Expansion Of Urban Built-up Areas Research Articles

An Improved Method to Identify Built-Up Areas of Urban Agglomerations in Eastern and Western China Based on Multi-Source Data Fusion

The rapid urbanization in China has significantly contributed to the vast expansion of urban built-up areas. Precisely extracting and monitoring these areas is crucial for understanding and optimizing the developmental process and spatial attributes of smart, compact cities. However, most existing studies tend to focus narrowly on a single city or on global scale with a single dimension, often ignoring mesoscale analysis across multiple urban agglomerations. In contrast, our study employs GIS and image-processing techniques to integrate multi-source data for the identification of built-up areas. We specifically compare and analyze two representative urban agglomerations in China: the Yangtze River Delta (YRD) in the east, and the Chengdu–Chongqing (CC) region in the west. We use different methods to extract built-up areas from socio-economic factors, natural surfaces, and traffic network dimensions. Additionally, we utilize a high-precision built-up area dataset of China as a reference for verification and comparison. Our findings reveal several significant insights: (1) The multi-source data fusion approach effectively enhances the extraction of built-up areas within urban agglomerations, achieving higher accuracy than previously employed methods. (2) Our research methodology performs particularly well in the CC urban agglomeration. The average precision rate in CC is 96.03%, while the average precision rate in YRD is lower, at 80.33%. This study provides an objective and accurate assessment of the distribution characteristics and internal spatial structure of built-up areas within urban agglomerations. This method offers a new perspective for identifying and monitoring built-up areas in Chinese urban agglomerations.

Investigating the multiscale associations between urban landscape patterns and PM1 pollution in China using a new combined framework

Urban landscape patterns serve as the spatial carriers of emission sources and influencing factors of air contaminants, which inevitably impact submicron particle (PM1) pollution. However, most studies ignore the spatial nonstationary and nonlinear effects of urban landscape patterns on the PM1 concentration. In this study, a novel framework was developed that integrates a multiscale geographically weighted regression (MGWR) model and Shapley Additive exPlanations (SHAP) machine learning method to explore the spatial heterogeneity effect, relative contribution and nonlinear impact of landscape patterns on the PM1 concentration at the national and urban agglomeration scales. The results indicated that urban landscape patterns were closely related to PM1 pollution and exhibited obvious spatial differences. The disorderly expansion of urban built-up areas and an irregular urban morphology could aggravate PM1 pollution, while urban fragmentation and connectivity could impose reducing effects. Dispersed urban landscape patterns could reduce the PM1 concentration in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Chengdu-Chongqing (CDCQ) regions, whereas compact and continuous landscape patterns positively affected PM1 pollution mitigation in the Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZH) urban agglomerations. The impact of urban landscape patterns on PM1 pollution was greater in urban agglomerations than elsewhere. The ENN_MN landscape index exhibited the highest feature importance and interpretability. The threshold effects between the urban shape indicators and PM1 concentration were more complex than those with the other landscape indices. This critical knowledge provides a scientific basis for further understanding the correlation mechanism between PM1 pollution and the landscape pattern, urban sustainable planning and air pollution control.

Spatiotemporal Evolution and Prediction of Land Use and Carbon Stock in Shanghai

Soil organic carbon (SOC) constitutes a critical component of carbon reservoirs within terrestrial ecosystems. The ramifications of urban land use transitions on SOC dynamics, particularly in rapidly urbanizing regions such as Shanghai, remain insufficiently elucidated. This investigation synergizes a predictive land use change model (Logistic-CA-Markov) with an ecosystem service quantification framework (InVEST), aiming to delineate the interplay between SOC variability and Land Use and Land Cover Change (LUCC) under natural development and ecological protection scenarios. Empirical observations from 2010 to 2020 reveal a contraction in Shanghai’s agricultural land of 34,912.76 hectares, juxtaposed with an expansion of urban built-up areas of 36,048.24 hectares. Projections for 2030 under an ecological protection scenario indicate a moderated urban sprawl, reducing built-up area expansion by 13,518 hectares relative to the natural development scenario. Notably, the net carbon sequestration capacity of Shanghai is anticipated to diminish by approximately 0.418 million tons between 2020 and 2030. This trend is observed under both considered scenarios, forecasting a cumulative reduction in SOC stocks exceeding 1 million tons by 2030. The natural development pathway portends a more pronounced and accelerated depletion of SOC reserves. Although ecological conservation measures show the potential to decelerate this loss, they appear insufficient to reverse the ongoing decline in SOC stocks. This study advocates for strategic urban planning interventions focused on constraining the growth of building densities and augmenting the preservation and management of eco-lands. Such measures are imperative for bolstering Shanghai’s carbon sequestration capacity.

