Nighttime Light Index Research Articles

Estimation of gridded anthropogenic heat flux at the optimal scale by integrating SDGSAT-1 nighttime lights and geospatial data

Diverse human activities in megaregions have generated excellent anthropogenic heat (AH), which disrupts the urban energy balance and affects the urban climate. However, few studies have investigated the spatial scale effect (SSE) of estimating the multi-source AH flux (QF). Hence, this study proposes a method, i.e., a nighttime light index (NTLI)-based top-down inventory model, to investigate the optimal spatial scale (So) for estimating multi-source gridded QF at the county level and mapping gridded products for the central region of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) megaregion in 2018. The method combines the multi-scale (10–500 m) enhanced NTLI with a top-down inventory model. We generated the enhanced NTLI by integrating the Sustainable Development Science Satellite 1 (SDGSAT-1) nighttime lights (NTL), points of interest, and road network data. Then we analyzed the SSE of QF estimation to determine the So and evaluated the accuracy and characteristics of multi-source QF at the So. The results showed that (1) the So of estimation for six heat components varied between 10 m and 450 m, which related to the difference in energy source and spatial pattern; (2) considering the estimation accuracy, numerical characteristics, and spatial detail of critical urban features, the So of QF was 300 m; (3) the proposed method reduced the estimation error of QF more effectively than the NTL-based inventory model. The root mean square error (RMSE) decreased by 2.45–21.20 %, and the goodness of fit (R2) increased by 2.17–13.66 % among six heat components; and (4) our multi-source QF product at 300 m outperformed previous QF products in spatial heterogeneity and numerical accuracy. This study first explored the SSE of QF estimation and captured the spatial and numerical information of multi-source QF at the So, which could provide valuable knowledge for urban micro-climate.

Analysis of the spatiotemporal expansion and pattern evolution of urban areas in Anhui Province, China, based on nighttime light data

Based on remote sensing images from National Polar-orbiting Partnership—Visible Infrared Imaging Radiometer Suite–like data and related statistical yearbook data acquired at 5-year intervals from 2000 to 2020, the urbanized areas of cities in Anhui Province, China, were extracted. The characteristics of their spatial and temporal expansion were analyzed in combination with the migration path of the urban center of gravity along with measures of the speed and intensity of urban expansion. In addition, the urban development trends in Anhui were evaluated by combining a nighttime light index with spatial autocorrelation and local autocorrelation methods to reveal the characteristics of the level of urban development and internal structural changes in each city in Anhui. The results show that the cities in Anhui are concentrated in two belt areas in the Yangtze and Huai river basins, and that they expanded outward, with several original urbanized areas as the center. The rate of urban expansion reached its lowest point during 2005–2010 and peaked during 2015–2020. The urbanized areas in Anhui were distributed in a northwest–southeast direction, while the center of urban gravity remained in Hefei City during the study period. The total amount of nighttime light in Anhui’s urbanized areas increased by nearly 10 times over 20 years, with the period of 2010–2020 experiencing particularly high increases in nighttime light. The grayness values of urban lights in each period show clear spatial correlation.

The estimation of building carbon emission using nighttime light images: A comparative study at various spatial scales

As one of the fundamental sectors to measure the carbon emission levels in a certain region, building carbon emission plays an important role in determining low-carbon development plans. Most of the carbon emission estimation research mainly focuses on the establishment of bottom-up GHG inventory and the implication of policy-driven approaches, there are still many theoretical gaps in the usage of remote sensing data to predict building carbon emission. This paper presents a comprehensive study to discuss the performance of different regression models using various open nighttime light (NTL) data sources. The Noord Brabant province was employed as a case study to verify the feasibility of using different estimation models at various spatial scales (city-level, district-level, and neighborhood-level). Among all regression models, the geographically weighted regression (GWR) has been proven to better reflect the relationship between building carbon emissions and the NTL index. For practical applications, the carbon intensity (CI) and annual nighttime light index (ANLI) are a pair of optimal sets to establish a reliable estimation model. It exhibits higher utility value at the city-level due to the fewer interferences caused by non-building lighting sources. The results of this comparative study provide a new reference to support the establishment of carbon inventory. By illustrating the differences among various estimation models, the applicable scope of using open remote sensing data to estimate building carbon emissions can be further defined. The conclusion may provide more detailed instructions during the process of developing low-carbon cities.

