttime light datasets (NTL) have been extensively employed as a surrogate measure for socioeconomic performance in underserved regions. In this work, we constructed an NTL-based relative PRR index to detect the spatial patterns of PRR among 832 counties. Then, we identified the main influencing factors concerning the PRR mitigation of 14 poverty-stricken clusters based on three dimensions: geophysical, economic, and social. Our results showed that counties in central China are far from returning to extreme poverty with low PRR. However 14 counties (2.41% of the 832) show high PRR, and those counties are mainly clustered in southwest China. This implies that those regions require more poverty-related policy support, financial inflows, and assistance. Regional differences were found in the factors that influence the mitigation of PRR. This is crucial for developing targeted strategies to consolidate poverty eradication. Our findings are essential for China's poverty-related SDGs, ensuring that no one is left behind. [Display omitted] • We investigated the 832 county-level relative PRRs. • China's relative PRRs are spatially imbalanced at the county level. • High PRR counties (2.41%) clusters gathering in southwestern China were observed. • There are geographic discrepancies in the factors that alleviate PRR. [ABSTRACT FROM AUTHOR]

The housing vacancy rate (HVR) is an important index in assessing the healthiness of residential real estate market. In China, it is hardly to take advantage of the basic data of real estate information due to the opaque of those data. In this paper, the HVR is estimated to two scales. At the grid level, urban area ratio was calculated by nighttime images after eliminating outliers of nighttime images and night light intensity of non-residential pixels in mixed pixels by a proposed modified optimal threshold method, and built-up areas in each pixel were extracted from the land-cover data. Then, the HVR is calculated by comparing the light intensity of specific grid with the light intensity of full occupancy rate regions. At the administrative scale, the GCI ('ghost city' index) is constructed by calculating the ratio of the total light radiation intensity of a city to the total construction land area of the city. The overall spatial differentiation pattern of the vacant houses in the national prefecture level administrative regions is analyzed. The following conclusions were drawn: vacant housing is rare in certain eastern coastal cities and regions in China with relatively fast economic development. Cities based on exhausted resources, some mountainous cities, and cities with relatively backward economic development more typically showed high levels of housing vacancy. The GCI of prefecture-level administrative units gradually declined from north to south, whereas the east-west distribution showed a parabolic shape. As city level decreased, the GCI registered a gradual upward trend. China's urban housing vacancy can be divided into five categories: industry or resources driven, government planned, epitaxy expansionary, environmental constraint and speculative activate by combining the spatial distribution of housing vacancy with the factors of natural environment, social economic development level, and population density into consideration. [ABSTRACT FROM AUTHOR]

Increasing light is widely recognised to be harmful to the wilderness of protected areas (PAs). However, minimal studies revealed the spatiotemporal distribution and variation of nighttime lights (NTLs) at the regional and single protected area levels on a continuous time. We assessed the extent and intensity of NTLs within and around the PAs during 1992–2012 in mainland China. Lighted area index (LAI), lighted intensity index (LII), potential LAI (PLAI) and potential LII (PLII) were developed as indicators to represent the total quantity and the intensity in a PA and in a 5-km buffer zone around the PA on the basis of the Defense Meteorological Program Operational Line-Scan System NTL data. NTL in PAs covered 7192 km2 in 2012, accounting for 0.73% of the total area of PAs. The highest values of LAI and LII were observed in Northeast and South Central China among the six geographical divisions. The LAI and LII increased by 3.22 and 3.51 times from 1992 to 2012, which showed that the area and intensity of NTL in PAs experienced simultaneous growth. We identified a significant increase of NTL within 36% of PAs and outside 60% of PAs, indicating the persistent increase in the intensity of human disturbance in and around these PAs. The NTL growth rates in marine and coastal and wild plant PAs were the greatest and lowest among different types of PAs, with values of 76.47% and 15.79%, respectively. We suggested that development of targeted countermeasures for each PA should comprehensively consider the conservation objectives, the type of PA, and the lighting conditions inside and outside the PA. The findings can provide useful information for targeting management strategies to alleviate light pollution and human disturbance.