Spatial and Temporal Evolution of Urban Patterns in the China Greater Bay Area Based on Remote Sensing of Nighttime Lights

With the advancement of urbanization in China, the rapid expansion of cities has reduced the alternative land use and intensified the conflict between people and land, and the adaptation and optimization of urban spatial patterns have become an important work in current urban research. Based on the comprehensive modified the long-term series DMSP-OLS and NPP-VIIRS nighttime lighting data, the spatial analysis method of GIS and urban expansion indicators are used to reveal the spatial and temporal change process of urban built-up areas in the center of gravity of Guangdong-Hong Kong-Macao Greater Bay Area from 1992 to 2020. The results reveal that the built-up area of the urban agglomerations in the study area generally shows an increasing trend year by year, but there are some periods when the area decreases or remains unchanged, while the expansion rate shows a gradual decrease over a long time scale. The compactness of the urban agglomerations in the GBA decreases over time, and the fractal dimension fluctuates but remains unchanged, while the spatial distribution of the urban agglomerations in the GBA shows a "southwest-northeast" direction during 1992-2020, with the center of gravity moving to the northeast gradually. On the side, the urban expansion shows a fluctuation of "diffusion-coalescence-diffusion". The expansion of urban built-up areas is influenced by both natural and socio-economic factors, and the degree of influence varies in different periods.

Effect of Urban Built-Up Area Expansion on the Urban Heat Islands in Different Seasons in 34 Metropolitan Regions across China

The urban heat island (UHI) refers to the land surface temperature (LST) difference between urban areas and their undeveloped or underdeveloped surroundings. It is a measure of the thermal influence of the urban built-up area expansion (UBAE), a topic that has been extensively studied. However, the impact of UBAE on the LST differences between urban areas and rural areas (UHIU−R) and between urban areas and emerging urban areas (UHIU−S) in different seasons has seldom been investigated. Here, the UHIU−S and UHIU−R in 34 major metropolitan regions across China, and their spatiotemporal variations based on long-term space-borne observations during the period 2001–2020 were analyzed. The UBAE quantified by the difference in landscape metrics of built-up areas between 2020 and 2000 and their impact on UHI was further analyzed. The UBAE is impacted by the level of economic development and topography. The UBAE of cities located in more developed regions was more significant than that in less developed regions. Coastal cities experienced the most obvious UBAE, followed by plain and hilly cities. The UBAE in mountainous regions was the weakest. On an annual basis, UHIU−R was larger than UHIU−S, decreasing more slowly with UBAE than UHIU−S. In different seasons, the UHIU−S and UHIU−R were larger, more clearly varying temporally with UBAE in summer than in winter, and their temporal variations were significantly correlated with UBAE in summer but not in winter. The seasonal difference in UHIU−R was larger than that of UHIU−S. Both the UHIU−S and UHIU−R in coastal cities were the lowest in summer, decreasing the fastest with UBAE, while those in mountain cities decreased the slowest. The change in the density of built-up lands was the primary driver affecting the temporal variations in UHIU−S and UHIU−R during UBAE, followed by changes in proportion and shape, while the impact of the speed of expansion was the smallest, all of which were more obvious in summer than in winter. The decreased density of built-up lands can reduce UHI. These findings provide a new perspective for a deeper understanding of the effect of urban expansion on LST in different seasons.