Study on the substitutability of nighttime light data for SDG indicators: a case study of Yunnan Province

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.

Spatiotemporal variation and driving factor of vegetation coverage from 2000 to 2020 in southern Jiangxi Province, China.

Vegetation plays a critical role in the water and carbon cycling and energy flow, serving as an indicator for regulating land carbon balance and reflecting climate change and human activities. We analyzed the spatiotemporal variations of normalized difference vegetation index (NDVI) during the growing season in southern Jiangxi from 2000 to 2020, using statistical methods, including the Mann-Kendall test, Theil-Sen Median analysis, Hurst index, and coefficient of variation. We employed the geodetector model to comprehensively assess the impacts of climate, topography, soil and human factors on spatial differentiation of vegetation NDVI. The results showed NDVI exhibited an upward fluctuating trend with a rate of 0.003 per year from 2000 to 2020. The proportion of high-grade and medium-high-grade NDVI areas were 55.8% and 41.9%, respectively, while the areas with low and relatively low fluctuations accounted for 92.3%. The proportions of areas showing extremely significant improvement and significant improvement were 40.4% and 19.4%, respectively. In contrast, the combined proportion of areas displaying extremely significant degradation and significant degradation was only 2.2%. The proportions of areas demonstrating continuous improvement and future improvement were 28.0% and 60.2%, respectively. Elevation, precipitation, relative humidity, temperature, landform type, land use type, population density, and nighttime light were identified as the major factors for the vairations of NDVI in the study area, followed by slope, soil type, and GDP, while slope aspect and vegetation type had indirect influence. Throughout the study period, NDVI in southern Jiangxi was overall stable, with future changes primarily indicating improvement. Notably, human factors such as land use type, population density, and nighttime light index exhibited an upward trend in their impacts on NDVI.

Spatiotemporal Pattern and Driving Factors of Carbon Emissions in Guangxi Based on Geographic Detectors

Analysis of the spatiotemporal distribution pattern and driving factors of carbon emissions has been a focus of research in recent years. However, at the county level, analyses of the driving factors of carbon emissions are still relatively few. This study selected the Guangxi Zhuang Autonomous Region as the research subject, selecting the normalized difference vegetation index (NDVI), nighttime light index (NLI), gross domestic product (GDP), and population density (POP) as driving factors. Based on the geographic detector method, the spatiotemporal distribution pattern and driving factors of carbon emissions at the county level in Guangxi were investigated. The results show the following: (1) There are significant regional differences in the degree of change in carbon emissions. From 2005 to 2020, the total carbon emissions in Guangxi show an upward trend, presenting a “high in the south and low in the north” distribution characteristic, gradually forming a high-level region in the capital city of Nanning, the city of Liuzhou, and some coastal cities (such as the cities of Qinzhou, Beihai, and Fangchenggang) as the core of carbon emissions; (2) NDVI, NLI, GDP, and POP have a relatively high impact on the carbon emission pattern in Guangxi, and the impact of human activity intensity on carbon emissions is higher than that of the influencing factors of NDVI; (3) The interaction between NDVI, NLI, GDP, and POP has a significant impact on the carbon emission pattern. The aforementioned results can provide decision-making suggestions for the social and economic development of Guangxi, as well as the formulation of carbon sequestration policies.

Coupling coordination between the ecological environment and urbanization in the middle reaches of the Yangtze River urban agglomeration

