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The Tibetan Plateau, known for its high elevation and sparse population distribution, heavily depends on gridded population data to enhance disaster prevention and management strategies. This study utilizes multi-source physical geographic and socio-economic factors to delineate the population distribution across the plateau. Using data from the seventh National Census in 2020, we apply three individual machine learning methods (Random Forest, GBDT, and XGBoost) and two multi-model ensemble methods (weighted average ensemble and stacking ensemble) to spatialize the population data into a 100-meter grid. The results reveal that the spatialization accuracy of all models exceeds that of the WorldPop dataset. Specifically, the Random Forest model (RMSE = 4061.09, nRMSE = 44.71 %) and the stacking ensemble model (RMSE = 4094.47, nRMSE = 44.26 %) demonstrate the highest accuracy among the individual and ensemble models, respectively. Emphasizing the importance of integrating multi-source big data, Tencent location-based services data emerges as a crucial variable across all models. This study highlights the effectiveness of ensemble models and multi-source big data in improving population mapping accuracy, especially in regions with complex terrains.

Nighttime light (NTL) is a reliable indicator for measuring urban development, population density, and economic activities. This study sets out to explain the spatio-temporal pattern of urbanization and population distribution using pixel-based NTL data. Changes in NTL intensity in Puglia (Italy) were evaluated from 2014 to 2023, and how NTL is affected by various spatial metrics was investigated. Summer–winter differences at the pixel level were monitored utilizing the Normalized Nighttime Light Index (NNLI), and the relationship between this indicator and tourism was assessed. A consistent increase in NTL across the region in both summer and winter was measured, with variations among provinces. Urban areas with high population density showed greater NTL values, while inland areas showed low NTL intensity. The study also revealed that proximity to urban areas was the most influential factor in predicting NTL intensity. The analysis revealed that differences between summer and winter correlated with tourist activity, especially in coastal municipalities. Leveraging NTL images can support decision-making across various sectors and aid in developing evidence-based urban strategies tailored to local needs and challenges.

As urbanization accelerates, characteristics of urban spatial expansion play a significant role in the future utilization of land resources, the protection of the ecological environment, and the coordinated development of population and land. In this study, Yunnan and Guizhou provinces were selected as the study area, and the 2013–2021 National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light (NTL) data were utilized for spatial and temporal change analysis of urban built-up areas. Firstly, the built-up areas in Yunnan and Guizhou provinces were extracted through ENUI (Enhanced Nighttime Lighting Urban Index) indices, and then the urban expansion speed and urban center of gravity migration were constructed and used to explore and analyze the spatial and temporal change and expansion characteristics of built-up areas in Yunnan and Guizhou provinces. The results showed the following. (1) Due to the complementarity between data types, such as NTL, EVI, NDBI, and NDWI, ENUI has better performance in expressing urban characteristics. (2) Influenced by national and local policies, such as “One Belt, One Road”, transportation infrastructure construction, geographic location, the historical background, and other factors, the urban expansion rate of Yunnan and Guizhou provinces in general showed a continuous advancement from 2013 to 2021, and there were three years in which the expansion rate was positive. (3) The center of gravity migration distance of most cities in Guizhou Province shows a trend of increasing and then decreasing, while the center of gravity migration distance in Yunnan Province shows a trend of continuous decrease in general. From the perspective of migration direction, Guizhou Province has the largest number of migrations to the northeast, while Yunnan Province has the largest number of migrations to the southeast. (4) Influenced by policy, economy, population, geography, and other factors, urban compactness in Yunnan and Guizhou provinces continued to grow from 2013 to 2021. The results of this study can help us better understand urbanization in western China, reveal the urban expansion patterns and spatial characteristics of Yunnan and Guizhou provinces, and provide valuable references for development planning and policymaking in Yunnan and Guizhou provinces.

The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning, resource allocation, government decision-making, disaster assessment, ecological protection, and other sustainability research. However, the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series, while GHS-POP only utilizes land use data with limited accuracy. In view of the limited remote sensing images of long time series, it is necessary to combine existing multi-source remote sensing data for population spatialization research. In this research, we developed a nighttime light desaturation index (NTLDI). Through the cross-sensor calibration model based on an autoencoder convolutional neural network, the NTLDI was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) data. Then, the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL. Furthermore, the change characteristics and the driving factors of China’s population spatial distribution are analyzed. The large-scale, long-term population spatialization results obtained in this study are of great significance in government planning and decision-making, disaster assessment, resource allocation, and other aspects.

The intensity of extreme weather events has been increasing, posing a unique threat to society and highlighting the importance of our electrical power system, a key component in our infrastructure. In severe weather events, quickly identifying power outage impact zones and affected communities is crucial for informed disaster response. However, a lack of household-level power outage data impedes timely and precise assessments. To address these challenges, we introduced an analytical workflow using NASA’s Black Marble daily nighttime light (NTL) images to detect power outages from the 2021 Winter Storm Uri. This workflow includes adjustments to mitigate viewing angle and snow reflection effects. Power outage is detected by comparing storm-time and baseline (normal condition) NTL images using an empirical adjusted equation. The outcomes of the workflow are 500-meter resolution power outage maps, which have the optimal correlation with real outage tracking data when NTL intensity is reduced by 26%. With the resultant power outage maps, we analyzed the relations between power outages and disadvantaged populations in 126 Texas counties and 4182 census tracts to evaluate environmental justice in the storm. The results show that Latino/Hispanic communities tend to suffer more from power outages at both the county and census tract levels.

The local climate zone (LCZ) scheme has been widely utilized in regional climate modeling, urban planning, and thermal comfort investigations. However, existing LCZ classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments. In this study, we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations, i.e. daytime Ziyuan-3 stereo imageries (2.1 m) and Luojia-1 nighttime light (NTL) data (130 m). Firstly, we performed land cover classification using multiple machine learning methods (i.e. random forest (RF) and XGBoost algorithms) and various features (i.e. spectral, textural, multi-view features, 3D urban structure parameters (USPs), and NTL). In addition, we developed a set of new cumulative elevation indexes to improve building roughness assessments. The indexes can estimate building roughness directly from fused point clouds generated by both along- and across-track modes. Finally, based on the land cover and building roughness results, we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping. The results for Beijing, China, show that our method yielded satisfactory accuracy for LCZ mapping, with an overall accuracy (OA) of 90.46%. The overall accuracy of land cover classification using 3D USPs generated from both along- and across-track modes increased by 4.66%, compared to that of using the single along-track mode. Additionally, the OA value of LCZ mapping using 2D and 3D USPs (88.18%) achieved a better result than using only 2D USPs (83.83%). The use of NTL data increased the classification accuracy of LCZs E (bare rock or paved) and F (bare soil or sand) by 6.54% and 3.94%, respectively. The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livability in urban areas. Ultimately, this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates.

Abstract The global COVID‐19 outbreak severely hampered the growth of the global economy, prompting the implementation of the strictest prevention policies in China. Establishing a significant relationship between changes in nighttime light and COVID‐19 lockdowns from a geospatial perspective is essential. In light of nighttime light remote sensing, we evaluated the spatiotemporal dynamic effects of COVID‐19 city lockdowns on human activity intensity in the Zhengzhou region. Prior to the COVID‐19 outbreak, nighttime light in the Zhengzhou region maintained a significant growth trend, even under regular control measures. However, following the October 2022 COVID‐19 lockdown, nighttime light experienced a substantial decrease. In the central area of Zhengzhou, nighttime light decreased by at least 18% compared to pre‐lockdown levels, while in the sub‐center, the decrease was around 14%. The areas where nighttime light decreased the most in the central region were primarily within a 15 km radius, while in the sub‐center, the decrease was concentrated within a 5 km radius. These changes in both statistical data and nighttime light underscored the significant impact of the COVID‐19 lockdown on economic activities in the Zhengzhou region.