POWER PYRAMID, HIDDEN DEBT, AND CHINA’S URBAN EXPANSION

Chinese urban spatial expansion leads to inefficient use of land resources. This study uses the data of 289 cities from 2007 to 2018 and uses panel data models to test the driving mechanism of urban expansion. The study derives three conclusions. First, there is a significant positive correlation between urban power hierarchy and urban spatial expansion. If the urban administrative power hierarchy is high, then the urban built-up area increases more. Second, there is a significant positive correlation between the scale of hidden debt and the expansion of urban space. The increase of the urban investment bonds’ scale will promote the expansion of urban built-up areas. Third, there is a significant positive correlation between the quantity of bond issuers and urban spatial expansion.

Triple Spatial Effects of the Administrative Hierarchy on Urban Built-Up Areas in Fujian Province, China: Heterogeneity, Radiation, and Segmentation

The expansion of urban built-up areas is one of the most prominent characteristics of land use change in China. A growing body of literature has emphasized the triple spatial effects of the administrative hierarchy on urban built-up areas expansion, including heterogeneity, radiation, and segmentation. However, the existing studies have mainly focused on the administrative hierarchy at the prefectural level and above and have primarily concentrated on one single effect; few have integrated the triple effects as a whole. Based on high-resolution land use data and taking Fujian province as a study case, this study proposes an integrated theoretical framework and modeling approach and investigates the triple spatial effects of administrative hierarchy on built-up areas at the prefectural level and below. Descriptive statistics show the following: (1) Built-up areas of municipal districts are significantly larger than those of county-level units, showing the heterogeneity characteristics of urban land distribution across different levels of administrative hierarchy; (2) The county-level units adjacent to municipal districts exploit more built-up areas than other county-level units, indicating the radiation effects of municipal districts; (3) The radiation effects tend to be reduced if a municipal district and its adjacent county-level units are not located in the same prefectural city, revealing the segmentation effects among the different prefectural cities. Using the spatial econometric model with regimes, we further find the following: (1) The strengths of driving forces of built-up areas are heterogeneous between municipal districts and county-level units, and there are significant spatial interactions among administrative units; (2) The spatial interactions between municipal districts and county-level units are stronger than those between two county-level units, but the strength is restricted by the prefectural boundary, reflecting the radiation effects of municipal districts and the segmentation effects of the prefectural boundary, respectively. By investigating the triple spatial effects of the administrative hierarchy on urban built-up areas, we conclude that comprehensively considering these triple effects as a whole will result in a fuller understanding of the rapid built-up areas expansion in China, especially at the prefectural level and below.

A New Approach to Monitoring Urban Built-Up Areas in Kunming and Yuxi from 2012 to 2021: Promoting Healthy Urban Development and Efficient Governance.

With the rapid expansion of urban built-up areas in recent years, accurate and long time series monitoring of urban built-up areas is of great significance for healthy urban development and efficient governance. As the basic carrier of urban activities, the accurate monitoring of urban built-up areas can also assist in the formulation of urban planning. Previous studies on urban built-up areas mainly focus on the analysis of a single time section, which makes the extraction results exist with a certain degree of contingency. In this study, a U-net is used to extract and monitor urban built-up areas in the Kunming and Yuxi area from 2012 to 2021 based on nighttime light data and POI_NTL (Point of Interest_Nighttime light) data. The results show that the highest accuracy of single nighttime light (NTL) data extraction was 89.31%, and that of POI_NTL data extraction was 95.31%, which indicates that data fusion effectively improves the accuracy of built-up area extraction. Additionally, the comparative analysis of the results of built-up areas and the actual development of the city shows that NTL data is more susceptible to urban emergencies in the extraction of urban built-up areas, and POI (Point of interest) data is subject to the level of technology and service available in the region, while the combination of the two can avoid the occasional impact of single data as much as possible. This study deeply analyzes the results of extracting urban built-up areas from different data in different periods and obtains the feasible method for the long time sequence monitoring of urban built-up areas, which has important theoretical and practical significance for the formulation of long-term urban planning and the current high-quality urban development.

