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Identifying and measuring urban spatial structure is a prerequisite for understanding urban spatial characteristics, formulating urban development strategies and testing urban planning results. As important data sources that can visually reflect the spatial distribution characteristics of urban socio-economic and other physical elements, urban points of interest (POI) and nighttime light data play an important role in the study of urban spatial structure. In this study, the main urban areas of 36 cities of the Three Northeast Provinces (Heilongjiang Province, Jilin Province, Liaoning Province) were selected as the study area, the POI (6,553,294 points of interest) and nighttime light data from 2010, 2016, and 2022 were chosen as the basic research data, and the methods of point kernel density estimation, data griddedness, and multifactor combination mapping were used to analyze the developmental dynamics of the urban spatial structure. The study demonstrated: (1) The spatial coupling consistency of POI and nighttime light data in the main urban areas of the Three Northeast Provinces was high, and both had good applicability in urban spatial structure research; (2) POI and nighttime light values formed the spatial pattern of "axis + core − periphery" with Shenyang, Dalian, Changchun and Harbin as the core, while the coupling relationship between the POI and nighttime light data identified that the main urban areas in the Three Northeast Provinces presented a centralized agglomeration type, a decentralized grouping type, a belt combination type and a radial expansion type urban spatial structure; (3) From the perspective of changes in the coupling relationship between POI and nighttime light, most of the main urban areas of resource-mature cities, resource-regeneration cities and non-resource cities were affected by the regional development agglomeration and the "T" railway network, and the "high/medium–high/medium" area showed an expanding tendency; most of the main urban areas of resource-decline cities were affected by the lower development potential and the deprivation of economic factors by the surrounding core cities, while the "high/medium–high/medium" areas showed a contracting tendency. The results of the study can provide a scientific basis and theoretical reference for the future adjustment of urban spatial structure, planning and construction as well as resource allocation in the main urban areas of the cities in the Three Northeast Provinces. [ABSTRACT FROM AUTHOR]

AbstractThis paper examines the spatiotemporal inequality in electricity consumption at the subnational level of Bangladesh using nighttime light (NTL) data. The NTL data, sourced from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) for the period from 2013 to 2020, reveals persistent variability in electricity consumption among the districts. Notably, the gap between urban and non-urban areas has widened. While within district inequality (measured by NTL Gini) has declined over time, it remains high in several districts. Convergence analysis confirms that while lagging districts are showing a catching up effect, the sub-districts are diverging among themselves (in terms of mean NTL per capita). Interestingly, the rural sub-districts are converging among themselves despite urban sub-district divergence. The study also identifies regions with significant imbalance between NTL, population, and built-up area density values. These findings have implications for policymakers aiming to ensure electricity for all and reduce inequality. [ABSTRACT FROM AUTHOR]

Nighttime light (NTL) is a valuable data source for understanding urban sprawl and human activities. Radiometric quantification of NTL data can be used to explain the reasons for changes in NTL. We used average annual NTL data from 2013 to 2020 to investigate the magnitude and distribution of NTL change in the Yangtze River Economic Belt (YREB) in China. We found that NTL increased annually, with 93.06% of the area experiencing brightening. The annual NTL in 2020 was 2.42 times higher than that in 2013, and the NTL in the densely populated eastern provinces was 16.17 times higher than that in the west. Therefore, the east-west development of the research area is extremely uneven. Urbanization is often accompanied by dramatic NTL brightening, and the area of cropland being brightened is the largest of all landuse types. Urban development inevitably encroaches on cropland space. To address this issue, future policies should focus on sustainable regional development, for example encouraging urban densification development patterns. This will help to avoid the negative impact of urban expansion on the entire ecosystem. [ABSTRACT FROM AUTHOR]

Predicting freight transportation is crucial since it is often likened to the foundation of society and a pivotal component of its progress. When access to freight data is limited in underdeveloped nations, nighttime light data could serve as a reliable proxy for assessing freight activity. This research aims to assess the reliability of Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light imagery data as an indicator of freight activity, utilizing Iran's county-level road freight transit database. The study incorporates Population (POP), Average Annual Household Income (Al), and Nighttime Light (NL) as independent variables, while the quantity of annual road freight attraction (FA) in each zone serves as the dependent variable. Two techniques, Geographically Weighted Regression (GWR) and Multivariate Linear Regression (MLR), were employed in this study. Compared to the MLR model, the GWR model's R-squared value increased from 0.68 to 0.79, indicating an enhanced model fit. The "F-test" demonstrated that the descriptive contribution of the nighttime light variable was more significant than that of other factors. The results of this study are significant for researchers and policymakers, as forecasting freight plays a crucial role in anticipating future freight traffic demands and effectively distributing transportation resources. [ABSTRACT FROM AUTHOR]