Producing gridded electric power consumption (EPC) maps at a fine geographic scale is critical for rational deployment and effective utilization of electric power resources. Brightness of nighttime light (NTL) has been extensively adopted to evaluate the spatial patterns of EPC at multiple geographical scales. However, the blooming effect and saturation issue of NTL imagery limit its ability to accurately map EPC. Moreover, limited sectoral separation in applying NTL leads to the inaccurate spatial distribution of EPC, particularly in the case of industrial EPC, which is often a dominant portion of the total EPC in China. This study pioneers the separate estimation of spatial patterns of industrial and nonindustrial EPC over mainland China by jointly using points of interest (POIs) and multiple remotely sensed data in a random forests (RF) model. The POIs provided fine and detailed information about the different socioeconomic activities and played a significant role in determining industrial and nonindustrial EPC distribution. Based on the RF model, we produced industrial, non-industrial, and overall EPC maps at a 1 km resolution in mainland China for 2011. Compared against statistical data at the county level, our results showed a high accuracy (R2 = 0.958 for nonindustrial EPC estimation, 0.848 for industrial EPC estimation, and 0.913 for total EPC). This study indicated that the proposed RF-based method, integrating POIs and multiple remote sensing data, can markedly improve the accuracy for estimating EPC. This study also revealed the great potential of POIs in mapping the distribution of socioeconomic parameters.

Abstract Remote sensing image products (e.g. brightness of nighttime lights and land cover/land use types) have been widely used to disaggregate census data to produce gridded population maps for large geographic areas. The advent of the geospatial big data revolution has created additional opportunities to map population distributions at fine resolutions with high accuracy. A considerable proportion of the geospatial data contains semantic information that indicates different categories of human activities occurring at exact geographic locations. Such information is often lacking in remote sensing data. In addition, the remarkable progress in machine learning provides toolkits for demographers to model complex nonlinear correlations between population and heterogeneous geographic covariates. In this study, a typical type of geospatial big data, points-of-interest (POIs), was combined with multi-source remote sensing data in a random forests model to disaggregate the 2010 county-level census population data to 100 × 100 m grids. Compared with the WorldPop population dataset, our population map showed higher accuracy. The root mean square error for population estimates in Beijing, Shanghai, Guangzhou, and Chongqing for this method and WorldPop were 27,829 and 34,193, respectively. The large under-allocation of the population in urban areas and over-allocation in rural areas in the WorldPop dataset was greatly reduced in this new population map. Apart from revealing the effectiveness of POIs in improving population mapping, this study promises the potential of geospatial big data for mapping other socioeconomic parameters in the future. Graphical abstract Unlabelled Image Highlights • A population map for China at 100-m spatial resolution was produced by random forests. • Remote sensing and POI data were jointly used to disaggregate census population. • The new population map showed higher accuracy than the Worldop dataset. • The use of POI reduced under-allocation in urban and over-allocation in rural areas. • POIs have more strengths than brightness of nighttime lights for population estimation. [ABSTRACT FROM AUTHOR]

The impacts of floods on human society are expected to increase at both global and regional scale in the context of global climate change and increasing human activity in flood-prone areas. However, spatially explicit and dynamic assessments of flood exposure in China are still very limited, which hinders the development of risk-based flood management considering dynamic changes. Therefore, this study explored the spatial-temporal variations of flood exposure in China from 1992 to 2020 through flood susceptibility mapping and using satellite-based nighttime light data. It can be revealed that a significantly high level and rapid increase of flood exposure could be observed in China, especially around some major metropolitan areas in the eastern region. The expansion of flood exposure was mainly distributed in North China and some coastal areas during 1992–2000; while mainly occurring in South China during 2000–2013, especially in middle Yangtze River basin where flood susceptibility is highest. The changes of flood exposure in China were mainly driven by the increase in the number of lit pixels during the early period, and were dominated by the increase in the intensity of lit pixels in the later period. This process continued to the latest period of 2014–2020. This implies that upgrading of existing flood management facilities and efforts for more effective measures are urgently needed in flood-prone urban areas with increasing exposure intensity. In addition, this study demonstrated the advantage of nighttime light data for disaster assessment and monitoring. With the development of more nighttime light products of higher resolution and higher detection ability and the integration with ground-based observation, it is promising to achieve more precise monitoring and analysis of flood exposure at a large scale, and support fine-grained management of flood risk.