The main factor of the formation and deterioration in China's urban thermal environment is human activity, which is difficult to describe and measure. A new perspective on the effect of human activity on the urban thermal environment can be obtained by examining the interaction between location-based service (LBS) data and the urban thermal environment in China. However, relevant research is still limited. In this study, we used Tencent LBS data, Terra/Aqua MODIS land surface temperature (LST) data, and land use data to investigate the relationship between LBS and the urban thermal environment, specifically the LST and surface urban heat island intensity (SUHII) across China and its provinces. Our results showed that (1) in summer, the heat island effect was an issue in 94% of the urban areas in China, which was worse during the day. The high- and low-value periods of LBS data on a given day coincided with the acquisition times of MODIS LST products during the day and at night, respectively. (2) During both the day and at night, there was a significant connection between LBS data and the urban thermal environment in China. The highest correlation coefficient (r) between LBS data and the LST could reach 0.55 (p < 0.01) at the provincial level, and the highest correlation coefficient (r) between LBS data and the SUHII could reach 0.78 (p < 0.01) at the provincial level. (3) The urban thermal environment diurnal difference and LBS data exhibited a significant relationship. The ΔLBS diurnal differences were significantly positively related to the SUHII diurnal differences in China. The overall study findings revealed that LBS data constitute an important parameter to represent the human activity intensity when investigating the formation of the urban thermal environment in China. [ABSTRACT FROM AUTHOR]

The health effects of urbanization are controversial. The association between urbanization and reversible subclinical risks of cardiovascular diseases (e.g., electrocardiogram (ECG) abnormalities) has rarely been studied. This study aimed to assess the association between urbanization and ECG abnormalities in China based on the China National Stroke Screening Survey (CNSSS). We used changes in the satellite-measured impervious surfaces rate and nighttime light data to assess the level of urbanization. Every interquartile increment in the impervious surfaces rate or nighttime light was related to a decreased risk of ECG abnormalities, with odds ratios of 0.894 (95% CI, 0.869–0.920) or 0.809 (95% CI, 0.772–0.847), respectively. And we observed a U-shaped nonlinear exposure–response relationship curve between the impervious surfaces rate and ECG abnormalities. In conclusion, the current average level of urbanization among the studied Chinese adults remains a beneficial factor for reducing cardiovascular risks. [ABSTRACT FROM AUTHOR]

Industrial agglomeration, as a typical aspect of industrial structures, significantly influences policy development, economic growth, and regional employment. Due to the collection limitations of gross domestic product (GDP) data, the traditional assessment of industrial agglomeration usually focused on a specific field or region. To better measure industrial agglomeration, we need a new proxy to estimate GDP data for different industries. Currently, nighttime light (NTL) remote sensing data are widely used to estimate GDP at diverse scales. However, since the light intensity from each industry is mixed, NTL data are being adopted less to estimate different industries' GDP. To address this, we selected an optimized model from the Gaussian process regression model and random forest model to combine Suomi National Polar-Orbiting Partnership—Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and points-of-interest (POI) data, and successfully estimated the GDP of eight major industries in China for 2018 with an accuracy (R2) higher than 0.80. By employing the location quotient to measure industrial agglomeration, we found that a dominated industry had an obvious spatial heterogeneity. The central and eastern regions showed a developmental focus on industry and retail as local strengths. Conversely, many western cities emphasized construction and transportation. First-tier cities prioritized high-value industries like finance and estate, while cities rich in tourism resources aimed to enhance their lodging and catering industries. Generally, our proposed method can effectively measure the detailed industry agglomeration and can enhance future urban economic planning. [ABSTRACT FROM AUTHOR]

With burgeoning economic development, a surging influx of greenhouse gases, notably carbon dioxide (CO2), has precipitated global warming, thus accentuating the critical imperatives of monitoring and predicting carbon emissions. Conventional approaches employed in the examination of carbon emissions predominantly rely on energy statistics procured from the National Bureau of Statistics and local statistical bureaus. However, these conventional data sources, often encapsulated in statistical yearbooks, exclusively furnish insights into energy consumption at the national and provincial levels, so the assessment at a more granular scale, such as the municipal and county levels, poses a formidable challenge. This study, using nighttime light data and statistics records spanning from 2000 to 2019, undertook a comparative analysis, scrutinizing various modeling methodologies, encompassing linear, exponential, and logarithmic models, with the aim of assessing carbon emissions across diverse spatial scales. A multifaceted analysis unfolded, delving into the key attributes of China's carbon emissions, spanning total carbon emissions, per capita carbon emissions, and carbon emission intensity. Spatial considerations were also paramount, encompassing an examination of carbon emissions across provincial, municipal, and county scales, as well as an intricate exploration of spatial patterns, including the displacement of the center of gravity and the application of trend analyses. These multifaceted analyses collectively contributed to the endeavor of predicting China's future carbon emission trajectory. The findings of the study revealed that at the national scale, total carbon emissions exhibited an annual increment throughout the period spanning 2000 to 2019. Secondly, upon an in-depth evaluation of model fitting, it was evident that the logarithmic model emerged as the most adept in terms of fitting, presenting a mean R2 value of 0.83. Thirdly, the gravity center of carbon emissions in China was situated within Henan Province, and there was a discernible overall shift towards the southwest. In 2025 and 2030, it is anticipated that the average quantum of China's carbon emissions will reach 7.82 × 102 million and 25.61 × 102 million metric tons, with Shandong Province emerging as the foremost contributor. In summary, this research serves as a robust factual underpinning and an indispensable reference point for advancing the scientific underpinnings of China's transition to a low-carbon economy and the judicious formulation of policies governing carbon emissions. [ABSTRACT FROM AUTHOR]

The extent of human activity has expanded, and the intensity of human activity has increased, supported by a progressive economy and powerful technology. Compared with daytime remote sensing images or statistical data, nighttime light (NTL) remote sensing has the advantages of long time series, global coverage, objectivity, and quantifiability in observing the development of global human activities. Existing studies have focused more on the levels of human activity development at multiple points in time and less on overall patterns of human activity development during a period. We developed a non-parametric time-series curve morphology classification methodology combining trend analysis and change-point detection. We identified modes and phases of human activity development based on NTL datasets and analyzed the spatial and temporal patterns, modes, and phases of county-level human activity development globally over the past 30 years (1992–2021). We found that the level of human activity development is generally on the rise globally, with areas of significant growth mainly along the coast of China and in global ports. In more than half of the counties, the extent of human activity development is saturated. A significant increase mode was observed in most counties (71.5 %), while a few counties (1.1 %) saw a decrease mode. 10.1 % of global counties are in an anomalous phase, with low levels of human activity and slower or even stopped development. The new method has been validated in cases of different development modes and has the potential to generalize to the pixel level. Monitoring human activity development facilitates understanding development modes globally over a long time series, which can guide future policy formulation and socioeconomic progress. We recommend a greater focus on inequalities within and between countries.