The rapid development of urban agglomerations has endangered the eco-environment. It is critical to research the coupling coordination between the eco-environment and urbanization to promote regional sustainable development. The aim of this study is to investigate the coupling coordination degree (CCD) between the eco-environment and urbanization in the MRYRUA and to identify the key factors influencing the CCD. Based on the remote sensing perspective, this study used the remote sensing ecological index (RSEI) and comprehensive nighttime light index (CNLI) to measure the eco-environment and urbanization level of the middle reaches of the Yangtze River urban agglomeration (MRYRUA). Then, based on the CCD model, the relationship between the eco-environment and urbanization was evaluated. The geographical detector was utilized to analyze the key factors. The results showed the following: (1) both the eco-environment and urbanization of the MRYRUA show a fluctuating upward trend, and the change rate of urbanization is greater than that of the eco-environment. (2) The overall coupling level of the MRYRUA changes from moderate unbalanced coupling to moderate balanced coupling, and the distribution of the CCD presents spatial imbalance. The coupling level of the central region is higher than that of the peripheral cities, and the coupling level of the provincial capital cities is higher than that of other cities. (3) The explanatory power of socioeconomic factors in the CCD is higher than that of natural factors in the single-factor detection; urban green space (UGS) plays the most significant role in the interaction detection. (4) RSEI has a negative correlation with the CCD, while the CNLI has a positive correlation with the CCD. The development of urbanization has played a decisive role in the CCD. The results provide a scientific foundation on which the MRYRUA can achieve sustainable urban development.

Exploring the impact of urbanization on ecological quality in the middle reaches of the Yangtze River Urban Agglomerations, China

Rapid urbanization in urban agglomeration areas puts tremendous pressure on the eco-environment, and balancing urbanization development and ecological quality protection is crucial for regional high-quality development. However, current research lacks a multi-scale assessment of ecological quality and urbanization in urban agglomeration areas. Here, we assessed the multi-scale ecological quality and urbanization level of the Middle Reaches of the Yangtze River Urban Agglomerations (MRYRUA) from 2001 to 2021 based on multi-source remote sensing imagery by constructing the remote sensing-based ecological index (RSEI) and the comprehensive nighttime light index (CNLI), respectively, and analyzed the coupling coordination degree (CCD) of the two using the CCD model. The following conclusions were obtained: (1) There are spatial differences in the ecological quality of pixel, county, and municipal scales. Multi-dimensional analysis indicates that the RSEI shows an increasing trend, and the ecological quality of the MRYRUA has generally improved in the past 20 years. (2) The DN values of nighttime light and the three light indices have increased to varying degrees at different scales, among which the average value of CNLI has increased from 0.02 in 2000 to 0.12 in 2021. The urbanization level of the MRYRUA is constantly improving, but there is an obvious development imbalance. (3) The ecological quality and urbanization level developed in a more coordinated direction at different scales, but the urbanization level lagged behind ecological quality as a whole, and the two were negatively correlated in space. The results of this study can guide the formulation of ecological protection and high-quality development policies in the MRYRUA.

Spatiotemporal Variation and Quantitative Attribution of Carbon Storage Based on Multiple Satellite Data and a Coupled Model for Jinan City, China

Rapidly predicting and revealing the spatiotemporal characteristics and driving factors of land-use changes in carbon storage within megacities under different scenarios is crucial to achieving sustainable development. In this study, Jinan City (JNC) is taken as the study area, and the Markov-FLUS-InVEST model is utilized to predict and analyze the spatiotemporal variation in carbon storage in 2030 under three scenarios, namely, the natural development scenario (S1), the ecological conservation scenario (S2), and the economic development scenario (S3). The drivers of carbon storage changes were identified using an optimal parameter-based geographic detection (OPGD) model. The findings indicate that (1) land use from 2010 to 2018 shows a trend of continuous expansion of construction land and reduction in arable land. (2) The main types of carbon pools were cropland, forest, and grassland, accounting for more than 96% of the total amount. Carbon storage showed a decreasing trend from 2010 to 2018, and the main type of carbon pool that decreased was cropland. The center of gravity of carbon storage increases and decreases was located in the southern Lixia District, and the center of gravity of increase and decrease moved to the southwest by 3057.48 m and 1478.57 m, respectively. (3) From 2018 to 2030, the reductions in carbon stocks were 3.20 × 106 t (S1), 2.60 × 106 t (S2), and 4.26 × 106 t (S3), and the carbon release was about 9 times (S1), 4 times (S2), and 10 times (S3) that of the carbon sink. (4) The contribution of slope (A2) ∩ nighttime light index (B6) and elevation (A1) ∩ nighttime light index (B6) to the regional heterogeneity of carbon stocks was the largest among the interaction drivers. To sum up, this study deepens the simulation of spatial and temporal dynamics of carbon storage under land-use changes in megacities and the related driving mechanism, which can provide the basis for scientific decision-making for cities to conduct territorial spatial planning and ecological protection and restoration.