Spatiotemporal variation and driving factors analysis on the expansion of the main urban agglomerations in China

The process of urbanization has brought about prosperity in urban civilizations, causing a series of ecological and social problems. Therefore, in recent years, monitoring the process of urban expansion has become a hot spot in the field of geosciences. The 8 main urban agglomerations built-up areas from 1995 to 2015 were extracted by night light images. Based on the expansion speed and intensity index, center of gravity migration model, spatial correlation analysis and grey correlation analysis, the characteristics of the spatial and temporal variation were described. Based on it, a driving force model was established to explore the factors behind its spatial and temporal expansion. The built-up areas of the Yangtze River Delta, the Pearl River Delta, the Beijing-Tianjin-Hebei Region, the Chengdu-Chongqing Economic Circle, Central Plains, the middle reaches of the Yangtze River, central Yunnan, and the Beibu Gulf have been increasing year by year, and reached 7671 km2, 3926 km2, 3729 km2, 3025 km2, 6649 km2, 3172 km2, 500 km2, 1047 km2 in 2015, which are 5.0, 6.6, 2.6, 5.1, 3.1, 2.8, 3.5, 3.2 times more than that in 1995 respectively. There is an expansion trend of ‘point-block-surface’ from the overall perspective. The development of all eight urban agglomerations belongs to the spatial expansion mode under the guidance of agglomeration, the spatial distribution presents positive spatial autocorrelation, and the agglomeration degree manifests fluctuating changes. Socio-economic factors such as non-agricultural population, regional Gross Domestic Product, and total industrial output have a greater impact on the expansion of urban built-up areas, while the number of colleges and universities and the total investment in fixed assets have less impact with less synchronization.

Extraction of Urban Built-Up Area Based on Deep Learning and Multi-Sources Data Fusion—The Application of an Emerging Technology in Urban Planning

With the rapid expansion of urban built-up areas in recent years, it has become particularly urgent to develop a fast, accurate and popularized urban built-up area extraction method system. As the direct carrier of urban regional relationship, urban built-up area is an important reference to judge the level of urban development. The accurate extraction of urban built-up area plays an important role in formulating scientific planning thus to promote the healthy development of both urban area and rural area. Although nighttime light (NTL) data are used to extract urban built-up areas in previous studies, there are certain shortcomings in using NTL data to extract urban built-up areas. On the other hand, point of interest (POI) data and population migration data represent different attributes in urban space, which can both assist in modifying the deficiencies of NTL data from both static and dynamic spatial elements, respectively, so as to improve the extraction accuracy of urban built-up areas. Therefore, this study attempts to propose a feasible method to modify NTL data by fusing Baidu migration (BM) data and POI data thus accurately extracting urban built-up areas in Guangzhou. More accurate urban built-up areas are extracted using the method of U-net deep learning network. The maximum built-up area extracted from the study is 1103.45 km2, accounting for 95.21% of the total built-up area, and the recall rate is 0.8905, the precision rate is 0.8121, and the F1 score is 0.8321. The results of using POI data and BM data to modify NTL data to extract built-up areas have not been significantly improved due to the fact that the more data get fused, the more noise there would be, which would ultimately affect the results. This study analyzes the feasibility and insufficiency of using big data to modify NTL data through data fusion and feature extraction system, which has important theoretical and practical significance for future studies on urban built-up areas and urban development.

Spatial and Temporal Changes of Urban Built-Up Area in the Yellow River Basin from Nighttime Light Data

Nighttime light (NTL) images obtained by the Visible Infrared Imaging Radiometer (VIIRS) mounted on the National Polar-orbiting Partnership (NPP) could objectively represent human activities and instantly identify urban shapes on a temporal and spatial scale. From 2013 to 2020, the built-up areas of eight provincial capital cities were extracted using NPP/VIIRS NTL data to examine the dynamic changes in city expansion and socioeconomic development in the Yellow River Basin during the urbanization process. The spatial characteristics of urban built-up area expansion were generated using the eight-quadrant analysis method and combined with the statistical data of population and (gross domestic product) GDP to analyze the correlations between the light intensity of built-up areas, population and GDP; this enables an understanding of the changes in population and economy in the development of urban built-up area expansion. The findings show that: (1) unbalanced city development existed in the Yellow River Basin’s upper, middle, and lower reaches, and the expansion and light intensity of cities in the upper reaches were slower than those in the middle and lower reaches; (2) the spatial differentiation of urban expansion was significant between each of the reaches in the Yellow River Basin, and greatly influenced by natural geographical elements; and (3) positive correlation exists between light intensity, population, and GDP in the built-up areas of the middle and lower reaches, while the correlations in the upper reaches were not stable. In conclusion, light data indirectly reflects urban development and could be used as a substitute variable for socioeconomic development indicators under certain conditions.