Climate warming enables easier access and operation in the Arctic, fostering industrial and urban development. However, there is no comprehensive pan-Arctic overview of industrial and urban development, which is crucial for the planning of sustainable development of the region. In this study, we utilize satellite-derived artificial light at night (ALAN) data to quantify the hotspots and the development of light-emitting human activity across the Arctic from 1992 to 2013. We find that out of 16.4 million km2 analyzed a total area of 839,710 km2 (5.14%) is lit by human activity with an annual increase of 4.8%. The European Arctic and the oil and gas extraction regions in Russia and Alaska are hotspots of ALAN with up to a third of the land area lit, while the Canadian Arctic remains dark to a large extent. On average, only 15% of lit area in the Arctic contains human settlement, indicating that artificial light is largely attributable to industrial human activity. With this study, we provide a standardized approach to spatially assess human industrial activity across the Arctic, independent from economic data. Our results provide a crucial baseline for sustainable development and conservation planning across the highly vulnerable Arctic region. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Human settlement areas significantly impact the environment, leading to changes in both natural and built environments. Comprehensive information on human settlements, particularly in urban areas, is crucial for effective sustainable development planning. However, urban land use investigations are often limited to two-dimensional building footprint maps, neglecting the three-dimensional aspect of building structures. This paper addresses this issue to contribute to Sustainable Development Goal 11, which focuses on making human settlements inclusive, safe, and sustainable. In this study, Sentinel-1 data are used as the primary source to estimate building heights. One challenge addressed is the issue of multiple backscattering in Sentinel-1's signal, particularly in densely populated areas with high-rise buildings. To mitigate this, firstly, Sentinel-1 data from different directions, orbit paths, and polarizations are utilized. Combining ascending and descending orbits significantly improves estimation accuracy, and incorporating a higher number of paths provides additional information. However, Sentinel-1 data alone are not sufficiently rich at a global scale across different orbits and polarizations. Secondly, to enhance the accuracy further, Sentinel-1 data are corrected using nighttime light data as additional information, which shows promising results in addressing multiple backscattering issues. Finally, a deep learning model is trained to generate building height maps using these features, achieving a mean absolute error of around 2 m and a mean square error of approximately 13. The generalizability of this method is demonstrated in several cities with diverse built-up structures, including London, Berlin, and others. Finally, a building height map of Iran is generated and evaluated against surveyed buildings, showcasing its large-scale mapping capability. [ABSTRACT FROM AUTHOR]

Exploring the impact of urbanization on carbon emissions is crucial for formulating effective emission reduction policies. Using nighttime light data and attribute data from 68 Chinese cities (2002–2021), this paper develops an urban development evaluation system with the entropy method. The Lasso method is employed to select key factors affecting carbon emissions, and hierarchical regression models are utilized to analyze these factors across different city types. The results show the following: (1) The extraction of built-up areas using integrated nighttime light data yields an overall accuracy ranging from 70.90% to 98.87%, reflecting high precision. (2) Expanding cities have predominated over the past two decades, indicating a continued upward trend in urbanization in China. (3) Urban development is influenced by internal characteristics and geographic location: contracting cities are mainly inland heavy industrial centers, while expanding cities are located in economically advanced coastal regions. Additionally, it is also impacted by the growth of surrounding cities, exemplified by the imbalance between central cities and their peripheries within metropolitan areas. (4) The expansion of built-up areas is a significant factor affecting carbon emissions across all city types. For expanding cities, managing population growth and promoting tertiary sector development are recommended, while contracting cities should focus on judicious economic planning and virescence area protection. [ABSTRACT FROM AUTHOR]