Exploring the seasonal relationship between spatial and temporal features of land surface temperature and its potential drivers: the case of Chengdu metropolitan area, China

Global climate change and the process of urbanization have had a significant impact on land surface temperature (LST). This study selects the Chengdu metropolitan area in China as a typical research subject. Based on the seasonal heterogeneity and spatial distribution characteristics of LST, different types of potential influencing factors are selected for Principal Component Analysis (PCA) to determine the categories of these factors. Subsequently, a multiple linear regression analysis is conducted to explore the relationship between LST and the identified potential influencing factors during different seasons. The findings of this study suggest that the regions with high temperatures and secondary high temperatures in the Chengdu metropolitan area are primarily concentrated in Chengdu and its adjacent localities, exhibiting noticeable seasonal variations. In the summer, high-temperature zone and second high-temperature zone of the LST show a central aggregation pattern. In the transition season, the high-temperature zone of the LST presents a “large dispersion, small aggregation” pattern. In the winter, it presents a dispersed pattern. In terms of influencing factors, elevation, slope, wind speed, humidity, and surface vegetation cover related to natural geographical conditions have a significant impact on LST, reaching a peak during the transition season. Factors associated with social and economic conditions, such as population size, nighttime light index, and road density, have a pronounced effect on LST during the summer season. During winter, LST is mainly influenced by landscape pattern-related factors such as Shannon Diversity Index, Edge Density, Largest Patch Index, and Patch Density. This study not only assesses the seasonal and spatial characteristics of LST in the Chengdu metropolitan area but also provides valuable insights for formulating phased measures to mitigate the Urban Heat Island (UHI) in other regions.

Association of Fine Particulate Matter and Residential Greenness With Risk of Pulmonary Tuberculosis Retreatment: Population-Based Retrospective Study.

The evidence on the association of fine particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) with pulmonary tuberculosis (PTB) retreatment is limited. There are no data on whether greenness exposure protects air pollution-related PTB retreatment in patients with prior PTB. In a population-based retrospective study, we aimed to investigate the influence of PM2.5 and residential greenness on the risk of PTB retreatment. A total of 26,482 patients with incident PTB, registered in a mandatory web-based reporting system between 2012 and 2019 in Zhengzhou, China, were included in the analysis. The exposure to PM2.5 was assessed based on the China High Air Pollutants dataset, and the level of greenness was estimated using the Normalized Difference Vegetation Index (NDVI) values. The associations of PTB retreatment with exposure to PM2.5 and greenness were evaluated, respectively, considering the local socioeconomic level indicated by the nighttime light index. Among the 26,482 patients (mean age 46.86, SD 19.52 years) with a median follow-up time of 1523 days per patient, 1542 (5.82%) PTB retreatments were observed between 2012 and 2019. Exposure to PM2.5 was observed to be significantly associated with the increased risk of PTB retreatment in fully adjusted models with a hazard ratio of 1.97 (95% CI 1.34-2.83) per 10 μg/m3 increase in PM2.5. Patients living in the regions with relatively high quartiles of NDVI values had a 45% lower risk of PTB retreatment than those living in the regions with the lowest quartile for the 500 m buffers (hazard ratio 0.55, 95% CI 0.40-0.77). Such a protective effect of residential greenness was more pronounced among patients living in lower nighttime light areas. The strength of the association between PM2.5 exposure and the risk of PTB retreatment was attenuated by greenness. No significant association was observed between NDVI and the incidence of drug resistance. Long-term exposure to PM2.5 might be a risk factor for PTB retreatment, while an increased level of residential greenness was found to be associated with reduced risks of PTB retreatment. Our results suggest strengthening the control of ambient air pollution and improving residential greenness may contribute to the reduction of PTB retreatment.

Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda

Crimean-Congo Hemorrhagic Fever (CCHF) is a viral disease that can infect humans via contact with tick vectors or livestock reservoirs and can cause moderate to severe disease. The first human case of CCHF in Uganda was identified in 2013. To determine the geographic distribution of the CCHF virus (CCHFV), serosampling among herds of livestock was conducted in 28 Uganda districts in 2017. A geostatistical model of CCHF seroprevalence among livestock was developed to incorporate environmental and anthropogenic variables associated with elevated CCHF seroprevalence to predict CCHF seroprevalence on a map of Uganda and estimate the probability that CCHF seroprevalence exceeded 30% at each prediction location. Environmental and anthropogenic variables were also analyzed in separate models to determine the spatially varying drivers of prediction and determine which covariate class resulted in best prediction certainty. Covariates used in the full model included distance to the nearest croplands, average annual change in night-time light index, percent sand soil content, land surface temperature, and enhanced vegetation index. Elevated CCHF seroprevalence occurred in patches throughout the country, being highest in northern Uganda. Environmental covariates drove predicted seroprevalence in the full model more than anthropogenic covariates. Combination of environmental and anthropogenic variables resulted in the best prediction certainty. An understanding of the spatial distribution of CCHF across Uganda and the variables that drove predictions can be used to prioritize specific locations and activities to reduce the risk of future CCHF transmission.

Intensive land-use is associated with development status in port cities of Southeast Asia

Port cities in Southeast Asia (SEA) have been undergoing intensive land use and land cover change (LUCC) because of rapid socio-economic development in recent decades. However, various development statuses among these cities could result in divergent LUCC transitions and therefore affect the local ecosystem at different levels. This study investigated the historic land-use transition in the SEA port cities by classifying Landsat images from 1990 to 2020 with the random forest algorithm, and further integrated these patterns with land-use modeling to inform the future land uses under the current developing mode. DIVISON was used to assess land-use fragmentation and investigated its relationship with development levels represented by the average nighttime light index (ANLI). The results showed an intensive LUCC in SEA port cities in 1990–2020, with artificial surface increasing by 9.2% (175.9 km2) of the total area, mainly converted from cultivated land which decreased by 136.9 km2. An inverted V-shaped relationship between fragmentation and development level was found at the landscape level and for cultivated land, in which both turning points (TPs) occurred in the mid-developed stage (ANLI = 41.1 and 20.0, respectively). Artificial surfaces tended to be more aggregated in later developed stages, showing a TP of ANLI around 53.2. Under the current developing mode, the aggregation trend will be generally continued for the next three decades, mainly resulting from the increased connection in artificial surfaces. The findings also highlighted a tradeoff between city development and agricultural production in SEA port cities, leading to increased food insecurity in more developed stages. Recent developments also negatively affected aqua ecosystems, such as aquaculture, mangroves, and natural waterbodies in several cities. This study underscored the importance of the sustainable LUCC strategy in SEA port cities and provides insights into rationalizing land-use policies for other port cities worldwide.

Spatial and Temporal Changes of Habitat Quality and Its Influential Factors in China Based on the InVEST Model

The loss of biodiversity is one of the three global crises today. How to reduce habitat destruction and overexploitation to protect biodiversity is an urgent issue to be addressed. This study aims to explore the influential factors and driving mechanisms of habitat quality to find ways to reduce the interference of human activities on habitat quality. This paper evaluates the habitat quality in 30 provinces of China from 2010 to 2020 using the InVEST model and studies its geospatial differences by spatial auto-correlation. Then it investigates the influencing factors and driving mechanisms based on Geodetector and proposes strategies to improve habitat quality for different regions. The study shows that first, habitat quality is not distributed homogeneously in Chinese provinces, and habitat quality varies widely among different regions in the structure. Second, factors have different influences on habitat quality, which can be grouped into “key factors” and “auxiliary factors”. Its driving forces vary greatly over time, with per capita water resources, nighttime light index, area of afforested land, forest area, and destructed forest area as key factors in both 2010 and 2020. Third, the factor pairs are all bifactor or non-linear enhanced, showing that two factors have a stronger combined effect on habitat quality than a single factor. In particular, factors such as per capita water resources and area of afforested land in very strong interactions with others. Fourth, corresponding strategies are proposed for different regions in China to improve habitat quality according to the analysis of the spatial inequality of habitat quality and its driving mechanism, providing a reference for relevant regions abroad.