Since China’s reform and development commenced, in the context of rapid urbanization and coordinated regional development, Chinese cities with a close geographic proximity and social ties have gradually formed an integrated city development model. As a new phenomenon in China’s urbanization process, existing research on China’s integrated cities mainly focuses on typical case studies, and most research has been limited to literature reviews and theoretical analyses. The growing application of remote sensing technology in urbanization research in recent years has provided new opportunities for the analysis of city integration. Therefore, based on multi-spectral Landsat-8 and nighttime light images (SNPP/VIIRS, Suomi National Polar-orbiting Platform/Visible Infrared Imaging Radiometer Suite), this paper selects four of the most representative integrated cities with different backgrounds in China to analyze the land-use conversion, plot light fluctuation, and light gravity center shift in the boundary zone between cities. The results show that (1) Guangfo has the highest level of integration and urban expansion is mainly concentrated in the south-central part of the boundary area; (2) Guanshen’s level of integration is second to Guangfo’s and is mainly concentrated in the west; (3) HuSu’s integration is still in the initial stage and its increase in light intensity lags behind the expansion of building land during the study period; (4) although the light intensity and building land area increased significantly during the study period in Xixian, the overall development level of Xixian still lagged behind coastal cities due to the restriction of its geographical location. Our application results expand the data sources for integrated city research and the obtained results can potentially support decision-making and planning in the process of urban development.

Satellite-derived nighttime light (NTL) data have been extensively used as an efficient proxy measure for monitoring urbanization dynamics and socioeconomic activity. This is because remotely sensed NTL signals can be quantitatively connected to demographic and socioeconomic variables at regional and global scales. The recently composited cloud-free NTL imagery derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite provides spatially detailed observations of human settlements. We quantitatively estimated socioeconomic development inequalities across 30 provinces and municipalities in mainland China using VIIRS NTL data associated with both regional gross domestic product (GDP) and population census data. We quantitatively investigated relations between NTL, GDP, and population using a linear regression model. Our results suggest that NTL radiances have significant positive correlations with GDP and population at different levels. Several inequality coefficients, commonly used in economics, were derived from VIIRS NTL data and statistical data at multiple spatial scales. Compared with the statistical data, NTL-derived inequality coefficients enabled us to elicit more detailed information on differences in regional development at multiple levels. Our study of provinces and municipalities revealed that county-level inequality was more significant than city-level inequality. The results of population-weighted NTL inequality indicate an obvious regional disparity with NTL distribution being more unequal in China’s undeveloped western regions compared with more developed eastern regions. Our findings suggest that given the timely and spatially explicit advantages of VIIRS, NTL data are capable of providing comprehensive information regarding inequality at multiple levels, which is not possible through the use of traditional statistical sources.

China is the largest developing country worldwide, with rapid economic growth and the highest population. Light pollution is an environmental factor that significantly influences the quality and health of wildlife, as well as the people of any country. The objective of this study is to model the light pollution spatial pattern, and monitor changes in trends of spatial distribution from 1992 to 2012 in China using nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System. Based on the intercalibration of nighttime light imageries of the study area from 1992 to 2012, this study obtained the change trends map. This result shows an increase in light pollution of the study area; light pollution in the spatial scale increased from 2.08% in the period from 1992–1996 to 2000–2004, to 5.64% in the period from 2000–2004 to 2008–2012. However, light pollution change trends presented varying styles in different regions and times. In the 1990s, the increasing trend in light pollution regions mostly occurred in larger urban cities, which are mainly located in eastern and coastal areas, whereas the decreasing trend areas were chiefly industrial and mining cities rich in mineral resources, in addition to the central parts of large cities. Similarly, the increasing trend regions dominated urban cities of the study area, and the expanded direction changed from larger cities to small and middle-sized cities and towns in the 2000s. The percentages of regions where light pollution transformed to severe and slight were 5.64% and 0.39%, respectively. The results can inform and help identify how local economic and environmental decisions influence our global nighttime environment, and assist government agencies in creating environmental protection measures.