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The COVID-19 outbreak directly and severely threatens global public health. Non-drug interventions in response to the COVID-19 pandemic have significantly altered urban socioeconomic activity. Understanding the different levels of city resilience to the impact of COVID-19 on urban human activities is essential. In this paper, 35 large cities in China were selected as research areas, and based on NPP-VIIRS night light images, the spatial pattern changes in human activities during the epidemic period from the end of December 2019 to December 2022 were explored. The results are as follows: (1) In the first two months of the epidemic, the luminous value of large cities showed an extensive range of decline, and the decline in different urban functional places was different. (2) There is a significant positive correlation between the urban population and the luminous change value. The closer the relationship between urban places and human activities, the stronger the correlation between the population and the luminous change value of urban places. (3) In the middle and later stages of the epidemic, the night light value of all cities showed an upward trend, but there was a difference. (4) The increase in the number of confirmed cases in the middle and later stages of the epidemic could hardly lead to a significant decrease in the value of night light on a monthly scale unless the city had a relatively large area and a relatively strict lockdown policy in that month. This study will help inform future strategies and decisions to effectively combat epidemics and the construction of resilient cities. [ABSTRACT FROM AUTHOR]

Abstract The COVID-19 pandemic has highlighted the importance of understanding environmental factors in disease transmission. This study aims to explore the spatial association between nighttime light (NTL) from satellite imagery and COVID-19 mortality. It particularly examines how NTL serves as a pragmatic proxy to estimate human interaction in illuminated nocturnal area, thereby impacting viral transmission dynamics to neighboring areas, which is defined as spillover effect. Analyzing 43,199 COVID-19 deaths from national mortality data during January 2020 and October 2022, satellite-derived NTL data, and various environmental and socio-demographic covariates, we employed the Spatial Durbin Error Model to estimate the direct and indirect effect of NTL on COVID-19 mortality. Higher NTL was initially directly linked to increased COVID-19 mortality but this association diminished over time. The spillover effect also changed: during the early 3rd wave (December 2020 – February 2021), a unit (nanoWatts/sr/cm2) increase in NTL led to a 7.9% increase in neighboring area mortality (p = 0.013). In contrast, in the later 7th wave (July – September 2022), dominated by Omicron, a unit increase in NTL resulted in an 8.9% decrease in mortality in neighboring areas (p = 0.029). The shift from a positive to a negative spillover effect indicates a change in infection dynamics during the pandemic. The study provided a novel approach to assess nighttime human activity and its influence on disease transmission, offering insights for public health strategies utilizing satellite imagery, particularly when direct data collection is impractical while the collection from space is readily available.

[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.

This study investigates the trends in chronic malnutrition (stunting) among young children across Bangladesh's 64 districts and 544 sub-districts from 2000 to 2018. We utilized remote-sensed data–nighttime light intensity to indicate urbanization, and environmental factors like precipitation and vegetation levels–to examine patterns of stunting. Our primary data source was the Bangladesh Demographic and Health Survey, conducted six times within the study period. Using Bayesian multilevel time-series models, we integrated cross-sectional, temporal, and spatial data to estimate stunting rates for years not covered by the direct survey information. This approach, enhanced by remote-sensed data, allowed for greater prediction accuracy by incorporating information from neighboring areas. Our findings show a significant reduction in national stunting rates, from nearly 50% in 2000 to about 30% in 2018. Despite this overall progress, some districts have consistently high levels of stunting, while others show fluctuating levels. Our model gives more precise sub-district estimates than previous methods, which were limited by data gaps. The study highlights Bangladesh's advancements in reducing child stunting, highlighting the value of integrating remote-sensed data for more precise and credible analysis. [ABSTRACT FROM AUTHOR]

