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China's rapid urbanization has been accompanied by serious urban sprawl. Instead of measuring the physical urban boundaries (PUBs), most of existing studies in China rely on yearbook statistics to describe the growth of urbanized area; therefore, the understanding of the actual form and quantity of urban sprawl are restrained. As the statistical unit is generally at or above the county level, these studies tend to omit the lower-level "larger towns". This paper discusses the measurement of urban sprawl and compactness using multi-source data on the GIS platform through the case study of the Wen-Tai region in China. GlobeLand30 remote sensing image data, vector road network data, NPP/VIIRS nighttime light data, and points of interest (POIs) data are adopted. The new method enhances the identification of built-up areas in larger towns. Besides, the 2020s' PUBs of this region, data for 2010 and 2000 are retraced to assess the urban expansion rate, and two approaches are used to discuss the urban growth pattern. Additionally, a compactness model is constructed from four dimensions, i.e., the compactness of external contour, accessibility of road network, land-use intensity, and functional diversity, by which a high-resolution visual analysis tool is created for the provincial government to monitor urban sprawl. [ABSTRACT FROM AUTHOR]

Reasonable quantitative assessment on urban ecosystem health is conducive to the sustainable development of the economy and human society. This paper quantitatively evaluated the impact of land use change on ecosystem services and ecosystem health by building a comprehensive evaluation system (vigor–organization–resilience–ecosystem services), and then analyzed the spatial-temporal pattern, evolution characteristics, and driving factors in the Shanghai–Hangzhou Bay area (SHB) over the 2000–2015 period. The results show that: the area of cropland and forest accounted for more than 65% and was mainly converted into built-up land in the past 15 years. The overall ESV showed a trend of first increasing and then decreasing. Forest accounted for the largest proportion of the total ESV, more than 60% in each year. The ecosystem health value of SBH decreased from 2000 to 2015. At the city scale, the ecosystem health was significantly deteriorated. All cities reached the lowest value by 2015. At the districts/counties scale, the number with the relatively well or well level decreased from 32 in 2000 to 20 in 2015 by 24.64% of the total area. Overall, inland regions of SBH had better ecosystem health situation than coastal areas. The rapid urbanization of population and economy were driving factors for the decline of the ecosystem health. The indicator system of integrating the vigor, organization, resilience, and ecosystem service for ecosystem health assessment is a potential method which could provide a quantitative and comprehensive way for evaluating ecological and environmental effects in the future. [ABSTRACT FROM AUTHOR]

Urbanization level is a key indicator for socioeconomic development and policy making, but the measurement data and methods need to be discussed further due to the limitation of a single index and the availability and accuracy of statistical data. China is urbanizing rapidly, but the urbanization level at the county scale remains a mystery due to its complexity and lack of unified and effective measurement indicators. In this paper, we proposed a new urbanization index to measure the Chinese urbanization level at the county scale by integrating population, land, and economic factors; by fusing remote sensing data and traditional demographic data, we investigated the multi-dimensional unbalanced development patterns and the driving mechanism from 1995 to 2015. Results indicate that: The average comprehensive urbanization level at the Chinese county scale has increased from 31.06% in 1995 to 45.23% in 2015, and the urbanization level in the permanent population may overestimate China's urbanization process. There were significant but different spatial and temporal dynamic patterns in population, land, and economic levels as well as at a comprehensive urbanization level. The comprehensive urbanization level shows the pattern of being high in the south-east and low in the north-west, divided by "Hu line". The urbanization of registered populations presents high in the northern border and the eastern coastal areas, which is further strengthened over time. Economic urbanization based on lighting data presents high in the east and low in the west. Land urbanization based on remote sensing data shows high in the south and low in the north. The registered population urbanization level is lower than economic and land urbanization. County urbanization was driven by large population size, reasonable industrial structure, and strong government capacity; 38% and 59% of urbanization levels can be regarded as the key nodes of the urbanization process. When the urbanization rate is lower than 38%, the secondary industry plays a strong role in powering urbanization; when the urbanization rate is higher than 38% but less than 59%, the promotion effect of the tertiary industry is more obvious, and the secondary industry is gradually weakened. When the urbanization rate exceeds 59%, the tertiary industry becomes the major driver. [ABSTRACT FROM AUTHOR]

With rapid urban development in China in the last two decades, the three-dimensional (3D) characteristic has been the main feature of urban morphology. However, the vast majority of researches of urban growth have focused on the planar area (two-dimensional (2D)) expansion. Few studies have been conducted from a 3D perspective. In this paper, the 3D urban expansion of the Yangzhou City, Jiangsu Province, China from 2003 to 2012 was evaluated based on Geographical Information System (GIS) tools and high-resolution remote sensing images. Four indices, namely weighted average height of buildings, volume of buildings, 3D expansion intensity and 3D fractal dimension are used to quantify the 3D urban expansion. The weighted average height of buildings and the volume of buildings are used to illustrate the temporal change of the 3D urban morphology, while the other two indices are used to calculate the expansion intensity and the fractal dimension of the 3D urban morphology. The results show that the spatial distribution of the high-rise buildings in Yangzhou has significantly spread and the utilization of the 3D space of Yangzhou has become more efficient and intensive. The methods proposed in this paper laid a foundation for a wide range of study of 3D urban morphology changes. [ABSTRACT FROM AUTHOR]