Realistic characteristics and driving mechanisms of pseudo-human settlements in Chinese cities

Pseudo-human settlements (PHS) reflect the subjective wishes and needs of residents. An in-depth characterization of PHS aids in clarifying the actual development needs and future development directions of the city from the residents’ perspective. China is currently undergoing high-speed urbanization and advocates improving the quality of cities. It is particularly important to test the achievements of urbanization through PHS. This study quantitatively analyses spatiotemporal characteristics and the realistic driving mechanism of PHS in 286 Chinese cities through spatial autocorrelation analysis and geographic detectors. The results indicate that (1) the PHS level exhibited notable hierarchical characteristics and spatial differences (high in the eastern coastal regions and low in the western inland regions), with weak positive autocorrelation characteristics; (2) nighttime light index, per capital GRP, and population size are major drivers of heterogeneity of development level and functional structure of PHS; and (3) the functional structure followed an emergence-development-maturity-decline-extinction life cycle. Overall, the development of PHS must play a leading role in high-level areas, and urban decision-makers should clarify and adapt to local preferences of PHS and predict future changes. The findings of this study can help China and other countries promote realistic development by unveiling the characteristics of PHS.

Coupling coordination analysis of urbanization and the ecological environment based on urban functional zones.

Urbanization is an inevitable process in human social progress; additionally, the ecological environment is the carrier and foundation of human social development. Considering central Shanghai, China, as an example, this study quantitatively analyzed the coupling coordination relationship between urbanization and the ecological environment based on urban functional zones; remote sensing images, Open Street Map roads, and point of interest data were analyzed for the urban functional zones via the remote sensing-based ecological index (RSEI), comprehensive nighttime light index (CNLI), and coupling coordination degree (D). The results revealed that urban functional zones in central Shanghai were mainly mixed functional zones and comprehensive functional zones, which formed a spatial structure that gradually radiated outward from the urban core. Additionally, CNLI values were high; the proportion of CNLI between 0.6 and 1 was 94.37%. Moreover, the RSEI showed spatial differentiation; it was low in the center and gradually increased outward. Additionally, D was at the primary coordination level. The coupling coordination type in the core area corresponded to an ecological environment lag, which gradually transitioned to a state of systematic balanced development from the core area outward, but showed sluggish urbanization in some areas. This quantitative analysis of the coupling coordination between urbanization and the ecological environment based on urban functional zones provides effective scientific references for optimization of spatial planning.

Study on Temporal and Spatial Characteristics of Fuzhou Built-Up Area Based on Remote Sensing Data of Nighttime Light

It is of great significance to grasp the spatio-temporal characteristics and expansion mechanism of urban built-up areas for formulating urban development strategy. This paper takes the built-up area of Fuzhou as the study area, uses multi-temporal Landsat images and remote sensing data of nighttime light (NTL) as the main data sources, and extracts the built-up area of NTL images with a higher spatial resolution comparison method. It discusses the development trends of the Fuzhou built-up area from 2000 to 2021 from the perspectives of temporal and spatial evolution characteristics and spatial morphology evolution and analyzes the relationship between population factors, economic factors, natural conditions, policy factors, and urban expansion. The results show that the urbanization level of Fuzhou is gradually improving, and the compounded nighttime light index (CNLI) increases from 0.0105 in 2000 to 0.0635 in 2021. The trend of expansion speed and expansion intensity is consistent, showing the changing trend of first fast and then slow, then accelerating and then slowing down. The expansion direction presents the trend of “expanding eastward, advancing southward and expanding westward”, the spatial form tends to be irregular, and the migration range of the center of gravity is not significant. Population factors, economic factors, and expansion are positively correlated and closely related, and natural conditions and policy guidance affect the direction and mode of the expansion of the built-up area. The above results indicate that the overall urban development of Fuzhou shows an upward trend, which is consistent with the planned urbanization development trend of Fuzhou.