Projections of urban built-up area expansion and urbanization sustainability in China's cities through 2030

In the context of socioeconomic initiatives to address world development sustainability such as the Sustainable Development Goals (SDGs), China's urbanization with increasing intensity and quickening pace has always been central to debates over the past few decades. Although the United Nations (UN) recommend to estimate and predict the local-scale urbanization sustainability by using the SDG indicator 11.3.1: “ratio of land consumption rate to population growth rate (LCRPGR)”, there remains a lack of reliable projections and applicable assessments at the city level, especially for the future urban expansion. Until now, limited knowledge of the urbanization sustainability dynamics in the next decade has been the primary challenge to inform the realization of urban sustainable development goal under the framework of SDGs. To evaluate the geographic region- and population size-specific urbanization sustainability at the city level in mainland China between 2020 and 2030, an integrated downscaling approach of trend extrapolation and regression analysis was developed based on historical statistics of urban built-up area and subnational scenarios of population and gross domestic product (GDP) under the Shared Socioeconomic Pathways (SSPs). The SSPs-consistent urban built-up area projections across different scales showed that the logistic regression had a better modeling performance than the panel data regression, while there was little difference between the linear and exponential extrapolation methods. Despite the geographical heterogeneity and size-related disparity, the scenario modeling results suggested that the average value of LCRPGR indicator in mainland China was expected to decline continuously, and would reach at the ideal state with a coordination between urban expansion and population growth through 2030. Specifically, the population growth stagnation of cities in Northeast China and the allometric urban expansion of cities in Western China should be the focus of strengthened regulations, and the urban growth of megacities would experience a steady and slower trend compared to their surrounding super-large cities and large cities. In summary, China's urbanization process might have headed toward a more sustainable and coordinated future under feasible SSPs scenarios. The local-scale forecasts will not only fill the data gap of SSPs-consistent urban land demand for all county- and prefecture-level cities in mainland China, but can also provide a reference for forecasting and assessment of urbanization sustainability to meet the SDGs in urban areas such as indicator 11.3.1.

Analysis on process of temporal and spatial evolution of urban built-up area expansion in the Yellow River Basin.

Urban spatial expansion is known as an important indicator of urbanization. In order to provide a reference for urban spatial expansion in the future high-quality development strategy of the Yellow River Basin (YB) cities in China, it is necessary to identify and calculate urban spatial expansion patterns. For this reason, we provide a "Spatiotemporal pattern-Center of gravity migrationt-Expansion pattern" solution to identify and calculate urban spatial expansion patterns in the YB. More specifically, 78 prefecture-level cities in the YB were selected as the subjects of the study, using the Defense Meteorological Satellite Program/Operational Line Scan System (DMSP/OLS) and the National Polarimetric Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data (NTL), together with the center of gravity shift and common edge detection models, to identify the YB urban expansion patterns from 2000–2018. The results suggest that: (1) on the spatial pattern, there is a obvious difference in the expansion intensity and growth rate of the urban built-up (UB) areas of cities in the upper and middle reaches of YB. In addition, there are also certain differences between the expansion patterns of provincial capital cities and non-capital cities; (2) The UB areas of YB has steadily expand from 3,500 km2 in 2000 to 10,600 km2 in 2018, amongst which the expansion of provincial capital cities is the most obvious 1919 km2; (3) Interestingly it is also discovered that urban expansion in Qinghai Province, the sourceland of the YB, takes place in a diffuse way, with the shifting of the centre of gravity for four types of total area, net increase in area, rate of growth and intensity of expansion followed a "northwest to southeast" tendency of development.

Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas

Changes in the spatial expansion of urban built-up areas are of great significance for the analysis of China’s urbanization process and economic development. Nighttime light data can be used to extract urban built-up areas in a large-scale and long-time series. In this article, we introduced the UNet model, a semantic segmentation network, as a base architecture, added spatial attention and channel attention modules to the encoder part to improve the boundary integrity and semantic consistency of the change feature map, and constructed an urban built-up area extraction model—CBAM_UNet. Also, we used this model to extract urban built-up areas from 2012 to 2021 and analyzed the spatial and temporal expansion of China’s urban built-up areas in terms of expansion speed, expansion intensity, expansion direction, and gravity center migration. In the last decade, the distribution pattern of urban built-up areas in China has gradually changed from “center” to “periphery-networked” distribution pattern. It reveals a trend from agglomeration to the dispersion of urban built-up areas in China. It provides a reference for China’s urban process and its economic development.

Urban Sprawl and Haze Pollution: Based on Raster Data of Haze PM2.5 Concentrations in 283 Cities in Mainland China

Incorporating the urban sprawl and its quadratic term into the analytical framework of the environmental Kuznets curve and considers the spatial and threshold effects of pollution, this paper used the raster data of haze PM2.5 concentrations in 283 cities in mainland China to verify the non-linear effects of urban sprawl on urban haze pollution. It finds that: the inter-city spillover effect of haze pollution is significant, and the environmental Kuznets curve holds on haze pollution; there is an inversed “U” relationship between urban population size and haze pollution; the enlarge of the urban built-up area of city would increase haze pollution significantly; the impact of urban population size on haze pollution has a threshold effect that it would decline with the urban built-up area expansion; the coordination between population urbanization and land urbanization has an notable effect on haze pollution that its incoordination in China’s urbanization has aggravated haze pollution in city and this impact would lagged 1–2 period in time.

ANALYSIS OF URBAN EXPANSION CHARACTERISTICS OF YANGTZE RIVER DELTA URBAN AGGLOMERATION BASED ON DMSP/OLS NIGHTTIME LIGHT DATA

Abstract. Urban agglomeration is the strongest driving force of national economic development. The formation and development of urban agglomeration is an important feature of modern urbanization process, and it is of great significance to study its spatial expansion and development process for regional sustainable development. Based on the DMSP/OLS nighttime light remote sensing data from 2000 to 2010, this paper uses threshold method to extract the urban built-up area of the Yangtze River Delta urban agglomeration. The expansion characteristics of urban built-up area in Yangtze River Delta urban agglomeration are analysed from three aspects: expansion area, expansion intensity and expansion type. The results show that the urban agglomeration in The Yangtze River Delta is experienced a rapid urban built-up area expansion process during the decade. The proportion of urban built-up area increased from 9.95% to 15.46%, the average nighttime light intensity increased from 10.18 to 23.43, and the type of urban built-up area expansion was filling type.

Shrinking cities, urban expansion, and air pollution in China: A spatial econometric analysis

Although shrinking cities' socio-economic sustainability has been focused in academia, the diffusing nature of shrinking cities’ environmental impact has rarely been systematically investigated. It remains uncertain the spatial extent of the effect of shrinking cities has on the environment. In this study, we address this issue by calculating the concentration of PM2.5 in the built-up area of 640 prefecture-level and county-level cities in China, and applying the Spatial Dubin Model (SDM) to analyze the spatial relationship between shrinking cities, built-up area expansion, and air pollution in China between 2000 and 2016. Our statistical findings indicate a negative relationship between shrinking cities and increased air pollution, both directly and indirectly. When cities contract, the growth of air pollution in local and surrounding areas decreases by 0.143% and 0.635%, respectively. However, shrinking cities associated with urban built-up area expansion have relatively higher air pollution growth. They contribute 0.598% and 4.968%, respectively, to the increase in air pollution in the local and surrounding areas. Besides, local economic development in mitigating air pollution has a positive spatial spillover effect on air quality in the neighboring cities. In contrast, local population size has a negative spatial spillover effect. The above findings contribute knowledge to the convoluted spatial dynamics between urban decline and air pollution in China. The associated policy implications are also discussed.

City size and energy conservation: Do large cities in China consume more energy?

