National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite Research Articles

Revisiting the relationships between energy consumption, economic development and urban size: A global perspective using remote sensing data

Existing methods of measuring energy consumption require complex statistics and computing. A real-time and globally applicable approach for comparing energy consumption across different cities is still lacking. Additionally, the nonlinear relationships and varying thresholds of energy consumption in relation to economic activities and urbanization remain unconfirmed. This study aims to fill these gaps by utilizing Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data in 2015 and a top-down approach based on a multiple regression model to examine energy consumption in global cities employing a redefined urban boundary. It also explores the accurate relationship between energy consumption, population density (as a proxy of urbanization), and per capita gross domestic product (GDP) across different regions and urban sizes using generalized additive models and regression models. High-resolution gridded population and GDP datasets covering the entire planet are utilized for this purpose. The study also estimates the development potentiality. The study yields followings outcomes: Firstly, the top 30 cities with the highest per capita energy consumption account for over 0.66% of the total per capita energy consumption of all cities. Secondly, in East Asia (EA) and Southeast Asia (SEA), the per capita energy consumption decreases when per capita GDP reaches $40,000 and $75,000, respectively, while it remains stable in cities located in Western Europe (WE) and North America (NA) as per capita GDP increases. Thirdly, the per capita energy consumption declines with increasing urban population density until reaching 10,000 person/km2, 22,000 person/km2, and 4000 person/km2 in EA, SEA, and NA, respectively. Fourthly, in Central Asia (CA), megacities can save over 100 Mbtu/population when per capita GDP increases by $1000 compared to big cities. This pioneering study provides a comparable investigation of energy consumption at the global city level, exploring its relationship with urbanization and economy by employing a unified calculation standard. It will facilitate long-term energy-saving policies and urban planning strategies.

The Assessment of Industrial Agglomeration in China Based on NPP-VIIRS Nighttime Light Imagery and POI Data

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.

Urban resilience assessment and its spatial correlation from the multidimensional perspective: A case study of four provinces in North-South Seismic Belt, China

Modern cities are facing increasingly complex challenges, and assessing urban resilience is crucial to improve their ability to withstand various types of shocks and disasters, especially in the China's North-South Seismic Belt with frequent tectonic activities and natural disasters. To address this issue, this study determined the weights of the assessment system based on the entropy weight method, and evaluated the urban resilience of four provinces in China's North-South Seismic Belt in multiple dimensions from the perspectives of society, economy, infrastructure, and ecology. Furthermore, we systematically explored the spatiotemporal trends of urban resilience and its spatial correlation from 2011 to 2021. Lastly, the assessment results were validated and analyzed by four historical earthquake cases using national polar-orbiting partnership - visible infrared imaging radiometer suite (NPP-VIIRS) nighttime light data. The findings reveal that the average urban resilience index increased from 0.027 to 0.058 from 2011 to 2021, signifying a remarkable surge of 115.42%. Sichuan emerged as a consistent frontrunner in terms of urban resilience. Overall, the study area shows a spatial distribution pattern of higher urban resilience in the east and lower resilience in the west, due to the larger population and more developed economy in the east. However, the south has a higher average annual growth rate, while the north has a lower average annual growth rate. A notable observation is the gradual reduction in the coefficient of variation of urban resilience from 0.823 in 2011 to 0.751 in 2021, indicating a decline in the disparity of resilience levels. Meanwhile, Moran's I gradually increased from 0.2017 in 2011 to 0.4476 in 2021, signifying a progressive intensification in the spatial correlation of urban resilience and an evident propensity toward aggregation. By selecting shifts in the total nighttime light index (TNLI) during representative earthquake incidents as an indirect gauge of urban resilience levels, this study finds congruence with the assessment outcomes, thereby further substantiating the precision of the urban resilience evaluations. Policymakers should prioritize the allocation of resources to less resilient cities to improve their ability to withstand and recover from disaster events. The urban resilience assessment system in this study, despite its multidimensionality, may still not be able to fully cover all the factors affecting urban resilience. This study provides a significant decision-making basis for policymakers in urban development and disaster response, as well as a useful reference for building a more robust and sustainable urban future.