Context: Artificial nighttime lights can adversely impact animal behavior, particularly in nocturnal species. Evidence shows that bats can become inactive due to night lights and may avoid trees bathed in such light, consequently decreasing nocturnal pollination frequency. Although numerous biological studies have been conducted to understand the alteration in bat behavior due to artificial night lights, mapping the intrusion of nighttime light into bat habitats remains largely unexplored. Objective: In this study, we aim to visualize the exposure likelihood of bat habitats to nighttime light in the United States over the past 30 years. Methods: We propose a novel method known as the "Emerging Bivariate Cluster" which has the potential to pinpoint locations experiencing the most significant exposure likelihood of bat habitats to nighttime light over time. Results: Our results show that the low count nighttime light value is decreasing for 24.9% of bat habitats in the contiguous United States. Additionally, 1.4% of bat habitats are likely to experience persistent exposure over the years. Our analysis reveals significant disparities in hotspot intervention patterns across different regions, highlighting the probable areas where light pollution is escalating. Conclusion: The insights gleaned from this study illuminate critical areas for future research and conservation efforts, pinpointing specific locations where the impact of artificial nighttime lighting on bat habitats is most likely to be pronounced. By highlighting these areas, our findings offer a roadmap for biological scientists seeking to delve deeper into the effects of light pollution on nocturnal wildlife. Moreover, the identification of these key exposure likelihood zones provides a valuable foundation for developing targeted mitigation strategies, aiming to preserve bat populations and maintain ecological balance. This research not only contributes to our understanding of the spatial–temporal patterns of light pollution but also emphasizes the necessity of implementing informed conservation practices to safeguard the natural nocturnal environment. [ABSTRACT FROM AUTHOR]

Nighttime light (NTL) remote sensing has emerged as a powerful tool in various fields such as urban expansion, socio-economic estimation, light pollution, and energy domains. However, current annual NTL products suffer from several critical limitations, including poor consistency, severe background noise, and limited comparability. These issues have significantly interfered with the research of long-term NTL trends and diminished the accuracy of related findings. Therefore, this study developed a de-difference smoothing algorithm for producing high-quality annual NTL products based on monthly National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data. It enabled the construction of a continuous global high-quality NTL dataset, named the De-Difference Smoothed Nighttime Light (DDSNL), covering the period from 2012 to 2023. Comparative analyses were conducted to validate the accuracy and availability of the DDSNL product against the benchmark EOG NPP-VIIRS and NPP-VIIRS-like NTL datasets. The results showed that DDSNL products had strong correlation with the NTL distribution of EOG NPP-VIIRS, but little correlation with NPP-VIIRS-like. Notably, DDSNL demonstrated better background noise reduction and higher separability between NTL and non-NTL areas compared to EOG NPP-VIIRS NTL. In contrast to the complete exclusion of background in NPP-VIIRS-Like, the retention of background values in DDSNL leads to more reasonable representation in the urban fringes. In the analysis of NTL changes matching impervious surface changes, the DDSNL product demonstrated the least interference from noise, resulting in the smallest segmentation threshold and the highest matching accuracy. This indirectly demonstrates the spatial and temporal consistency of the annual DDSNL product, ensuring its reliability in change detection-related studies. The annual DDSNL product developed in this research exhibits high fidelity, strong consistency, and improved comparability, and can provide reliable data reference for applications in electrification and urban studies. [ABSTRACT FROM AUTHOR]

The accumulation of artificially built environment stock during urbanization processes has been actively involved in altering the material and energy use pattern of human societies. Therefore, an accurate assessment of built environment stock can provide insights for decision makers to implement appropriate environmentally sustainable retrofitting strategies. This study presents a building stock estimation enhancement framework (BSEEF) that leverages nighttime light (NTL) to accurately assess and spatially map building stocks. By innovatively integrating a region classification module with a hybrid region-specified self-optimization module, BSEEF adaptively enhances the estimation accuracy across diverse urban landscapes. A comparative case study of Japan demonstrated that BSEEF significantly outperformed a traditional linear regression model, with improvements ranging from 1.81% to 16.75% across different metrics used for assessment, providing more accurate building stock estimates. BSEEF enhances environment/sustainability studies by enabling precise spatial analysis of built environment stocks, offering a versatile and robust framework that adapts to technological changes and achieves superior accuracy without extensive reliance on complex datasets. These advances will make BSEEF an indispensable tool in strategic planning for urban development, promoting sustainable and resilient communities globally. [ABSTRACT FROM AUTHOR]