Nighttime Light (NTL) has been widely used as a proxy of many socio-environmental issues. However, the limited range of sensor radiance of NTL prevents its further application and estimation accuracy. To improve the performance, we developed an improved Vegetation Adjusted Nighttime light Urban Index (VANUI) through fusing multi-year NTL with population density, the Normalized Difference Vegetation Index and water body data and applied it to fine-scaled carbon emission analysis in China. The results proved that our proposed index could reflect more spatial variation of human activities. It is also prominent in reducing the carbon modeling error at the inter-city level and distinguishing the emission heterogeneity at the intra-city level. Between 1995 and 2013, CO2 emissions increased significantly in China, but were distributed unevenly in space with high density emissions mainly located in metropolitan areas and provincial capitals. In addition to Beijing-Tianjin-Hebei, the Yangzi River Delta and the Pearl River Delta, the Shandong Peninsula has become a new emission hotspot that needs special attention in carbon mitigation. The improved VANUI and its application to the carbon emission issue not only broadened our understanding of the spatiotemporal dynamics of fine-scaled CO2 emission, but also provided implications for low-carbon and sustainable development plans. [ABSTRACT FROM AUTHOR]

Rapid urbanization and economic development inevitably lead to light pollution, which has become a universal environmental issue. In order to reveal the spatiotemporal patterns and evolvement rules of light pollution in China, images from 1992 to 2012 were selected from the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and systematically corrected to ensure consistency. Furthermore, we employed a linear regression trend method and nighttime light index method to demonstrate China's light pollution characteristics across national, regional, and provincial scales, respectively. We found that: (1) China's light pollution expanded significantly in provincial capital cities over the past 21 years and hot-spots of light pollution were located in the eastern coastal region. The Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei regions have formed light pollution stretch areas; (2) China's light pollution was mainly focused in areas of north China (NC) and east China (EC), which, together, accounted for over 50% of the light pollution for the whole country. The fastest growth of light pollution was observed in northwest China (NWC), followed by southwest China (SWC). The growth rates of east China (EC), central China (CC), and northeast China (NEC) were stable, while those of north China (NC) and south China (SC) declined; (3) Light pollution at the provincial scale was mainly located in the Shandong, Guangdong, and Hebei provinces, whereas the fastest growth of light pollution was in Tibet and Hainan. However, light pollution levels in the developed provinces (Hong Kong, Macao, Shanghai, and Tianjin) were higher than those of the undeveloped provinces. Similarly, the light pollution heterogeneities of Taiwan, Beijing, and Shanghai were higher than those of undeveloped western provinces. [ABSTRACT FROM AUTHOR]

Light after dusk disrupts the circadian rhythms and shifts the timing of sleep later; but it is unknown whether outdoor artificial light at night (ALAN) affects sleep quality. This study aimed to explore the association between residential outdoor ALAN and sleep duration in a nationally representative sample of Chinese older adults. We examined the cross-sectional associations of outdoor ALAN with self-reported sleep duration in 13,474 older adults participating in the 2017–2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Outdoor ALAN exposure was estimated at the residence level using satellite images. We applied generalized linear mixed models to investigate the association between ALAN exposure and sleep duration. We performed stratified analyses by age, sex, education, and household income levels. Moreover, we used multi-level logistic regression models to investigate the effects of ALAN on the short sleep duration (≤6 h) and the long sleep duration (>8 h), respectively, in reference to sleep for >6–8 h per day. We found a significant association between outdoor ALAN intensity and sleep duration. The highest quartile of ALAN was associated with 17.04 (95% CI: 9.42–24.78) fewer minutes of sleep as compared to the lowest quartile. The reductions in sleep duration per quartile change in ALAN were greater in the young old (≥65–85 years) and in those with higher levels of education, and those with higher household income, respectively. We did not detect a sex difference. In addition, those in the highest quartile of ALAN were more likely to report a 25% (95% CI: 10%–42%) increase in short sleep (<6 h), and a 21% (95% CI: 9%–31%) decrease in long sleep (>8 h). Increasing outdoor nighttime light intensity surrounding residences was associated with shorter sleep duration in older residents in China. This finding implies the importance of urban outdoor artificial light management as a potential means to lower the public health burden of sleep disorders. [ABSTRACT FROM AUTHOR]