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

BackgroundUrbanization greatly affects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases. Schistosomiasis, a common parasitic disease transmitted by the snail Oncomelania hupensis, is mainly found in areas with population aggregations along rivers and lakes where snails live. Previous studies have suggested that factors related to urbanization may influence the infection risk of schistosomiasis, but this association remains unclear. This study aimed to analyse the effect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China.MethodsCounty-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected. The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The geographically and temporally weighted regression model (GTWR) was used to quantify the influence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled. The regression coefficient of urbanization was tested for significance (α = 0.05), and the influence of urbanization on schistosomiasis infection risk was analysed over time and across space based on significant regression coefficients. Variables studied included climate, soil, vegetation, hydrology and topography.ResultsThe mean regression coefficient for urbanization (0.167) is second only to the leached soil area (0.300), which shows that the urbanization is the most important influence factors for schistosomiasis infection risk besides leached soil area. The other important variables are distance to the nearest water source (0.165), mean minimum temperature (0.130), broadleaf forest area (0.105), amount of precipitation (0.073), surface temperature (0.066), soil bulk density (0.037) and grassland area (0.031). The influence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year. During the study period, the significant coefficient of urbanization level increased from − 0.205 to − 0.131.ConclusionsThe influence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous. The urbanization does reduce the risk of schistosomiasis infection to some extend, but the strength of this influence decreases with increasing urbanization. Additionally, the effect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors. This study provides scientific basis for understanding the influence of urbanization on schistosomiasis, and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

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

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

Monitoring Urban Expansion (2000–2020) in Yangtze River Delta Using Time-Series Nighttime Light Data and MODIS NDVI

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

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.

Spatio-Temporal Characteristics and Influencing Factors of Urban Spatial Quality in Northeast China Based on DMSP-OLS and NPP-VIIRS Nighttime Light Data

The quality of urban spaces is a pivotal part of high-quality spatial development. It is directly connected to the comprehensive, coordinated and sustainable development of a region. In recent years, Northeast China has characterized urban space contraction and development. To study the quality of urban space in Northeast China, this paper fitted the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data with 11 indicators related to high-quality urban development for the period 1992–2018. The feasibility of nighttime light data reflecting urban spatial quality was verified by a linear equation, and the temporal characteristics of urban spatial quality in Northeast China were obtained. The Exploratory Spatial Data Analysis Geographically and Temporally Weighted Regression (ESDA-GTWR) explores the spatial relevance and possible influencing factors of this kind of development. The results suggest that the overall trend of spatial quality in the three northeastern provinces is “initial slow growth and significantly weakened after”. The fast developing cities include Panjin, Liaoyang, Shenyang, and Dalian in the Liaoning Province. On the other hand, cities such as Heihe and Yichun in the Heilongjiang Province have relatively slow development speeds. Furthermore, the spatial quality development in the three northeastern provinces exhibits a trend of continuous concentration. The cities with high spatial qualities are concentrated near the Liaoning Province, with low spatial qualities in the north and high spatial qualities in the southern parts of the three provinces. As there is a notable gap between the northern and the southern regions, the central region represents an area in partial transition. The spatial quality of each city in the three northeastern provinces is the result of a number of intertwined factors, with significant differences in the degree of their influence. The significant degree of influence factors on spatial quality from higher to lower is urbanization, quality of life, rural revitalization, government promotion, and infrastructure.

Analyzing the Spatially Heterogeneous Relationships between Nighttime Light Intensity and Human Activities across Chongqing, China

Nighttime light (NTL) intensity is highly associated with the unique footprint of human activities, reflecting the development of socioeconomic and urbanization. Therefore, better understanding of the relationship between NTL intensity and human activities can help extend the applications of NTL remote sensing data. Different from the global effect of human activities on NTL intensity discussed in previous studies, we focused more attention to the local effect caused by the spatial heterogeneity of human activities with the support of the multiscale geographically weighted regression (MGWR) model in this study. In particular, the Suomi National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) NTL data within Chongqing, China were taken as example, and the point of interest (POI) data and road network data were adopted to characterize the intensity of human activity type. Our results show that there is significant spatial variation in the effect of human activities to the NTL intensity, since the accuracy of fitted MGWR (adj.R2: 0.86 and 0.87 in 2018 and 2020, respectively; AICc: 4844.63 and 4623.27 in 2018 and 2020, respectively) is better than that of both the traditional ordinary least squares (OLS) model and the geographically weighted regression (GWR) model. Moreover, we found that almost all human activity features show strong spatial heterogeneity and their contribution to NTL intensity varies widely across different regions. For instance, the contribution of road network density is more homogeneous, while residential areas have an obviously heterogeneous distribution which is associated with house vacancy. In addition, the contributions of the commercial event and business also have a significant spatial heterogeneity distribution, but show a distinct decrement when facing the COVID-19 pandemic. Our study successfully explores the relationship between NTL intensity and human activity features considering the spatial heterogeneity, which aims to provide further insights into the future applications of NTL data.