The gut microbiome has been reported to be associated with nighttime light (NTL) exposure and temperament. However, the specific role of infant gut microbiome plays in NTL exposure and temperament is unclear. This study investigated the potential mediating role of infants' gut microbiome in correlations between NTL exposure and temperament. Demographic information, stool samples, and temperament scores were collected from 40 infants. Temperament was evaluated using the Infants Behavior Questionnaire-Revised (IBQ-R). The gut microbiota was analyzed using 16S rRNA sequencing. Cumulative and lagged effects of NTL exposure were calculated based on residential address (NTLpoint) and a concentric 1 km radius buffer zone around the address (NTL1000m), respectively. Mediation models were utilized for assessing the mediating effects of the gut microbiome. The gut microbiome of infants with higher fear scores was characterized by a higher abundance of Akkermansia and Clostridium_sensu_stricto_1 and a lower abundance of Bacteroides. Mediation models indicated Akkermansia played a full mediating role in associations between NTLpoint, NTL1000m and fear in specific time periods. Genus Akkermansia explained 24.46% and 33.50% of associations between fear and cumulative exposure to NTLpoint and NTL1000m, respectively. This study provides evidence for the mediating role of Akkermansia between NTL exposure and fear. However, further experimental is required to elucidate the mechanisms through which the gut microbiome mediates between NTL exposure and temperament in infants. [ABSTRACT FROM AUTHOR]

Urban flood vulnerability monitoring requires a large amount of socioeconomic and environmental data collected at regular time intervals. However, collecting such a large volume of data poses a significant constraint in assessing changes in flood vulnerability. This study proposed a novel method to monitor spatiotemporal changes in urban flood vulnerability from satellite nighttime light (NTL) data. Peninsular Malaysia was chosen as the research region as floods are the most devastating and recurrent phenomena in the region. The study developed a flood vulnerability index (FVI) based on socioeconomic and environmental data from a single year. This FVI was then linked to NTL data using an Adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. The model was calibrated and validated with administrative unit scale data and subsequently used to predict FVI at a spatial resolution of 10 km for 2000–2018 using NTL data. Finally, changes in estimated FVI at different grid points were evaluated using the Mann-Kendall trend method to determine changes in flood vulnerability over time and space. Results showed a nonlinear relationship between NTL and flood vulnerability factors such as population density, Gini coefficient, and percentage of foreign nationals. The ANFIS technique performed well in estimating FVI from NTL data with a normalized root-mean-square error of 0.68 and Kling-Gupta Efficiency of 0.73. The FVI revealed a high vulnerability in the urbanized western coastal region (FVI ∼ 0.5 to 0.54), which matches well with major contributing regions to flood losses in Peninsular Malaysia. Trend assessment showed a significant increase in flood vulnerability in the study area from 2000 to 2018. The spatial distribution of the trend indicated an increase in FVI in the urbanized coastal plains, particularly in rapidly developing western and southern urban regions. The results indicate the potential of the technique in urban flood vulnerability assessment using freely available satellite NTL data. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: China implemented some of the strictest COVID‐19 control measures globally, yet research on large‐scale city lockdowns in China remains limited. Zhengzhou, being one of China's major cities with stringent control measures and a global hub for electronic goods processing and production, has seen significant impacts on the global electronic goods supply chain due to these measures. Our research offers a new approach to assessing the impact of pandemic transmission in urban systems, providing valuable insights from a geographic spatial perspective. The utilization of nighttime light data highlights how computer‐based data analysis enhances our understanding of the spatial scope and dynamics of large‐scale pandemics in urban areas. Key Points: Nighttime light in the central area of Zhengzhou decreased by at least 18%The reduction in nighttime light was observed within a 15‐km radius of the central areaLockdown measures significantly impacted economic activities in Zhengzhou [ABSTRACT FROM AUTHOR]

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.

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.

Impervious surface extraction is essential for environmental management and urban studies. Although nighttime light data have been widely used for this purpose, few studies have utilized the high-resolution (21 m) Qimingxing-1 data released in 2022, which offer significantly higher spatial resolution compared to the widely used NPP-VIIRS product (750 m). In this study, the effectiveness of Qimingxing-1 nighttime light data for impervious surface extraction was investigated for the first time using the highly urbanized city of Dongguan as an example. We combined nighttime light data with POI data to create a ‘Nighttime-POI’ (NP) index and extracted impervious surfaces from four datasets (Qimingxing-1, NPP-VIIRS, QM_NP, and NPP_NP) using the thresholding method. Comparisons with reference data from Landsat 8 and Sentinel-2 revealed that the Qimingxing-1 data improved extraction accuracy by 2.7% over NPP-VIIRS. The Qimingxing-1 data could more accurately reflect the spatial distribution of impervious surfaces and imperviousness degree. Additionally, combining nighttime light data with POI data further enhanced accuracy by 2–3%, resulting in more accurate boundaries and detailed information on impervious surfaces. In conclusion, the Qimingxing-1 data are highly advantageous for impervious surface extraction. The high-precision impervious surface results can assist the government in promptly comprehending the dynamics of urban development.