Remote sensing of nighttime light (NTL) offers a unique and practical means of assessing human developments through snapshots of nocturnal lighting captured from space. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) is one of the widely used NTL products and is now publicly available on Google Earth Engine (GEE), which allows accelerated analysis using different geospatial approaches. Here, we perform a comparative trajectory analysis of NTL over the three largest cities of South Africa between 2014 and 2023, and project the changes for 2027 based on Markov chain analysis. Our results show that the NTL increased steadily for the city of Durban (from 27.93 nW cm−2.sr−1 in 2014 to 29.45 nW cm−2.sr−1 in 2023) but decreased in the cities of Cape Town (from 32.30 nW cm−2.sr−1 in 2014 to 31.14 nW cm−2.sr−1 in 2023) and Johannesburg (from 52.19 nW cm−2.sr−1 in 2014 to 46.82 nW cm−2.sr−1 in 2023). The unstable power supply of South African cities can affect the NTL data. In addition, from our results, a similar pattern for each of these cities is observed to be in line with load shedding events in the country, especially for Cape Town and Johannesburg. Regarding the spatiotemporal distribution of the NTL, the central areas have medium to high radiance and have been spreading to the periphery of the cities since 2014. Moreover, land use and land cover (LULC) analysis reveals that developed urban areas, shown by dense built-up areas, coincide with the locations of medium and high NTL radiances. The assessment of NTL changes across low, medium, and high classes indicates a stable configuration with no large changes between classes; this is also observed on the spatial projection of NTL for the year 2027. Our results shed light on NTL mapping in large cities, an area that is constantly evolving due to new developments in remote sensing.

With the increasing accessibility of historical satellite datasets and the emergence of satellites with higher temporal resolution, the geospatial community faces the challenge of effectively utilizing this wealth of past information. This research aims to leverage this high temporal resolution satellite dataset to uncover longterm patterns in historical satellite data, particularly in urban areas impacted by disasters. The study focuses on baseline modelling and anomaly detection of VIIRS SNPP day/night bands VNP46A1 and VNP46A2. Preprocessing and transformation of satellite images into a pixel-based time series dataset were performed, followed by progressive harmonic modelling using one-year lag data and three-month progression parameters to generate baseline models. Subsequently, anomaly detection was conducted using fbprophet, with an optimal value of 120-day time lag used to calculate the interval width. The algorithm successfully detected anomalous values, which were then mapped and correlated with significant events occurring in the selected study areas, including severe flooding and earthquakes. The results demonstrated the algorithm's capability to extract valuable information from large historical satellite dataset and highlight its potential for real-world applications. [ABSTRACT FROM AUTHOR]

Numerous cities around the world are currently experiencing surges in population growth and urban expansion, often accompanied by uncontrolled sprawl. It is widely recognized that unplanned urban development contributes to a decline in urban sustainability. In this research, our goal is to identify instances of sprawl development in the Kansai region of Japan by utilizing remote sensing data. As a result, we have discovered that nighttime light data are valuable for gaining additional insights into sprawl, even when time-series land-use data and spatial population data are available. Specifically, when comparing the changes in these indices between circa 2010 and 2020, it becomes evident that these indices exhibit very weak correlations. Consequently, they can be used as independent variables for identifying urban sprawl. Each index comes with its own advantages and disadvantages in the context of urban sprawl detection. Therefore, we can conclude that a combination of multiple data sources is necessary for a comprehensive determination and detection of urban sprawl. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

China fully lifted restrictions of the COVID-19 pandemic on December 7, 2022. However, the influence of this policy on urban socioeconomic activities is unclear. Remotely sensed nighttime light (NTL) data have been widely used in evaluating public policies in near real time; therefore, this article attempts to explore the spatiotemporal impact of liberalization on mainland China from the perspective of NTL data. Taking 25 cities with different development levels as representatives, based on daily Black Marble NTL product (VNP46A2), we obtained monthly and weekly averaged NTL images before and after liberalization, and calculated NTL changes in concentric rings to detect the spatiotemporal variations from the urban centers, which can reflect the urban vitality to some degree. Experiment results show that urban NTL radiances within 18 km from urban center are generally on the rise after the lifting of restrictions. The increase of NTL around urban core is the most significant, and the increase intensity decreases exponentially (R2 > 0.7) with the distance from city center or near-center for most cities. Furthermore, NTL radiance generally increases significantly within 1–2 weeks after the lifting in megacities, and different levels of cities responded differently. NTL of megacities and large cities generally changed more rapidly than small–medium cities. Moreover, although mainland China has experienced 3 years of pandemic control, urban development is still underway. The lifting of COVID-19 travel restrictions has brought back the recovery of urban economic vitality. These findings can provide insightful support to urban construction and policy guidance intervention after the pandemic.

The urban functional areas (UFAs) are the basic spatial units of a city, identifying their category information and spatial distribution is of great significance for studying urban spatial structure and formulating scientific and reasonable urban planning policies. However, traditional classification systems are still limited due to the data processing costs and time-consuming process. With the development of high-resolution satellite, the 2-D image has been able to identify more ground object information on a fine scale, but it lacks the application of 3-D characteristics and social attributes. At the same time, machine learning and deep learning have shown great utility in extracting features, and they should be applied more to the classification of urban functional areas to improve efficiency. To solve these problems, we propose an efficient and accurate framework for mapping UFA using multisource geospatial data. It can grasp the dynamic changes in the city more accurately, and provide a reference for the study of urban functional areas. An improved frequency density (IFD) model was proposed to improve the overall classification accuracy by 4.4%. Besides, the nighttime light (NTL) data from SDGSAT-1 satellite glimmer imagers are also used to classify UFA. By combining NTL and urban building height data, a sky view enhanced nightlight index (SVENI) was proposed, which can improve the overall classification accuracy of UFA by 5.8%. This study systematically clarifies the role of data sources, methods, and automatic integration models in the classification framework for UFA, which is of great significance for urban planning and sustainable development.

ABSTRACTNighttime light (NTL) remote sensing data plays a crucial role in comprehending changes in human activities. The availability of the daily lunar BRDF-corrected Black Marble NTL product (VNP46A2) enables the use of NTL data to detect and assess the impact of short-term emergencies. However, daily NTL data often experience missing values due to cloud cover and low-quality signals. To address this issue, many studies utilize monthly or annual time-composite NTL products, which restrict the timeliness and potential application scenarios of NTL data usage. Therefore, it is necessary to generate the gap-filled daily NTL product. This study presented a novel NTL gap-filling method comprising rough reconstruction based on spatiotemporal weighting and refined gap-filling using a Bidirectional Long Short-Term Memory (Bi-LSTM) model. We evaluate the accuracy of the proposed method using the “remove-reconstruct-compare” approach, which randomly removes some original data from the complete image, fills the gaps with the proposed gap-filling method, and compares the reconstructed NTL data with the original observations in Beijing, Shanghai, Xi’an and New York. The results reveal that when the rate of missing values in Beijing is 40% and 50%, the proposed gap-filling method achieves accuracy with mean coefficient of determination (R2) values of 0.834 and 0.841, accompanied by corresponding root mean square (RMSE) values of 7.793 and 7.171, respectively. Furthermore, the gap-filling accuracy was evaluated quantitatively, and our proposed gap-filling method performed better than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Our proposed gap-filling method had R2 values of 0.685, 0.781, 0.720 and 0.642, which were higher than those for STARFM (0.430, 0.662, 0.221 and 0.345). The RMSE values of our gap-filling method were 9.628, 12.083, 10.963 and 19.882 for the four sites, while those of STARFM were 12.953, 14.872, 18.280 and 26.990, respectively. The temporal and spatial analysis results demonstrate that this model is robust, capturing city boundaries and NTL high-brightness hotspots with high accuracy and stability. The gap-filling model proposed in this study provides a new technique for expanding the potential applications and reliability of NASA’s daily Black Marble product (VNP46A2) in remote sensing.