Although the nexus between urbanization and energy consumption has been widely discussed at the macro level or in specific areas such as in relation to residents and transport, how the expansion of city size affects energy consumption in the process of urbanization is still unknown. Clarifying this impact mechanism is necessary for China to achieve its energy conservation goals. In this context, based on a systematic theoretical elaboration, this study constructs an econometric model of the nexus between city size and energy use and performs a series of robust empirical analyses through the endogenous control of instrumental variables. The results show that the current expansion of China's city size tends to positively affect energy consumption; however, as city size continues to expand, energy consumption will exceed the critical value and change from increasing to decreasing. In this process, it is easier to achieve a decline in energy intensity than a decline in per capita energy consumption. Cities with a population exceeding 1 million in their urban districts are more conducive to reducing energy use. Compared with the expansion of urban built-up areas, an increasing population agglomeration can more effectively promote the decline of urban energy consumption. This study provides policy makers with new ideas about urban planning and energy conservation.

Evaluating the influencing factors of urbanization in the Xinjiang Uygur Autonomous Region over the past 27 years based on VIIRS-DNB and DMSP/OLS nightlight imageries.

The Xinjiang Uygur Autonomous Region is the core economic area of the “Silk Road Economic Belt”. The urbanization of this region plays a highly important role in economic and cultural communications between China, Central Asia and Europe. However, the influencing factors of urbanization in this region remain unclear. In this study, we presented a new modified thresholding method to extract the urban built-up areas from two nightlight remote sensing data sources, i.e., the DMSP/OLS and VIIRS/DNB nightlight imageries. Then, geographical detectors and hierarchical partitioning analysis were used to test the influences of anthropogenic and geographic environmental factors on urbanization. Our results showed that the relative error between the actual and the extracted urban built-up areas calculated using our method ranged from -0.30 to 0.27 in two biggest sample cities (Urumqi and Karamay) over the last 27 years. These errors were lower than those calculated by using the traditional method (-0.66 ≤ relative error ≤ -0.11). The expansion of urban built-up areas was greater in the northern regions than the southern regions of Xinjiang, as well as was greater in large cities than small and medium-sized cities. The influence of anthropogenic factors on urbanization has continually decreased over the past 27 years, while the influence of geographical environmental factors has increased. Among all influencing factors, fixed asset investment, topographic position index and per capita possession of water resources have the high contributions on urbanization, accounting for 18.75%, 15.62% and 14.18% of the variance of urbanization, respectively. Here, we provided a new method for studying urbanization by using remote sensing data. Our results are helpful for understand the driving factors of urbanization, and they provide guidance for the sustainable economic development of the Xinjiang Uygur Autonomous Region.

Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China.

Urbanization in China is progressing rapidly and continuously, especially in the newly developed metropolitan areas. The Google Earth Engine (GEE) is a powerful tool that can be used to efficiently investigate these changes using a large repository of available optical imagery. This work examined land-cover changes in the central region of the lower Yangtze River and exemplifies the application of GEE using the random forest classification algorithm on Landsat dense stacks spanning the 30 years from 1987 to 2017. Based on the obtained time-series land-cover classification results, the spatiotemporal land-use/cover changes were analyzed, as well as the main factors driving the changes in different land-cover categories. The results show that: (1) The obtained land datasets were reliable and highly accurate, with an overall accuracy ranging from 88% to 92%. (2) Over the past 30 years, built-up areas have continued to expand, increasing from 537.9 km2 to 1500.5 km2, and the total area occupied by built-up regions has expanded by 178.9% to occupy an additional 962.7 km2. The surface water area first decreased, then increased, and generally showed an increasing trend, expanding by 17.9%, with an area increase of approximately 131 km2. Barren areas accounted for 6.6% of the total area in the period 2015–2017, which was an increase of 94.8% relative to the period 1987–1989. The expansion of the built-up area was accompanied by an overall 25.6% (1305.7 km2) reduction in vegetation. (3) The complexity of the key factors driving the changes in the regional surface water extent was made apparent, mainly including the changes in runoff of the Yangtze River and the construction of various water conservancy projects. The effects of increasing the urban population and expanding industrial development were the main factors driving the expansion of urban built-up areas and the significant reduction in vegetation. The advantages and limitations arising from land-cover mapping by using the Google Earth Engine are also discussed.