Accurate population mapping is crucial for disaster management, urban planning, etc. However, current methods using nighttime light (NTL) and gridded population datasets are limited by low spatial resolution and insufficient training data for complex models such as deep learning. These models do not adequately utilize spatial information in population mapping. To address these limitations, this study proposes a high-resolution population mapping method using the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) glimmer imager data and deep learning. The method includes a sample generation strategy with multiple regression and multilevel screening to provide sufficient, high-quality samples for deep learning. A Fine Population mapping network (FinePop-net) is also developed to train regression models using image samples, capturing multi-scale features for model training. When applied to the Guangdong-Hong Kong-Macao Greater Bay Area with 40-meter resolution SDGSAT 1 glimmer imagery, the method significantly reduced the average absolute error and root-mean-square error by 9.35% and 11.44%, respectively, compared with those of the pixel-level learning methods. It also outperformed other population spatialization datasets and NTL data by over 30% and 10%, respectively, in terms of error reduction. The results highlight the method’s effectiveness and the value of SDGSAT-1 glimmer imagery for fine population spatialization.

ABSTRACTThe Central Business Districts (CBDs) are important hubs for urban economic activities. In the context of globalization, a unified CBD boundary would greatly facilitate the study of global CBD comparative analysis, urban socio-economic development, urban transport and commuting mode. However, previous studies have encountered challenges in developing a practical method for global CBD identification due to limitations in data sources and methodologies. In this study, we selected 32 global megacities as research objects and used the open-access Black Marble nighttime light (NTL) products to construct indicators with the intensity and angular effects of NTL. Clustering and decision tree were then employed to derive rules for CBD identification. Results show that combining Z-score indicators and the strategy of clustering 32 cities before decision tree classification could improve the accuracy of CBD identification, which achieved a producer accuracy of 85%. The 32 cities were clustered into three types, i.e. U.S.A.-like, the mixed type, and China-like. The rules for CBD identification became more complex in the above order, but the accuracy decreased in turn. This study provides a new CBD identification method for cities lacking reference data, allowing for the delineation of unified and comparable CBD boundaries on a large scale.

ABSTRACTUnderstanding the urbanization and transformation trajectories of resource-based cities (RBCs) is pivotal for China’s sustainable development goals. This study introduces a novel regression-based algorithm for assessing urbanization patterns. We delineated the urban boundaries of 335 cities and employed time-series nighttime light data from 2001 to 2020, shifting the analysis from a pixel-scale to an urban-scale perspective. Our analysis reveals distinct disparities within the 125 RBCs when compared to the national average, leading to their categorization. The key findings include: (1) Within Forest and Coal RBCs, numerous areas have stabilized after experiencing contraction. Geographically, a significant number of RBCs in the Northeast, North, and Northwest regions are experiencing or have experienced substantial urban shrinkage. The developmental status of RBCs exhibits a spatial positive correlation. (2) Although the government categorized RBCs based on their development level in 2013, findings suggest that this classification may no longer accurately reflect the current development status of RBCs in the context of urbanization. (3) Utilizing spectral clustering, we categorized RBCs into five types, identified 10 RBCs undergoing shrinkage and 30 cities trending towards stabilization post-shrinkage. This research offers a refined evaluative method for urbanization, providing insights beneficial for policy-making concerning RBCs’ sustainable growth..

ABSTRACTDue to the difficulty of obtaining survey data, nighttime light (NTL) imagery has emerged as valuable alternative data for asset wealth estimation. However, the nighttime light values do not differentiate between levels of asset wealth in various unlit areas, as the nighttime light values in unlit areas are all 0. Here, NTL data and World Settlement Footprint (WSF) data were combined to extract multidimensional luminous features that are also differential in unlit areas to estimate asset wealth in nighttime light-poor areas at 500 m × 500 m spatial units. A random forest model was used to estimate asset wealth, based on the shortest distance of settlements to three categories of lighted areas, along with the brightness values derived from the nearest lighted area and the settlements themselves. This model achieved an explanation of 71% for the variation in settlement asset wealth and demonstrated effectiveness in estimating the asset wealth of unlit areas. The MAE and RMSE of asset wealth estimation in the unlit clusters were 4.03 and 5.28, respectively. Asset wealth is generally low across most African settlements, with clear two-tier differentiation in Africa. In summary, the proposed method can extensively explore the luminous information in unlit areas.