The comprehensive characterization of global urbanization requires consideration of both human activities and urban physical structures. Both human activities and urban physical structures exhibit regular self-similar patterns, yet the spatial patterns between the two at a global scale remain elusive. This study utilized NPP-VIIRS annual composite data and newly available world settlement footprint 3D data to investigate the global spatial relationships between nighttime light intensity and urban building morphological indicators across several spatial scales. Our results demonstrated that there is a weak association between nighttime light intensity and urban building morphology at the pixel level, as shown by a maximum correlation coefficient of approximately 0.4, but a strong correlation at the provincial/state level with a correlation coefficient over 0.8. Additionally, we performed an urban-rural gradient analysis to evaluate the spatial patterns between nighttime light intensity and urban building morphological indicators. The results indicated that the dominant urban-rural gradients for both nighttime light intensity and building morphologies follow a declining trend from urban centers to rural areas. Notably, spatial inconsistencies between nighttime light intensity and building morphology were found predominantly in Africa. Our findings also suggested that spatial patterns between nighttime light intensity and urban building morphology can be served as an indicator of urbanization, and thus can provide implications for facilitating solutions aimed at reducing income disparity and promoting sustainable urban development.

Introdution: One crucial method to attain Sustainable Development Goals (SDGs) involves timely adjustment of development policies, promoting the realization of SDGs through a time-series assessment of the degree of accomplishment. In practical applications, data acquisition is a significant constraint in evaluating the SDGs, not only in China but across the globe. Hence, expanding data channels and exploring the feasibility of various data sources for sustainable development assessment are effective strategies to tackle the challenge of data acquisition.Methods: In light of this issue, this study selected Nighttime Light Data, a remote sensing data source closely linked to human social activities, as an alternative data source. Using Yunnan Province as an example, 16 localized indicators of social, economic, and environmental types were chosen. These indicators were then subjected to a correlation analysis with the Total Nighttime Light Index (TNLI). The relationships between different types of indicators and TNLI were analyzed at both temporal and spatial scales, thus identifying the indicators for which TNLI could serve as a suitable substitute measure.Results: The study indicates that when the SDG indicators are classified into economic, social and environmental categories, the total value of nighttime light presents a significant correlation and substitutability with economic indicators; significantly correlated with some social indicators, it can reveal the weak links in the development of underdeveloped areas; it is not significantly correlated with environmental indicators, while a trend correlation exists, which can provide some reference values.Discussion: This study has demonstrated the feasibility of using Nighttime Light Data for sustainable development assessment. It provides a novel evaluation method for countries that, despite a lack of resources for conducting sustainable development assessments, have a greater need for such assessments due to their lower economic development. Furthermore, a multitude of assessment methods can be developed based on Nighttime Light Data.

Poverty is a social issue of global concern. Although socioeconomic indicators can easily reflect poverty status, the coarse statistical scales and poor timeliness have limited their applications. While spatial big data with reasonable timeliness, easy access, and wide coverage can overcome such limitations, the integration of high-resolution nighttime light and spatial big data for assessing relative poverty is still limited. More importantly, few studies have provided poverty assessment results at a grid scale. Therefore, this study takes the Pearl River Delta, where there is a large disparity between the rich and the poor, as an example. We integrated Luojia 1-01, points of interest, and housing prices to construct a big data poverty index (BDPI). To evaluate the performance of the BDPI, we compared this new index with the traditional multidimensional poverty index (MPI), which builds upon socioeconomic indicators. The results show that the impoverished counties identified by the BDPI are highly similar to those identified by the MPI. In addition, both the BDPI and MPI gradually decrease from the center to the fringe of the study area. These two methods indicate that impoverished counties were mainly distributed in ZhaoQing, JiangMen and HuiZhou Cities, while there were also several impoverished parts in rapidly developing cities, such as CongHua and HuaDu Counties in GuangZhou City. The difference between the two poverty assessment results suggests that the MPI can effectively reveal the poverty status in old urban areas with convenient but obsolete infrastructures, whereas the BDPI is suitable for emerging-development areas that are rapidly developing but still lagging behind. Although BDPI and MPI share similar calculation procedures, there are substantial differences in the meaning and suitability of the methodology. Therefore, in areas lacking accurate socioeconomic statistics, the BDPI can effectively replace the MPI to achieve timely and fine-scale poverty assessment. Our proposed method could provide a reliable reference for formulating targeted poverty-alleviation policies. [ABSTRACT FROM AUTHOR]

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method's limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation. [ABSTRACT FROM AUTHOR]

Climate change caused by CO2 emissions is posing a huge challenge to human survival, and it is crucial to precisely understand the spatial and temporal patterns and driving forces of CO2 emissions in real time. However, the available CO2 emission data are usually converted from fossil fuel combustion, which cannot capture spatial differences. Nighttime light (NTL) data can reveal human activities in detail and constitute the shortage of statistical data. Although NTL can be used as an indirect representation of CO2 emissions, NTL data have limited utility. Therefore, it is necessary to develop a model that can capture spatiotemporal variations in CO2 emissions at a fine scale. In this paper, we used the nighttime light and the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), and proposed a normalized urban index based on combination variables (NUI-CV) to improve estimated CO2 emissions. Based on this index, we used the Theil–Sen and Mann–Kendall trend analysis, standard deviational ellipse, and a spatial economics model to explore the spatial and temporal dynamics and influencing factors of CO2 emissions over the period of 2000–2020. The experimental results indicate the following: (1) NUI-CV is more suitable than NTL for estimating the CO2 emissions with a 6% increase in average R 2 . (2) The center of China's CO2 emissions lies in the eastern regions and is gradually moving west. (3) Changes in industrial structure can strongly influence changes in CO2 emissions, the tertiary sector playing an important role in carbon reduction. [ABSTRACT FROM AUTHOR]

Nighttime light guarantees outdoor work, leisure, and other activities, and its convenience and security greatly improve the quality of human life. However, the negative effects of outdoor artificial light at night (ALAN) on humans are also being proven. This study aimed to examine the relationships between outdoor nighttime lighting and human behaviors (i.e., outdoor nighttime behaviors such as jogging and meeting friends) and health (i.e., physical and mental health, including sleep quality and stress) through how individuals feel about outdoor ALAN (i.e., ALAN perceptions) in their living environment. Two online questionnaire surveys were conducted among residents of Tokyo (Survey 1, N = 2000) and Tsukuba City (Survey 2, N = 500), Japan. Structural equation modeling results show that both positive and negative ALAN perceptions increase nighttime outdoor behavior in both surveys. In Survey 1, this association led to a deterioration in sleep quality and physical and mental health. The current findings contribute to understanding the controversial relationships between the pros and cons of nighttime light perceptions and behaviors and health. They also contribute to designing better nighttime lighting in outdoor public and private spaces with relevant restrictions by balancing the benefits and harms of outdoor ALAN. [ABSTRACT FROM AUTHOR]