Spatio-Temporal Dynamics and Driving Forces of Multi-Scale CO2 Emissions by Integrating DMSP-OLS and NPP-VIIRS Data: A Case Study in Beijing-Tianjin-Hebei, China

The emission of greenhouse gases, especially CO2, is the main factor causing global warming. Due to incomplete statistical data on energy consumption at and below the urban scale of Beijing-Tianjin-Hebei (BTH), in this study, Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data were combined, and a neural network model and weighted average method based on DN (Digital Number) value were used to obtain CO2 emissions at the municipal and county scales with a resolution of 1 km × 1 km from 2000–2019. Next, a spatial-temporal analysis model and spatial econometric model were used to study the CO2 emissions at different scales of BTH. This study also solved the problem that STIRPAT analysis cannot be carried out due to insufficient urban statistical CO2 emissions data. The results show that the energy CO2 emissions in BTH present a distribution pattern of “East greater than West”, with a trend of first rising and then slowing down. Moreover, the rapid growth areas are mainly located in Chengde and Tianjin. The degree of regional spatial aggregation decreased year by year from 2000–2019. Population, affluence and technology factors were positively correlated with CO2 emissions in Tianjin and Hebei. For Beijing, in addition to foreign investment, factors such as urbanization rate, energy intensity, construction and transportation factors all contributed to the increase in CO2 emissions. Among them, the growth of population is the main reason for the increase of CO2 at the urban scale in BTH. Finally, based on the research results and the specific situation of the cities, corresponding policies and measures are proposed for the future low-carbon development of the cities.

Inversion of Regional Economic Trend from NPP-VIIRS Nighttime Light Data Based on Adaptive Clustering Algorithm

Night lighting is closely related to social and economic development. Inversion of socioeconomic parameters using nighttime light (NTL) remote sensing data is a research hot spot currently. In this paper, a calibration method based on adaptive clustering algorithm for the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data was proposed to remove background noise in the original imagery. The validity of the calibration method was evaluated through comparing the correlation between the corrected NTL data and the regional economic data. The result indicated that the NTL data obtained by this calibration method have higher correlation with the regional GDP data, and the values of R2 and the root mean square error (RMSE) were 0.8531 and 133.18, respectively. On this basis, the total nighttime light (TNL)-gross domestic product (GDP) regression model obtained from this paper was used to invert the GDP of Liaoning Province from 2012 to 2016. Using the TNL-GDP regression model established in high-quality regions to verify the fraudulent economic statistics of Liaoning Province, it can be proved that NTL data can be a reliable reference for reflecting regional economic development trends.

Spatial Expansion and Correlation of Urban Agglomeration in the Yellow River Basin Based on Multi-Source Nighttime Light Data

The Chinese government proposed a major national strategy for ecological protection and high-quality development in the Yellow River Basin. The Framework of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin proposes building a dynamic development pattern characterized by “one axis, two regions and five poles” in the Yellow River Basin with high-quality and high-standard urban agglomerations along the Yellow River. The urban agglomeration is the economic growth pole of the Yellow River Basin and the main carrier of the population and productivity. This study integrates DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night light remote sensing data from 2000 to 2020 and uses methods such as spatial expansion measurement, the center of gravity offset, urban primacy, and the gravity model to study the spatial expansion and correlation characteristics of five urban agglomerations. The results show that: (1) From 2000 to 2020, urban agglomeration in the Yellow River Basin continued to expand, and the area increased by 6.4 times. The total amount of nighttime lights in the city presents a spatial distribution pattern that is high in the east and low in the west. (2) The expansion centers of the five major urban agglomerations all shifted. The centers of gravity of the Shandong Peninsula urban agglomeration, the Jiziwan urban agglomeration of the Yellow River, the Guanzhong Plain urban agglomeration, and the Lanzhou–Xining urban agglomeration all shifted westward, while the center of gravity of the Central Plains urban agglomeration shifted to the southeast. (3) Qingdao, Zhengzhou, Xi’an and Lanzhou are the primate cities of the four urban agglomerations of the Shandong Peninsula, Central Plains, Guanzhong Plain, and Lanzhou–Xining, respectively. The primate city in the Jiziwan urban agglomeration of the Yellow River was changed from Taiyuan to Yinchuan and then to Yulin. (4) The density of the gravitational network of the urban agglomeration in the Yellow River Basin and the distribution of the maximum gravitational line show the spatial differentiation characteristics of being dense in the east and sparse in the west.