Spatial–temporal evolution and driving force analysis of eco-quality in urban agglomerations in China

Urban agglomerations are important spatial carriers of regional economic development, and their ecological quality (EQ) is closely related to economic growth and human development. However, the rapid urbanization in China has generated a series of EQ problems that threaten the sustainable development of the country. Therefore, it is essential to explore changes in EQ for the development of sustainable “human–land” relations in urban agglomerations. Using GIS, GeoDetector, Stepwise multiple regression, and Sen'trend analysis, to reveal the spatial-temporal evolution of EQ in urban agglomerations along with the spatial heterogeneity of its driving forces in China. Results show that: (1) The annual change rate of EQ of urban agglomerations ranges from −0.0312 to 0.0334. Taking the Hu-line as a boundary, the EQ of urban agglomerations is spatially high in the east and low in the west. (2) The Global Moran's I index ranged from 0.740 to 0.687 during the study period, indicating a positive correlation in the EQ spatial distribution. The EQ of urban agglomerations has significant spatial agglomeration, with hot spots concentrated in the eastern region and cold spots in the northwestern region. (3) Main drivers of EQ of urban agglomerations are elevation, population density, nighttime light index, arable land area, real GDP per capita, precipitation, and built-up urban area (q > 10 %). (4) The stepwise multiple regression model spatially reveals that the nighttime light index, built-up urban area land and GDP per capita dominate the ecological quality changes of urban agglomerations, accounting for 73.68 % of the total number of urban agglomerations. This study provides an effective method for assessing spatial-temporal changes of EQ in urban agglomerations, supports scientific decision-making support for the construction of ecological civilization and the development of human–land harmony in urban agglomerations, and promotes the development and construction of “Beautiful China.”

A spatial distribution dataset for GDP in Hainan Island at 500 m from 2012 to 2020

Based on ESA (European Space Agency) landcover data, national polar-orbiting partnership-visible infrared imaging radiometer suite (NPP-VIIRS) nighttime light data and Hainan provincial statistical data, we implemented the spatialization of Hainan Island’s Gross Domestic Product (GDP) in this article. According to the relationship between gross product of primary industry and landcover type, spatialization of Hainan Island gross product of primary industry was implemented. And by building the regression model between gross product of secondary and tertiary industries and the nighttime light index, Hainan Island gross product of secondary and tertiary industries is spatialized. Then the spatialization of GDP was realized by combining the results of spatialization of Hainan Island's primary industry, secondary and tertiary industries. Compared with Hainan provincial statistical data, the results show that the maximum relative errors of spatial distribution data and actual statistical data of gross product of primary industry, gross product of secondary and tertiary industries and GDP in each period are 3.25%, 9.57% and 7.49%, respectively, which indicates the dataset can better describe the spatial distribution of GDP in Hainan Island. In addition, this dataset can provide a decision-making basis for the government to formulate policies on economic development of Hainan Island, and provide data support for scholars to explore differences and trends in economic development of Hainan Island.

Investigating drivers impacting vegetation carbon sequestration capacity on the terrestrial environment in 127 Chinese cities

Vegetation cover significantly improves the terrestrial environment by increasing carbon sequestration capacity. It is projected that a major threat to China's terrestrial environment will be happened by 2030 due to the increment in carbon emissions. Identifying reliable techniques to assess carbon absorption by green coverage is necessary to build a resilient environment. This research examines the performance of two weighted regression models to explain the capacity of vegetation carbon sequestration (VCS), spatial distribution, and degree of influence of vegetation coverage for reducing carbon emission. The results demonstrate changes in the VCS capacity from slow to fast, with an average yearly growth rate of 0.043% (2005–2010) to 1.963% (2010–2015) and more obvious growth in local cities. Variables such as the night-time light index, average relative humidity, and length of sunlight substantially impacted VCS capacity, although their effect varied yearly. Finally, the comparative results show that This study can play an influential role in finding specific locations facing issues with carbon emissions and can support local governments through the association of effective measures to mitigate it. • The vegetation carbon sequestration (VCS) capacity was assessed for chinas 127 cities. • The nighttime light index was used to characterize the socioeconomic factors. • Humidity, sunshine, wind and temperature data were used to assess the natural environment. • MGWR model more accurately analyzes the impact of various factors on VCS index. • The nighttime light index influnec was more significent in increasing VCS capacity.