Today, with the non-stop expansion of urbanization, mapping urban areas and monitoring their dynamic changes have become challenges for governments and also a hot topic for researchers. Remote sensing imageries play a key role in urban studies, the extraction of urban built-up areas, and monitoring their changes. A variety of studies have proposed methods for the extraction of regional, national, and global built-up areas. However, the majority of them used limited features and applied a manual sample selection strategy for classification, leading to time-consuming and low-efficient algorithms. This paper proposes a fully automatic procedure to real-time extract built-up areas by integrating the Luojia 1–01 nighttime lights (NTL) images, Sentinel-2 multispectral data, Sentinel-1 Radar images, and SRTM elevation data in cloud-computing Google Earth Engine. Firstly, potential built-up areas (PBA) and non-built-up areas (NBA) are obtained by applying Otsu and multi-level thresholding to some of the extracted spectral-textural-spatial (STS) features and by applying logical rules. Secondly, built-up and non-built-up samples are automatically selected and are used to train a Support Vector Machine (SVM) supervised classifier and to classify the hybrid feature set so that a preliminary classified map (PCM) can be obtained. Thirdly, the PCMs are automatically corrected using the non-built-up area, and morphological operations in the so-called post-classification to provide a refined classified map (RCM) and final built-up map. Four study areas in Northern America, Europe (Scandinavia), the Middle East, and Eastern Asia were selected to test the proposed method. Also, five state-of-the-art built-up products, accompanied by Google Earth images, were used as the reference data. The results indicate that the proposed method can accurately and automatically select samples and map built-up areas with a spatial resolution of 10 m. Its performance is validated with an average overall accuracy of 94.4% and an average Kappa coefficient of 0.89 and by visual comparison of our method results with other reference data. The proposed method has significant potential to be used in real-time extracting built-up areas and in monitoring their dynamic changes on national and global scales. [ABSTRACT FROM AUTHOR]

The spatial extent and values of nighttime light (NTL) data are widely used to reflect the scope and intensity of human activities, such as extracting urban boundaries, spatializing population density, analyzing economic development levels, etc. DMSP-OLS and NPP-VIIRS are widely used global NTL datasets, but their severe inconsistencies hinder long-time series studies. At present, global coverage, long time series, and public NTL products are still rare and have room for improvement in terms of pixel-scale correction, temporal and spatial consistency, etc. We proposed a set of inter-correction methods for DMSP-OLS and NPP-VIIRS based on two corrected DMSP-OLS and NPP-VIIRS products, i.e., CCNL-DMSP and VNL-VIIRS, with the goal of temporal and spatial consistency at the pixel-scale. A pixel-scale corrected nighttime light dataset (PCNL, 1992–2021) that met the needs of pixel-scale studies was developed through outlier removal, resampling, masking, regression, and calibration processes, optimizing spatial and temporal consistency. To examine the quality of PCNL, we compared it with two existing global long time series NTL products, i.e., LiNTL and ChenNTL, in terms of overall accuracy, spatial consistency, temporal consistency, and applicability in the socio-economic field. PCNL demonstrates great overall accuracy at both the pixel-scale (R2: 0.93) and the city scale (R2: 0.98). In developing, developed, and war regions, PCNL shows excellent spatial consistency. At global, national, urban, and pixel-scales, PCNL has excellent temporal consistency and can portray stable trends in stable developing regions and abrupt changes in areas experiencing sudden development or disaster. Globally, PCNL has a high correlation coefficient with GDP (r: 0.945) and population (r: 0.971). For more than half of the countries, the correlation coefficients of PCNL with GDP and population are higher than the results of ChenNTL and LiNTL. PCNL can analyze the dynamic changes in socio-economic characteristics over the past 30 years at global, regional, and pixel-scales. [ABSTRACT FROM AUTHOR]

Objectives: Qimingxing-1 (QMX-1) satellite can obtain nighttime light images, which was developed by Wuhan University and launched on 27 February 2022. In this study, we compare the QMX-1 nighttime light images with Luojia1-01 (LJ1-01) nighttime light images. Methods: The study selects the urban area of Beijing, Shenzhen and Wuhan as the study areas, and compares the two kinds of images in terms of statistical characteristics and road network analysis. Using wavelet decomposition to evaluate the spatial characteristics of QMX-1 nighttime light images. Results: The experimental results show that the digital number distribution of QMX-1 nighttime light images is different from that of the LJ1-01 nighttime light images. Besides, by comparing the coefficient of variation of four different surface feature types in the images, the pixel value of the QMX-1 nighttime light image is more homogeneous. For the road network analysis, the roads in QMX-1 nighttime light images are easier to identify, of which the digital number has a stronger correlation with road length and road kernel density. Additionally, in the QMX-1 nighttime light images, about 15% of the wavelet component energy is distributed in the 23 m and 46 m resolution, indicating that QMX-1 nighttime light images contain rich spatial details. Conclusions: The study shows that the QMX-1 nighttime light image is a nighttime light data with high quality, and nighttime light images with a spatial resolution about 20 m can effectively provide more detailed urban information. [ABSTRACT FROM AUTHOR]

Nighttime light (NTL) remote sensing data offer unique capabilities to characterize both the extent and intensity of human activities and have been extensively used to understand urbanization since 1992. The recent proliferation of NTL sensors, algorithms, and products creates new opportunities to understand contemporary urbanization and the associated socioeconomic and environmental changes. We conducted a comprehensive literature review of 688 peer-reviewed papers published between 1992 and 2022 to understand the trends in how NTL data have been used to study urbanization (e.g., with which data products, during which time span, and in which geographies) and to synthesize the progress and challenges of key urban application topics. Based on our review, we identified four research directions for future NTL-based urban applications: (1) a better understanding of scale effects and sources of variations in NTL data; (2) integrating multi-source NTL data and synergizing NTL data with other types of geospatial data for improved NTL utilization; (3) more research on the Global South; and (4) developing new urban applications with new NTL data products. Addressing research gaps in these areas will generate new insights into the urbanization process under different geographical and socioeconomic settings. [ABSTRACT FROM AUTHOR]

The Turkey–Syria earthquake on 6 February 2023 resulted in losses such as casualties, road damage, and building collapses. We mapped and quantified the areas impacted by the earthquake at different distances and directions using NOAA-20 VIIRS nighttime light (NTL) data. We then explored the relationship between the average changes in the NTL intensity, population density, and building density using the bivariate local indicators of the spatial association (LISA) method. In Turkey, Hatay, Gaziantep, and Sanliurfa experienced the largest NTL losses. Ar Raqqah was the most affected city in Syria, with the highest NTL loss rate. A correlation analysis showed that the number of injured populations in the provinces in Turkey and the number of pixels with a decreased NTL intensity exhibited a linear correlation, with an R-squared value of 0.7395. Based on the changing value of the NTL, the areas with large NTL losses were located 50 km from the earthquake epicentre in the east-by-south and north-by-west directions and 130 km from the earthquake epicentre in the southwest direction. The large NTL increase areas were distributed 130 km from the earthquake epicentre in the north-by-west and north-by-east directions and 180 km from the earthquake epicentre in the northeast direction, indicating a high resilience and effective earthquake rescue. The areas with large NTL losses had large populations and building densities, particularly in the areas approximately 130 km from the earthquake epicentre in the south-by-west direction and within 40 km of the earthquake epicentre in the north-by-west direction, which can be seen from the low–high (L-H) pattern of the LISA results. Our findings provide insights for evaluating natural disasters and can help decision makers to plan post-disaster reconstruction and determine risk levels on a national or regional scale. [ABSTRACT FROM AUTHOR]