An Approach for Retrieving Consistent Time Series “Urban Core–Suburban-Rural” (USR) Structure Using Nighttime Light Data from DMSP/OLS and NPP/VIIRS

The long time series and consistent “urban core-suburban-rural” (USR) structure in a city region is essential to understanding urban–suburban–rural interaction and urbanization pathways. It is always considered to be a single land use type (e.g., impervious area) in remote sensing research. The long-term (1992–present) nighttime light (NTL) data of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) provide the potential for retrieving time series of USR structure. In this study, we propose an improved approach to mapping the USR structure of the three subcategories based on a heuristic algorithm of Mann–Kendall mutation detection on the NTL quantile curve. First, a minor adjustment of VIIRS NTL is applied for matching the value ranges of DMSP NTL data and keeping the advantage of VIIRS to generate a long-term NTL dataset. Second, the heuristic algorithm of Mann–Kendall mutation detection is processed to find two optimal thresholds in the NTL quantile curve, which is used for USR extraction. Finally, a temporal consistency check is used to post-process the initial USR area for obtaining a more consistent and reliable USR sequence. To evaluate the performance of the proposed method, we retrieved the USR structures of 19 typical cities in China from 1992 to 2020 based on NTL datasets. The evaluations of spatiotemporal consistency compared with the validation data indicate that the USR retrieval results show good agreement with the land use map derived from Landsat images and the time series product from MODIS. The average overall accuracy (OA) of overall urban extent is higher than 0.95 and the average kappa coefficient (KC) reaches 0.6. Moreover, we investigated the urban dynamics and USR interactions of 19 cities from 1992 to 2020. Overall, this study proposes an improved approach for long-term USR mapping from NTL images at a regional scale and it will provide a valuable method for urbanization dynamics analysis.

Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS

Adequate and up-to-date knowledge of the spatiotemporal dynamics of electricity power consumption (EPC) is important for the sustainable use of global electricity power resources. However, global EPC patterns were not clear after Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) in 2013 due to the significant differences between Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) and DMSP-OLS. In this paper, global EPC patterns in the recent decade are investigated and assessed for the first time by the proposed locally adaptive method with integrating two nighttime light (NTL) images to global pixel-level EPC from 1992 to 2019. The geospatial dataset of built-up area density (BUAD) is adopted with a higher spatial resolution and more direct relation to human activities. A two-step regression method is designed to simulate DMSP-like images after 2013, based on the inter-annual relationships of provincial-level VIIRS. With this consistent nighttime light dataset, pixel-level EPC over the 28 years are estimated for the first time, and then the spatiotemporal dynamics of EPC are investigated from global, continental, to national scales. The obtained EPC estimates are of satisfactory accuracy in 92.6% of the countries with a MARE (Mean of the Absolute Relative Error) of less than 20%. Over these 28 years, Japan, South Korea, and China experienced high proportion of EPC high-growth. These results provide reliable scientific basis for exploring the spatial pattern and temporal variations of global EPC, especially for the latest years.

Spatiotemporal Evolution and Coupling Pattern Analysis of Urbanization and Ecological Environmental Quality of the Chinese Loess Plateau

Understanding the interactive coupling mechanism between urbanization and eco-environmental quality is crucial to achieve the goal of urban sustainable development. The Chinese Loess Plateau (CLP) was taken as the research object, and the city nighttime light index (CNLI) and remote sensing ecological index with local adaptability (LARSEI) were constructed based on the data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS), National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS), and Moderate Resolution Imaging Spectroradiometer (MODIS). Then, trend analysis, standard deviation ellipse (SDE), coupling degree (C), and coupling coordination degree (CCD) models were used to determine the spatiotemporal variation of urbanization and eco-environmental quality and its coupling relationship. The results show that: (1) the urbanization level of the CLP showed a trend of continuous improvement from 2000 to 2019. A significant increasing trend was found from the CNLI (slopeCNLI = 0.0030 yr−1, p < 0.01), and its value rose from 0.07 in 2000 to 0.14 in 2019. In terms of spatial distribution, a multi-core distribution pattern with provincial capital cities as the core was presented in the CLP. The cities expanded at different degrees and presented a gradual concentrated expansion towards the southeast on the whole. (2) The eco-environmental quality in the CLP greatly increased during 2000 to 2019. An area with an increasing trend in the remote sensing ecological index with local adaptability (LARSEI) accounted for 58.82% and was mainly concentrated in the west and central part of the CLP. (3) The C and CCD between urbanization and eco-environmental quality in the CLP presented a trend of significant increase during 2000 to 2019 (slopeC = 0.0051 yr−1, p < 0.01; slopeCCD = 0.0040 yr−1, p < 0.01). The cities with a higher coupling degree were mainly located in the southeastern and northern parts of the CLP, while those with a higher coordination degree were scattered in the marginal parts of the CLP. The research results can provide suggestions for decision-making to achieve high-quality coordinated development of the cities in the CLP.