China's extensive urbanization has resulted in a massive loss of natural habitat, which is threatening the nation's biodiversity and socioeconomic sustainability. A timely and accurate understanding of natural habitat loss caused by urban expansion will allow more informed and effective measures to be taken for the conservation of biodiversity. However, the impact of urban expansion on natural habitats is not well-understood, primarily due to the lack of accurate spatial information regarding urban expansion across China. In this study, we proposed an approach that can be used to accurately summarize the dynamics of urban expansion in China over two recent decades (1992-2012), by integrating data on nighttime light levels, a vegetation index, and land surface temperature. The natural habitat loss during the time period was evaluated at the national, ecoregional, and local scales. The results revealed that China had experienced extremely rapid urban growth from 1992 to 2012 with an average annual growth rate of 8.74%, in contrast with the global average of 3.20%. The massive urban expansion has resulted in significant natural habitat loss in some areas in China. Special attention needs to be paid to the Pearl River Delta, where 25.79% or 1518 km2 of the natural habitat and 41.99% or 760 km2 of the local wetlands were lost during 1992-2012. This raises serious concerns about species viability and biodiversity. Effective policies and regulations must be implemented and enforced to sustain regional and national development in the context of rapid urbanization. [ABSTRACT FROM AUTHOR]

Protected areas (PAs) are designed to conserve biodiversity and protect the species within their boundaries. However, some PAs are not achieving these goals, partly due to human activities inside and outside PAs. Here we evaluated the ability of 290 national-level PAs in China to reduce the impacts of economic development pressures using remote sensing-based fine resolution impervious surface area (ISA) and nighttime light (NTL) data as proxies of development pressure. We measured and compared the development pressure in protected areas and within buffers the same size in the area of the PAs (namely outside of the PAs). We also investigated whether the performance of PAs will be threatened by outside development pressure. According to the ISA, we found that 176 of the PAs had more development pressure outside their boundaries than inside PAs in 2018. For 175 PAs, we found a higher increasing rate of impervious areas outside of the PAs from 2000 to 2018, which suggested that increased human activities outside of the PAs were placing increased development pressure on the PAs. The PAs with higher development pressure outside their boundaries were more common in populous regions, especially in eastern China. Consistent with our findings from the ISA data, the NTL data also indicated that national PAs in China have faced increasing development pressures from outside PAs from 2000 to 2018. Our study warns that more attention should be paid to economic development pressures from the areas surrounding PAs to sustain their biodiversity protection over the long run. • More development pressure in outside PAs than inside national PAs in China in 2018. • Higher growth of development pressure in outside PAs than inside during 2000–2018. • Outside of the PAs were placing increased development pressure on the PAs. • PAs with higher development pressure were more common in populous regions. • More attention should be paid to development pressure from areas surrounding PAs. [ABSTRACT FROM AUTHOR]

Abstract: Urbanization process involving increased population size, spatially extended land cover and intensified economic activity plays a substantial role in anthropogenic environment changes. Remotely sensed nighttime lights datasets derived from the Defense Meteorological Satellite Program''s Operational Linescan System (DMSP/OLS) provide a consistent measure for characterizing trends in urban sprawl over time (Sutton, 2003). The utility of DMSP/OLS imagery for monitoring dynamics in human settlement and economic activity at regional to global scales has been widely verified in previous studies through statistical correlations between nighttime light brightness and demographic and economic variables (). The quantitative relationship between long-term nighttime light signals and urbanization variables, required for extensive application of DMSP/OLS data for estimating and projecting the trajectory of urban development, however, are not well addressed for individual cities at a local scale. We here present analysis results concerning quantitative responses of stable nighttime lights derived from time series of DMSP/OLS imagery to changes in urbanization variables during 1994–2009 for more than 200 prefectural-level cities and municipalities in China. To identify the best-fitting model for nighttime lights-based measurement of urbanization processes with different development patterns, we comparatively use three regression models: linear, power-law and exponential functions to quantify the long-term relationships between nighttime weighted light area and four urbanization variables: population, gross domestic product (GDP), built-up area and electric power consumption. Our results suggest that nighttime light brightness could be an explanatory indicator for estimating urbanization dynamics at the city level. Various quantitative relationships between urban nighttime lights and urbanization variables may indicate diverse responses of DMSP/OLS nighttime light signals to anthropogenic dynamics in urbanization process in terms of demographic and economic variables. At the city level, growth in weighted lit area may take either a linear, concave (exponential) or convex (power law) form responsive to expanding human population and economic activities during urbanization. Therefore, in practice, quantitative models for using DMSP/OLS data to estimate urbanization dynamics should vary with different patterns of urban development, particularly for cities experiencing rapid urban growth at a local scale. [Copyright &y& Elsevier]