This article examined the causal relationship between economic and innovation performance and the associated lag time within 282 prefecture-level cities in China. With the nighttime light (NTL) intensity representing economic status and 19 indicators comprehensively simulating the innovation performance index (CII), the Granger causality test was applied. The results showed that the temporal lag between economic status and innovation performance varies among cities. The innovation performance of 145 cities showed significant causal relationships with economic development in the ItoN test, and the average time lag was 2.01 years. The economic development of 137 cities also showed significance in innovation performance, with an average time lag of 3.15 years in the NtoI test. The growth rate of economic development and innovation performance has strongly impacted the temporal lag, especially in the ItoN causality test. A bidirectional causality between economic and innovation performance was found in 136 cities, but these cities complete the 'economy-innovation-economy' circle over a long period. Overall, this study concludes that the Granger causality test offers a useful approach to measure the time lag between economic and innovation performance, which can help better implement policies and expand research on economy and innovation. [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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study examines the changes in Dodoma between 2014 and 2021 using Landsat and night-time light (NTL) data. The results indicate that the built-up areas have grown by 47.1 km² between 2014 and 2021. The change point detection results of NTL time series data show that at one period (location 43) of the time series, the probability of a change is very high (> 0.70). This location corresponds to the year 2017. The Bayesian structural time series model results show that the relocation to Dodoma increased NTL by a statistically significant 81% (p < 0.001) from 2017. [ABSTRACT FROM AUTHOR]

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 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 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 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.

A major source of carbon dioxide emissions (CO2) arises from the household sector. Recent studies have reported increasing household CO2 emissions (HCO2) in many countries. Cities represent a key administrative level in China and can be managed to mitigate HCO2 if spatial and temporal variations in HCO2 are understood at fine scales. Here, we applied panel data analysis to map HCO2 at a pixel scale of 1 km in China using remotely sensed time series nighttime light data, grid population density data, and provincial energy consumption statistics from 2000 to 2020. Spatial and temporal variations in HCO2 were observed with four growth modes, including high growth (HG), low growth (LG), negative growth (NG), and high negative growth (HNG), for different periods, i.e., 2000–2010, 2010–2020, and 2000–2020. We proposed a local scale analysis of HCO2 growth patterns within a global context to assess the contribution roles of 372 municipal cities to the changes in the national total HCO2 (T-HCO2). The results indicated that T-HCO2 has tripled in the last two decades, but the roles of the contribution to the increase varied among cities. The local scale analysis revealed that more cities contributed to the rise in T-HCO2 through HG and LG than those that suppressed it through NG and HNG. The majority of the cities displayed contributions to the rise in T-HCO2 through two or more of the growth modes, confirming a significant variation in HCO2 across locations, even within a city. This study provides a new approach to understanding the roles cities play in the long-term dynamics of T-HCO2. We recommend increased efforts to encourage HCO2 mitigation in cities that have contributed to the rise in T-HCO2 to help neutralize carbon emissions at the national level.

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.

Human settlement areas significantly impact the environment, leading to changes in both natural and built environments. Comprehensive information on human settlements, particularly in urban areas, is crucial for effective sustainable development planning. However, urban land use investigations are often limited to two-dimensional building footprint maps, neglecting the three-dimensional aspect of building structures. This paper addresses this issue to contribute to Sustainable Development Goal 11, which focuses on making human settlements inclusive, safe, and sustainable. In this study, Sentinel-1 data are used as the primary source to estimate building heights. One challenge addressed is the issue of multiple backscattering in Sentinel-1’s signal, particularly in densely populated areas with high-rise buildings. To mitigate this, firstly, Sentinel-1 data from different directions, orbit paths, and polarizations are utilized. Combining ascending and descending orbits significantly improves estimation accuracy, and incorporating a higher number of paths provides additional information. However, Sentinel-1 data alone are not sufficiently rich at a global scale across different orbits and polarizations. Secondly, to enhance the accuracy further, Sentinel-1 data are corrected using nighttime light data as additional information, which shows promising results in addressing multiple backscattering issues. Finally, a deep learning model is trained to generate building height maps using these features, achieving a mean absolute error of around 2 m and a mean square error of approximately 13. The generalizability of this method is demonstrated in several cities with diverse built-up structures, including London, Berlin, and others. Finally, a building height map of Iran is generated and evaluated against surveyed buildings, showcasing its large-scale mapping capability.