The tremendous advancement of cities has caused changes to the urban subsurface. Urban climate problems have become increasingly prominent, especially with regard to the intensification of the urban heat island (UHI) effect. The local climate zone (LCZ) is a new quantitative method for analyzing urban climate that is based on the kind of urban surface and can effectively deal with the problem of the hazy distinction between urban and rural areas in UHI effect research. LCZs are widely used in regional climate modeling, urban planning, and thermal comfort surveys. Existing large-scale LCZ classification methods usually use visual features of optical images, such as spectral and textural features. There are many problems with hyperspectral LCZ extraction over large areas. LCZ is an integrated concept that includes features of the geography, society, and economy. Consequently, it makes sense to consider the characteristics of human activity and the visual features of the images to interpret them accurately. ALOS_DEM data can depict the city's physical characteristics; however, images of nighttime lights are crucial indicators of human activity. These three datasets can be used in combination to portray the urban environment. Therefore, this study proposes a method for fusing daytime and nighttime data for LCZ mapping, i.e., fusing daytime Zhuhai-1 hyperspectral images and their derived feature indices, ALOS_DEM data, and nighttime light data from Luojia-1. By combining daytime and nighttime information, the proposed approach captures the temporal dynamics of urban areas, providing a more complete representation of their characteristics. The integration of the data allows for a more refined identification and characterization of urban land cover. It comprehensively integrates daytime and nighttime data, exploits synergistic information from multiple sources, and provides higher accuracy and resolution for LCZ mapping. First, we extracted various features, namely spectral, red-edge, and textural features, from the Zhuhai-1 images, ALOS_DEM data, and nighttime light data from Luojia-1. Random forest (RF) and XGBoost classifiers were used, and the average impurity reduction method was employed to assess the significance of the variables. All the input variables were optimized to select the best combination of variables. The results from a study of the 5th ring road area of Beijing, China, revealed that the technique achieved LCZ mapping with good precision, with a total accuracy of 87.34%. In addition, to examine and contrast the effects of various feature indices on the LCZ classification accuracy, feature combination methods were used. The results of the study showed that the accuracies of LCZ classification in terms of spectral and textural were improved by 2.33% and 2.19% using the RF classifier, respectively. The radiation brightness value (RBV) (GI value = 0.0212) attained the classification's highest variable importance value; the DEM also produced a high GI value (0.0159), indicating that night lighting and landform features strongly influence LCZ classification. [ABSTRACT FROM AUTHOR]

Urbanization is a crucial indicator which reflects the socio-economic development of a country or region. The regions across the Taiwan Strait (TSR) have garnered attention worldwide as being representative of typical urbanization development along the southeastern coast. Currently, research in the TSR predominantly focuses on individual regions, with limited academic achievements comparing urbanization paths across the strait. In particular, the domain of comparative analysis of the spatiotemporal characteristics of urbanization dynamics in TSR by using long time series of nighttime light data remains largely underexplored. Therefore, this study focused on comparing the urbanization paths in the TSR and analyzing the spatiotemporal characteristics of urbanization by using the long-term nighttime light data from 1992 to 2020. Additionally, some methods such as Theil–Sen median trend analysis, Mann–Kendall significance test, Hurst exponent, spatial statistics, and time series were used to quantitatively analyze the spatial distribution patterns and temporal trends of nighttime lights in the TSR since 1992. The results were as follows: (1) From 1992 to 2020, the spatial distribution of nighttime light data in TSR exhibited significant spatial heterogeneity, with high-value areas mainly located in southeastern Fujian and northwestern Taiwan, while low-value areas were concentrated in Fujian's inland regions; (2) During this period, nighttime lighting data increased from 729,863 in 1992 to 2,729,052 in 2020, and the percentage of its high-value (40–063) increased from 2.59% in 1992 to 12.22% in 2020; (3) Comparison of nighttime light data across representative cities from Taiwan (Taipei, Hsinchu) and Fujian (Xiamen, Fuzhou) uncovered distinct growth patterns—while Taiwanese cities had a high initial brightness value (the lowest value in the last 30 years was 518,379.4), their growth was relatively slow (average growth rate of 17%); Fujian cities, on the other hand, started from lower initial brightness value (the lowest value in the last 30 years was 35,123.1), but displayed substantial growth vigor (average growth rate of 222%); (4) During the study period, the nighttime light data of the vast majority of cities in the TSR demonstrated a significant increasing trend, particularly in coastal areas and urban centers; (5) Predictions of future trend variation suggest that the significantly increasing trend of cities surrounding Taiwan's primary metropolitan areas will intensify, whereas metropolitan regions such as Keelung may witness a decline in future trend variations. However, only a mere 0.03% of the nighttime light data show a significant decreasing trend. Additionally, there are distinct differences in the urbanization development stages of the TSR. Fujian is currently undergoing rapid urbanization, while Taiwan's urbanization has entered a stable stage. The study reveals that factors such as geographical location, natural resources, transportation infrastructure, population size, and industrial structure collectively influence the urbanization characteristics of the TSR. This research bears substantial significance for deepening the comprehension of the patterns and processes of urbanization development in the TSR and provides valuable insights for urban construction and development across the strait. [ABSTRACT FROM AUTHOR]

The agglomeration of the rural e-commerce industry represented by Taobao villages has reshaped the existing urban and rural spatial organization and proposed a new urbanization model. This study identified the spatiotemporal characteristics of Taobao villages in Quanzhou city and built panel regression models to examine the impact of these villages on urbanization level, which is measured via nighttime light (NTL). The results show that (1) while the number of Taobao villages in Quanzhou city has increased rapidly, it has also experienced sporadic growth and monocentric agglomeration, finally forming a polycentric agglomeration pattern; (2) Taobao villages display a significant near-city tendency, and the urbanization level of towns with Taobao villages is higher than that of towns without Taobao villages; (3) the panel regression model highlights that Taobao villages have a significant positive impact on urbanization level. Taobao villages near the city exhibit a greater effect; meanwhile, those that are far away from the city cannot improve their urbanization level unless they reach a considerable degree of agglomeration. Rural e-commerce will become an important direction for the transformation of urban fringe areas, which provides a certain reference for the development of new urbanization in China. [ABSTRACT FROM AUTHOR]

In China, tourism development is a crucial approach to poverty alleviation. With the consolidation of poverty alleviation achievements and the promotion of rural revitalization, it is of great significance to explore the relationship between tourism development and poverty alleviation from the perspective of multidimensional poverty. Therefore, this study took 28 key assistance counties for rural revitalization in the Sichuan–Chongqing region (hereinafter referred to as “key counties”) as the research objects, introduced NPP-VIIRS nighttime light (NTL) data, and a coupling coordination degree (CCD) model to explore the coordination relationship and mechanism between them. The results showed that from 2015 to 2020, the tourism development index (TDI) and estimated comprehensive development index (ECDI) of the key counties increased by 112.57% and 115.12%, respectively. In addition, the spatial differences in tourism development and multidimensional poverty both showed a narrowing trend. According to the results of the CCD model, the key counties basically faced coordination obstacles in the early stage, which were mainly transformed into reluctant coordination and moderate coordination in the later stage. This indicated that tourism poverty alleviation showed a coordinated development trend overall. However, the study also found that there may not be synchronicity between tourism development and poverty alleviation and analyzed the mechanism of their interaction. Overall, the study confirmed the positive impact of tourism development on alleviating multidimensional poverty. In addition, the study found that measuring multidimensional poverty based on NTL data has a high accuracy and can provide support for poverty research. These research results have an important reference value for China to carry out sustainable tourism poverty alleviation and comprehensively promote rural revitalization.