The shrinking of cities has become an increasingly global phenomenon, posing challenges for sustainable urban development. However, most focus remains on Europe and North America, and relatively little attention has been paid to the East Asia, especially the urbanizing China. Nighttime light (NL) dataset and its features (long-term time-series free access and large coverage) provide an alternative means to quantify shrinking cities. Here, we developed a new approach to identify shrinking cities and measure urban shrinkage, using corrected-integrated DMSP/OLS and NPP/VIIRS NL data. Based on this approach, we quantified the spatiotemporal patterns of shrinking cities in China from 1992 to 2019. Our study identified 153 shrinking cities in China during the study period, accounting for 23.39% of all 654 cities. These shrinking cities were widely distributed across eight economic regions and most provinces. The number of shrinking cities changed periodically and peaked following the Asian Financial Crisis in 1997 and again after the Global Economic Crisis in 2008. The cities that experienced the greatest shrinkage intensity were mainly distributed in northeast China, with severe urban shrinkage occurring between 2008 and 2013. The new approach proposed in this study can effectively identify shrinking city hotspots and key periods of urban shrinkage. Our findings suggest that sustainable urban development in China must consider shrinking cities, which are faced with challenging and urgent sustainability issues different from those by rapidly growing cities. • We proposed a method to quantify shrinking cities using two nighttime-light data. • About 23% of Chinese cities were identified as shrinking cities from 1992 to 2019. • Shrinking cities were widespread in all economic regions and most provinces. • Shrinking cities increased periodically and peaked after financial/economic crises. • The most severe urban shrinkage occurred in northeast China between 2008 and 2013. [ABSTRACT FROM AUTHOR]

An accurate and efficient extraction of urban extent is important for understanding the dynamics of urban expansion process and for sustainable planning and management of cities. We proposed an improved dynamic nightlight threshold method to model urban extent and to reveal the spatiotemporal dynamics and driving forces of urban expansion. Differing from previous studies, we correct the blooming and over-saturation problems of nighttime light (NTL), and highlight a combination of NTL with urban population data for determining a yearly-continued and city-class-wide threshold for urban mapping. China is selected as a case study area to test the improved method and to gain insights to its urban expansion process. Through the validation, our method has been proven to be more accurate than the traditional NTL threshold method. Accordingly, the yearly-continued NTL data can better describe the changing patterns and driving forces of urban expansion than the yearly-discontinued land use and land cover data do. It is found that the total urban area in China has more than quadrupled from 25.2 in 1992 to 108.2 thousand km2 in 2013. Some significant pulses of urban expansion have been detected in our study, which may be attributed to the policy and socioeconomic impacts. Moreover, the panel regression based on annual NTL data indicates that GDP is a more important driver of urban expansion than urban population. [Display omitted] • An improved dynamic threshold method was developed for urban mapping. • Validations prove the method's accuracy better than the traditional threshold method. • Improved method can better describe urban expansion pattern than the traditional method. • GDP is a more important driver of urban expansion than urban population. • Limitations and potential improvements have been discussed. [ABSTRACT FROM AUTHOR]