Exploring the impact of urbanization on carbon emissions is crucial for formulating effective emission reduction policies. Using nighttime light data and attribute data from 68 Chinese cities (2002–2021), this paper develops an urban development evaluation system with the entropy method. The Lasso method is employed to select key factors affecting carbon emissions, and hierarchical regression models are utilized to analyze these factors across different city types. The results show the following: (1) The extraction of built-up areas using integrated nighttime light data yields an overall accuracy ranging from 70.90% to 98.87%, reflecting high precision. (2) Expanding cities have predominated over the past two decades, indicating a continued upward trend in urbanization in China. (3) Urban development is influenced by internal characteristics and geographic location: contracting cities are mainly inland heavy industrial centers, while expanding cities are located in economically advanced coastal regions. Additionally, it is also impacted by the growth of surrounding cities, exemplified by the imbalance between central cities and their peripheries within metropolitan areas. (4) The expansion of built-up areas is a significant factor affecting carbon emissions across all city types. For expanding cities, managing population growth and promoting tertiary sector development are recommended, while contracting cities should focus on judicious economic planning and virescence area protection.

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]

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]

Nighttime light (NTL) remote sensing has emerged as a powerful tool in various fields such as urban expansion, socio-economic estimation, light pollution, and energy domains. However, current annual NTL products suffer from several critical limitations, including poor consistency, severe background noise, and limited comparability. These issues have significantly interfered with the research of long-term NTL trends and diminished the accuracy of related findings. Therefore, this study developed a de-difference smoothing algorithm for producing high-quality annual NTL products based on monthly National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data. It enabled the construction of a continuous global high-quality NTL dataset, named the De-Difference Smoothed Nighttime Light (DDSNL), covering the period from 2012 to 2023. Comparative analyses were conducted to validate the accuracy and availability of the DDSNL product against the benchmark EOG NPP-VIIRS and NPP-VIIRS-like NTL datasets. The results showed that DDSNL products had strong correlation with the NTL distribution of EOG NPP-VIIRS, but little correlation with NPP-VIIRS-like. Notably, DDSNL demonstrated better background noise reduction and higher separability between NTL and non-NTL areas compared to EOG NPP-VIIRS NTL. In contrast to the complete exclusion of background in NPP-VIIRS-Like, the retention of background values in DDSNL leads to more reasonable representation in the urban fringes. In the analysis of NTL changes matching impervious surface changes, the DDSNL product demonstrated the least interference from noise, resulting in the smallest segmentation threshold and the highest matching accuracy. This indirectly demonstrates the spatial and temporal consistency of the annual DDSNL product, ensuring its reliability in change detection-related studies. The annual DDSNL product developed in this research exhibits high fidelity, strong consistency, and improved comparability, and can provide reliable data reference for applications in electrification and urban studies.

The accumulation of artificially built environment stock during urbanization processes has been actively involved in altering the material and energy use pattern of human societies. Therefore, an accurate assessment of built environment stock can provide insights for decision makers to implement appropriate environmentally sustainable retrofitting strategies. This study presents a building stock estimation enhancement framework (BSEEF) that leverages nighttime light (NTL) to accurately assess and spatially map building stocks. By innovatively integrating a region classification module with a hybrid region-specified self-optimization module, BSEEF adaptively enhances the estimation accuracy across diverse urban landscapes. A comparative case study of Japan demonstrated that BSEEF significantly outperformed a traditional linear regression model, with improvements ranging from 1.81% to 16.75% across different metrics used for assessment, providing more accurate building stock estimates. BSEEF enhances environment/sustainability studies by enabling precise spatial analysis of built environment stocks, offering a versatile and robust framework that adapts to technological changes and achieves superior accuracy without extensive reliance on complex datasets. These advances will make BSEEF an indispensable tool in strategic planning for urban development, promoting sustainable and resilient communities globally.

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.

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.