The accelerated urbanization taking place across Xinjiang in recent years has vastly improved the quality of life for people living in the region. However, to achieve rational urban growth and sustainable regional development, a deeper understanding of the spatial and temporal patterns, spatial morphology, and driving factors of urban sprawl is crucial. Nighttime light (NTL) data provide a novel approach for studying the spatial and temporal changes in urban expansion. In this study, based on DMSP-OLS and NPP-VIIRS data, we analyze the spatiotemporal characteristics of urban changes using the standard deviation ellipse and employ the geographical detector to analyze the impact of natural environmental and socioeconomic factors on the dynamic rate of urban expansion. The results reveal the following. (1) The overall accuracy of urban area extraction is above 80%, and the urban area of Xinjiang has expanded about 9.1 times over the past 30 years. Further, the growth rate from 2007 to 2017 exceeds the growth rate from 1992 to 1997, with the center of gravity of urban development shifting to the southwest. (2) The 5a sliding average temperature and average annual precipitation in the study area in 1992–2022 are 6.08 °C and 169.72 mm, respectively, showing a decrease in the urbanization rate followed by an increase, due to a rise in temperature and precipitation levels. (3) By combining the results of geographical detector factor detection and interaction detection, precipitation is determined to be the main controlling factor, while air temperature and GDP are secondary factors. This study presents new findings on the correlation between urban spatial and temporal changes and climate in Xinjiang, thus providing a scientific reference for future research on urban expansion and natural environment evolution.

Remotely sensed light imagery provides a unique perspective for high-frequency human activity monitoring. NASA has developed the Black Marble, a daily nighttime light (NTL) product from the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime images by correcting environmental effects (i.e., atmospheric, lunar, and stray light effects). However, variation and uncertainty remain in the daily lunar-BRDF corrected NTL product and hinder its quantitative applications. To better understand the uncertainty across multiple spatial and temporal scales, this study proposed a spatial–temporal hierarchical analysis strategy to separate uncertainties from different sources, and evaluated the effects of multiple factors on daily NTL time series. The experiments conducted on two populous regions show that: (1) The daily NTL in Northern America has variations up to 50% of the annual average, which is stronger than in East Asia, with variations up to 25%. (2) The variation in seasonality is stronger in Northern America, whereas the variation related to day-to-day changing factors is stronger in East Asia. (3) Generalized linear models are built to capture the relationship between daily NTL and influential factors in each grid, revealing that approximately 25%∼50% of the daily variations can be explained by environmental factors, observational factors, and seasonality. (4) Environmental factors and observational conditions show spatially varying impacts on NTL. Specifically, aerosol exhibit opposite impacts on rural and urban areas; significant impacts of moonlight are mainly distributed in rural areas; the impact of satellite viewing angle is less pronounced and frequent in East Asian cities compared to those in North America. This research revealed the essential knowledge about variations of satellite-derived NTL and will benefit the processing and utilization of daily NTL products.

Detailed snow cover maps are essential for estimating the earth's energy balance and hydrological cycle. Mapping the snow cover across spatially extensive and topographically complex areas with less or no cloud obscuration is challenging, but the SNPP-VIIRS Day/Night Band (DNB) nighttime light data offers a potential solution. This paper aims to map snow cover distribution at 750 m resolution across the diverse 1,664,900 km2 of Xinjiang, China, based on SNPP-VIIRS DNB radiance. We implemented a swarm intelligent optimization technique Krill Herd algorithm, which finds the optimal threshold value by taking Otsu's method as the objective function. We derived SNPP-VIIRS DNB snow maps of 14 consecutive scenes in December 2021, compared our snow-covered area estimations with those from MODIS and AMSR2 standard snow cover products, and generated composite snow maps by merging MODIS and SNPP-VIIRS DNB data. Results show that SNPP-VIIRS DNB snow maps are capable of providing reliable snow cover maps superior to MODIS and AMSR2, with an overall accuracy level of 84.66%. The composite snow maps at 500 m spatial resolution provided 55.85% more information on snow cover distribution than standard MODIS products and achieved an overall accuracy of 84.69%. Our study demonstrated the feasibility of snow cover detection in Xinjiang based on SNPP-VIIRS DNB, which can serve as a supplementary dataset for MODIS estimations where clouded pixels are present. [ABSTRACT FROM AUTHOR]

The Yangtze River Delta Urban Agglomeration (YRDUA), which is located in the convergence zone of "The Belt and Road Initiative", is one of the regions with the best urbanization foundations in China. Referring to the four five-year plans (China's national economic plan), this study aimed to investigate the spatiotemporal patterns of urban expansion in the YRDUA from 2000 to 2020. To conduct a long-term analysis of urbanization, an extended time series (2000–2020) of a nighttime light (NTL) dataset was built from the multi-temporal Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) data (2000–2013), and Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data (2014–2020); data from these sources are crucial to understanding the urbanization processes in the region in order for more effective decision making to take place. The support vector machine (SVM) method was used to extract urban clusters from the extended time-series NTL data and MODIS NDVI products. The evolution of the urban expansion intensity was detected at city scales, and the inequality of urban growth was demonstrated using the Lorenz curve and Gini coefficient. Finally, a quantitative relationship between urban NTL intensity and socio-economic data was built to explore the main factors that control urban intensity. The results indicated that the urban extents extracted from time-series NTL data were consistent with those extracted from Landsat data, with an average overall accuracy (OA) of 89%. A relatively fast urbanization pace was observed during the 10th five-year plan (from 2000 to 2005), which then declined slightly in the 11th five-year plan (from 2006 to 2010). By the 12th and 13th five-year plan (from 2011 to 2020), urban clusters in all cities tended to grow steadily. Urban expansion has presented a radial pattern around the main cities, with sprawl inequality across cities. The results further revealed that the primary factors controlling NTL brightness were gross domestic product (GDP), total fixed asset investment, tertiary industry, gross industrial output, urban area, and urban permanent residents in city clusters, but the same driving factors had a different contribution order on the NTL intensity across cities. This study provides significant insight for further urbanization study to be conducted in the YRDUA region, which is crucial for sustainable urban development in the region. [ABSTRACT FROM AUTHOR]

Zipf's law, characterizing the rank-size distribution of city size or population, has been widely applied in urban studies. Previous studies on Zipf's law mainly focused on the spatial dimension of urban size and population, with limited consideration of its temporal dynamics. Here, we proposed a conceptual model to characterize the spatial sprawl pattern of urban clusters using the approach of Zipf's law and approximately 30-year time series of global urban extent data. First, we quantified increments of urban areas in global megacities and small settlements over the past three decades. Urban sprawl patterns (i.e., equilibrium, diffuse, and aggregated) were revealed from the Zipfian dynamics, using the proposed conceptual model. Then, we characterized urban sprawl patterns at different spatial scales. Overall, the sprawl of small urban clusters is slightly faster than those of large clusters globally. At the continental scale, the sprawl pattern shows equilibrium patterns in Asia and Africa, whereas other continents mainly present diffuse sprawl patterns. In general, for developing regions (e.g., North and West Africa), an aggregated sprawl pattern was observed, whereas, for those highly developed regions (e.g., Canada and West Europe), the diffuse sprawl pattern dominated. Patterns of urban sprawl revealed in this study reflect different growth rates of varying sizes of urban clusters, advancing our understanding of urban development pathways. The relatively robust performance of Zipf's law over multiple spatial and temporal dimensions suggests the self-organized mechanism of city evolution behind urban sprawl, which contributes to the development of future urban growth models. [ABSTRACT FROM AUTHOR]