Efficiency of China's urban development under carbon emission constraints: A city-level analysis

This study explores the efficiency of China's urban development under carbon emission constraint from 1996 to 2017. Firstly, it uses the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) and Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nightlight imaging to evaluate the carbon dioxide emission levels of 249 cities in China. Then, it utilises the epsilon-based measure (EBM) superefficiency model and Global Malmquist–Luenberger (GML) index to evaluate the urbanization efficiency and changes in the total factor productivity (TFP) of urban development of the 249 cities under carbon emission constraints. Results show that from 1996 to 2017, the total carbon emissions of the 249 cities grew during a rapid development stage, while the efficiency of urban development under carbon emission constraints showed fluctuation and did not increase significantly. The redundant improvement values of government fiscal expenditure, urban employees, urban fixed asset investment, foreign direct investment, and carbon emission increased in fluctuations. From the regional spatial perspective, the urbanization efficiency of western China was the highest, followed by the northeast region, the central region, and finally the eastern region. In addition, the driving factors of TFP of urbanization in China have improved as a whole. The application of nightlight imaging data and EBM superefficiency model improves the accuracy of urban development efficiency measurement. The findings of this study can provide a scientific basis for different regions and cities of different sizes to formulate carbon emission reduction strategies, and have important policy value for improving urban development efficiency in China. In addition, the research of index system should be deepened to further improve the measurement accuracy.

Urban shrinkage in a developing context: Rethinking China's present and future trends

While many studies have investigated urban shrinkage in Chinese cities, most have used political-administrative boundaries rather than urban entities to examine population change. This results in an inaccurate picture of urban shrinkage and, more importantly, an inadequate understanding of its underlying mechanism. This study utilized land-use data to identify the urban entity boundary of a city, and then employed the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nightlight data as a proxy to estimate urban population change from 2012 to 2019. Next, a conceptual framework was developed from the perspective of migration processes and their transformation to explain current urban shrinkage and to explore future trends. We found that only about 6.3% of the sample cities experienced significant urban population shrinkage from 2012 to 2019, most of which were resource-based cities in the Northeast region. A lack of employment opportunities, stagnant economic growth, and low birth rate were important drivers of urban shrinkage. In contrast, low urbanization levels and the accompanying strong intra-regional rural-to-urban migration process can help prevent urban shrinkage and have contributed to the current low percentage of shrinking cities. However, urban shrinkage may increase in the future based on insights into the transformation of migration processes under the persistent unbalanced regional development and the increasing urbanization level. According to these findings, we suggest that, while urban expansion and related challenges may still be prioritized by Chinese policy makers, it is also necessary to promote the sustainable development of urbanization to deal with and prevent urban shrinkage.

Rapid assessment of disaster damage and economic resilience in relation to the flooding in Zhengzhou, China in 2021

ABSTRACT The timely and accurate assessment of flooding disasters and economic resilience is significant for post-disaster reconstruction and recovery. In July 2021, the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data were explored as a proxy to assess the flooding damage caused by heavy rainfall in Zhengzhou City, China. A combination of the night-time light (NTL) changes and the radiation normalization method was used to rapidly identify affected areas and extract populations following the flooding disaster. A daily gross domestic product (GDP) prediction model was developed to evaluate the economic resilience of Zhengzhou City using multi-temporal DNB daily and monthly NTL data. The severity of the disaster was estimated by the extent of power outages, flooding crisis regions, and affected populations. It has been predicted that the Zhengzhou economy is unlikely to be restored to its normal level before the end of 2021 owing to the dual impact of the coronavirus outbreak and flooding disaster; the revised recovery-time prediction is late April 2022. We concluded that our NTL data provided new, simple, and effective insights into the post-flooding assessment of the affected areas, populations, GDP forecast, and economic recovery.

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

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

City- and county-level spatio-temporal energy consumption and efficiency datasets for China from 1997 to 2017

Understanding the evolution of energy consumption and efficiency in China would contribute to assessing the effectiveness of the government’s energy policies and the feasibility of meeting its international commitments. However, sub-national energy consumption and efficiency data have not been published for China, hindering the identification of drivers of differences in energy consumption and efficiency, and implementation of differentiated energy policies between cities and counties. This study estimated the energy consumption of 336 cities and 2,735 counties in China by combining Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) and Suomi National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) satellite nighttime light data using particle swarm optimization-back propagation (PSO-BP). The energy efficiency of these cities and counties was measured using energy consumption per unit GDP and data envelopment analysis (DEA). These data can facilitate further research on energy consumption and efficiency issues at the city and county levels in China. The developed estimation methods can also be used in other developing countries and regions where official energy statistics are limited.