Producing gridded electric power consumption (EPC) maps at a fine geographic scale is critical for rational deployment and effective utilization of electric power resources. Brightness of nighttime light (NTL) has been extensively adopted to evaluate the spatial patterns of EPC at multiple geographical scales. However, the blooming effect and saturation issue of NTL imagery limit its ability to accurately map EPC. Moreover, limited sectoral separation in applying NTL leads to the inaccurate spatial distribution of EPC, particularly in the case of industrial EPC, which is often a dominant portion of the total EPC in China. This study pioneers the separate estimation of spatial patterns of industrial and nonindustrial EPC over mainland China by jointly using points of interest (POIs) and multiple remotely sensed data in a random forests (RF) model. The POIs provided fine and detailed information about the different socioeconomic activities and played a significant role in determining industrial and nonindustrial EPC distribution. Based on the RF model, we produced industrial, non-industrial, and overall EPC maps at a 1 km resolution in mainland China for 2011. Compared against statistical data at the county level, our results showed a high accuracy (R2 = 0.958 for nonindustrial EPC estimation, 0.848 for industrial EPC estimation, and 0.913 for total EPC). This study indicated that the proposed RF-based method, integrating POIs and multiple remote sensing data, can markedly improve the accuracy for estimating EPC. This study also revealed the great potential of POIs in mapping the distribution of socioeconomic parameters. [ABSTRACT FROM AUTHOR]

Urbanization has destroyed and fragmented large amounts of natural habitats, resulting in serious consequences for urban ecosystems over past decades, especially in the rapidly urbanizing areas of developing countries. The Yangtze River Delta Urban Agglomeration, which has experienced the fastest socioeconomic development in China, was selected as the study area. To explore the relationship between urbanization and vegetation dynamics at the agglomeration scale, the spatiotemporal characteristics of urban expansion and vegetation variation of the study area were evaluated by landscape spatial analysis, regression analysis, and trend analysis. The results show that the urbanization level of the study area exhibited a continuous upward trend, with Shanghai as the regional core city, and the level of urbanization gradually decreased from the center towards the periphery of the urban agglomeration. The overall urban expansion presented obvious landscape spatial heterogeneity characteristics and the emergence of new cities and towns enhanced landscape connectedness and created a more aggregated urban agglomeration. Noticeable spatiotemporal differences of vegetation variation were observed from 2004 to 2013. Areas with relatively low vegetation coverage showed a steady growth trend, while those with higher vegetation coverage reported a significant decreasing trend. The spatial heterogeneity analysis of the vegetation trend demonstrated that vegetation degradation was a dominant and inevitable process across the study area. However, some parts of the urban sprawl area, especially at the periphery of the metropolis, may experience a greening trend rather than a browning trend, indicating that urbanization does not necessarily lead to large-scale vegetation degradation. Although urbanization poses a negative impact on vegetation and physical environments, urbanization has not yet reduced a large area of vegetation at the regional level. [ABSTRACT FROM AUTHOR]

Protected areas (PAs) provide refuges for threatened species and are considered to be the most important approach to biodiversity conservation. Besides climate change, increasing human population is the biggest threat to biodiversity and habitats in PAs. In this paper, the temporal and spatial variations of land cover changes (LCC), vegetation fraction (VFC), and net primary productivity (NPP) were studied to present the ecosystem dynamics of habitats in 6 different types of national nature reserves (NNRs) in 8 climate zones in China. Furthermore, we used Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light datasets and the human disturbance (HD) index estimated from LCC to quantify the living and developing human pressures within the NNRs in the period 2000–2013. The results showed that (1) the living human activities of NNRs increased apparently in the humid warm-temperate zone, Qinghai-Tibet Plateau, mid-temperate semi-arid zone, and mid-temperate humid zone, with the highest increase of nighttime light observed in inland wetlands; (2) the developing human activities in NNRs indicated by the HD index were higher in the humid warm-temperate zone and mid-temperate semi-arid zone as a result of increasing areas of agricultural and built activities, and lower in the sub-tropics due to improved conservation of forest ecosystems; (3) the relationship between HD and VFC suggests that ecosystems in most NNRs of south-subtropics, mid-temperate arid zone and Qinghai-Tibet Plateau were predominantly impacted by climate change. However, HDs were the prevalent factor of ecosystem dynamics in most NNRs of north-subtropics, mid-temperate semi-arid and humid zones. [ABSTRACT FROM AUTHOR]