Impervious surfaces affect the ecosystem function of watersheds. Therefore, the impervious surface area percentage (ISA%) in watersheds has been regarded as an important indicator for assessing the health status of watersheds. However, accurate and frequent estimation of ISA% from satellite data remains a challenge, especially at large scales (national, regional, or global). In this study, we first developed a method to estimate ISA% by combining daytime and nighttime satellite data. We then used the developed method to generate an annual ISA% distribution map from 2003 to 2021 for Indonesia. Third, we used these ISA% distribution maps to assess the health status of Indonesian watersheds according to Schueler's criteria. Accuracy assessment results show that the developed method performed well from low ISA% (rural) to high ISA% (urban) values, with a root mean square difference value of 0.52 km2, a mean absolute percentage difference value of 16.2%, and a bias of −0.08 km2. In addition, since the developed method uses only satellite data as input, it can be easily implemented in other regions with some modifications according to differences in light use efficiency and economic development in each region. We also found that 88% of Indonesian watersheds remain without impact in 2021, indicating that the health status of Indonesian watersheds is not a serious problem. Nevertheless, Indonesia's total ISA increased significantly from 3687.4 km2 in 2003 to 10,505.5 km2 in 2021, and most of the increased ISA was in rural areas. These results indicate that negative trends in health status in Indonesian watersheds may emerge in the future without proper watershed management. [ABSTRACT FROM AUTHOR]

Insulators deposits can cause flashovers when the right conditions are met, threatening the normal operation of power grids. Grid staffs performed massive in situ measurements to obtain the ESDD observations and then produced susceptibility level maps, helping guiding cleaning the insulators deposits. Such works are time-consuming and costly, and relies on a limited number of discrete point measurements to infer the susceptibility distribution in the spatial domain. In this work, we proposed a novel assessment model for mapping deposits accumulation on insulators using remotely sensed products, emission inventories and other text data. We have spatialized the text data first and then performed the spatial registration to unify the spatial coordinate systems of multi-sources data. On that basis, we utilized a scoring method to quantitatively evaluate the susceptibility of deposits accumulation on insulators after determining the weights and scoring rule for different attributes using the rough set theory. Results showed that the susceptibility map can very well indicate the major risky regions via comparisons with the present susceptibility level map. Further classification experiments yielded results with an accuracy of 83.6%. We believed that such a novel method can provide great helps for the grid maintenance by reducing costs and improving performances of the insulator deposits susceptibility maps. [ABSTRACT FROM AUTHOR]

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The article estimates the impact of tourism on the nighttime economy drawing on panel data between 2005 and 2019 for Chinese 280 cities. Both tourist arrivals and tourism revenue have significant negative effects on nighttime economic development. Various rigorous robustness tests confirm the validity of this finding. In particular, the effect of tourist arrivals on the nighttime economy is significantly positive in Eastern and Western China and the period 2005-2009. On the contrary, tourism revenue still affects the nighttime economy negatively for different subsamples. Moreover, regional socioeconomic variables significantly weaken the negative effect of tourism on the nighttime economy. This paper is the first study of investigating the effects of tourism on the nighttime economy and the underlying moderating mechanisms, thus contributing significantly to the existing body of knowledge. [ABSTRACT FROM AUTHOR]

• Developed a framework for quantifying dynamic embodied carbon emissions (ECE). • Evaluated multi-scale evolution of ECE from in-use steel stock (IUSS) in the B&R area. • IUSS growth resulted in a 2.5-fold increase in ECE from 1992 to 2020. • Observed geographical convergence in IUSS-ECE evolution in the B&R region. • Steelmaking, energy, recycling, and regional exchange are critical to reducing IUSS-ECE. Embodied carbon emissions (ECE) from in-use steel stocks (IUSS) are crucial for urban sustainability and climate goals, yet relevant spatiotemporal information is still lacking. This study employs multi-source spatiotemporal data to develop a dynamic estimation method, evaluating the spatiotemporal dynamics of IUSS-ECE across 67 countries along the Belt and Road (B&R) from 1992 to 2020. The results indicate that a significant increase in IUSS in the B&R region led to a 24 GtCO 2 rise in ECE between 1992 and 2020, with 2005 marking a turning point for accelerated growth. ECE intensity per unit area increased from 3.4 to 5.9 ktCO 2 /km2, mainly in eastern B&R regions. Spatiotemporal analysis reveals the area convergence effect in ECE across the B&R, indicating that geographical relationships significantly influence ECE evolution. Additionally, the study emphasizes the significance of short-process steelmaking technology, clean energy supply, and promoting cross-regional resource circulation for the decarbonization of IUSS. [ABSTRACT FROM AUTHOR]

This study investigates the economic impact of China's "stadium diplomacy" in Sub-Saharan Africa. Exploiting the staggered timing of the construction in a difference-in-differences framework, we analyze the effect of Chinese-built and financed stadiums on local economic development. Employing nighttime light satellite data, we provide both an aggregate and spatially disaggregated assessment of these investments. We find that a stadium's city nighttime light intensity increases by about 24 percent, on average, after stadium completion. The effects can be attributed to the stadiums but are not only visible close to the stadium's location. Estimates on nighttime light activity are mirrored by individual-level employment effects in the stadiums' surrounding area. For stadiums not built or financed by China, we cannot find similar effects. Our results contrast with the widely held notion that China's development finance projects constitute "white elephants". • Estimation of causal effect of the Chinese "stadium diplomacy" on local economic development in Sub-Saharan Africa. • Stadiums have large and persistent effect on nighttime light emissions. • On average, a stadium increases local economic development proxied by nightlights by 27 percent over the whole post-treatment period at the city level. • We use DHS enumeration areas within 5 and 10km of the stadiums and find an increase in employment of up to 10 percentage points. [ABSTRACT FROM AUTHOR]

Rapid urban expansion reflects the increasing economic growth in China. As an original conceive for urban sustainability, economic growth contributes to vegetation changes. However, the spatiotemporal heterogeneity of the relationships between these two factors remains unclear in China. After analyzing the spatiotemporal characteristics of the urban expansion in different cities in China from 2002 to 2020, we found a rapid and continuous urban expansion in China, with the mean annual expansion of 1710.96 km2 year−1 (p 60% of cities showed incoordination status and 38.08% of cities showed strongly incoordination status. At pixels levels, these strong incoordination status in urban areas can be fully explained by long-term average NTL digital Number (DN) gradient.Based on a piecewise linear regression model, the proportion of areas with the strong incoordination status was significantly increased by the 1.07% DN−1 (p

Rapid urbanization has placed the sustainable development of some watershed ecosystems in jeopardy. In order to achieve sustainable urban development, it is vital to identify the coupling mechanisms between urbanization and the ecological environment quality. This study establishes indicators to evaluate the Jiulong River Basin’s urbanization and ecological environment systems. These are utilized to analyze spatial and temporal changes and build a coupling coordination degree model. This research investigates the level of development coordination between urbanization and the ecological environment quality in the basin. The data sources include nighttime lighting and Landsat data from 2000 to 2020. The findings indicated the following: 1) Urbanization levels in the basin rise annually, and the years 2010–2020 represent a stage of high urbanization growth. In addition, the development levels are spatially heterogeneous, with high levels in the south and low levels in the north. 2) The ecological environment quality category for the basin is generally Excellent; however, many facets of the climate and human activity drastically lowered this grade in 2005. 3) In the basin, there is a basic coordination relationship between urbanization and environmental quality, but the number of cities falling into the moderate imbalance category has grown. 4) Increasing the urbanization level raises the coordination between urbanization and the ecological environment more than improving environmental quality, but antagonistic effects make it necessary to strengthen the protection of the ecological environment alongside economic development. From the viewpoint of counties, this study examines the long-term interactions between the ecological environment and urbanization in Fujian Province, China. Recommendations for balancing urban development and environmental concerns in coastal watersheds are presented, providing references to the fact that the future of this area of China can be sustainable.