The landscape pattern is affected by human activities and the natural environment. China's urbanization has been a great achievement in the past 30 years, but also it has caused a variety of environmental problems. The landscape of the mountain valley area is particularly affected by human activities and the natural environment. The rise of remote sensing technologies has promoted a better understanding of ecological process, and the quantitative study on landscape dynamics has received more and more considerable attention. In this paper, four years from 2000, 2005, 2010, and 2015 were selected as the research time. Using Landsat TM/ETM remote sensing image, DEM, and socioeconomic data, we calculated the landscape pattern indices in four districts (Lishi, Fangshan, Liulin, and Zhongyang) of Luliang City on patch scale, land cover scale and landscape scale respectively. Thus, the change of landscape pattern in four districts from 2000 to 2015 were obtained. The results showed that: 􀃦 the landscape in the mountain valley area has become more and more fragmented in the past 15 years (2000-2015); 􀃧 the landscape dynamics of the main and tributary catchment areas of the Sanchuan River were different from that of urban development, which is closely related to the development stages of urban and rural areas; 􀃨 there were differences in landscape changes between different topographic and landform units, and the landscape at higher altitudes was more vulnerable to disturbance. This study can provide some reference for the ecological restoration and management on landscape scale in the mountain and valley areas of the north and northwest China. [ABSTRACT FROM AUTHOR]

In the urbanization of China, construction land is generally distributed as a continuous area, and its change shows distinct spatial aggregation leading to the strong spatial autocorrelation, which is more obvious in remote sensing imagery with a fine spatial resolution. Based on the TripleSat-2 multi-spectral remote sensing images covering Nanjing city in 2016 and 2017, the paper compares and analyzes the performance of remote sensing change detection of construction land after we introduced the spatial autocorrelation index of the change vector as the image feature. Firstly, the three typical spatial autocorrelation indices of local G, Moran's I and Geary's C are extracted, and then the optimal spatial range of Lag and the optimal autocorrelation index suitable for the change detection are determined. The results showed that: (1) The spectral change vector had significant positive correlation. (2) The optimal range of Lag can be determined by global Moran's I and semi-variance. (3) We used the local G and local Moran's I with the spectral change vectors to increase the F1 score of the change detection, and the local G showed the better performance. (4) Using the local G with the optimal range of Lag as additional image features, the F1 scores were 20% higher than that using only the spectral change vectors. Fusing local spatial autocorrelation information especially of the multi- scale local G as the additional image features can effectively improve the change detection accuracy of construction land in remfote sensing imagery. [ABSTRACT FROM AUTHOR]

Impervious surfaces are the most significant feature of human settlements. Timely, accurate, and frequent information on impervious surfaces is critical in both social-economic and natural environment applications. Over the past 40 years, impervious surface areas in China have grown rapidly. However, annual maps of impervious areas in China with high spatial details do not exist during this period. In this paper, we made use of reliable impervious surface mapping algorithms that we published before and the Google Earth Engine (GEE) platform to address this data gap. With available data in GEE, we were able to map impervious surfaces over the entire country circa 1978, and during 1985–2017 at an annual frequency. The 1978 data were at 60-m resolution, while the 1985–2017 data were in 30-m resolution. For the 30-m resolution data, we evaluated the accuracies for 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Overall accuracies reached more than 90%. Our results indicate that the growth of impervious surface in China was not only fast but also considerably exceeding the per capita impervious surface area in developed countries like Japan. The 40-year continuous and consistent impervious surface distribution data in China would generate widespread interests in the research and policy-making community. The impervious surface data can be freely downloaded from http://data.ess.tsinghua.edu.cn. [ABSTRACT FROM AUTHOR]

Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models. [ABSTRACT FROM AUTHOR]

Sri Lanka is experiencing speedy urbanization by converting the agriculture land and other natural land cover into built-up land. The urban population of Sri Lanka is expected to reach to 60% by 2030 from 14% in 2010. The rapid growth in urban population and urban areas in Sri Lanka may cause serious socioeconomic disparities, if they are not handled properly. Thus, planners in Sri Lanka are in need of information about past and future urban growth patterns to plan a better and sustainable urban future for Sri Lanka. In this paper, we analyzed the characteristics of past land use and land cover trends in Matara City of Sri Lanka from 1980 to 2010 to assess the historic urban dynamics. The land use change detection analysis based on remote sensing datasets reveal that the conversion of homestead/garden and paddy into urban land is evident in Matara City. The historic urban trends are projected into the near future by using SLEUTH urban growth model to identify the hot spots of future urbanization and as well as the urban growth patterns in Matara City up to the basic administrative level, i.e., Grama Niladari Divisions (GND). The urban growth simulations for the year 2030 reveal that 29 GNDs out of 66 GNDs in Matara City will be totally converted into urban land. Whereas, 28 GNDs will have urban land cover from 75% to 99% by 2030. The urban growth simulations are further analyzed with respect to the proposed Matara city development plan by the Urban Development Authority (UDA) of Sri Lanka. The results show that the UDA's city development plan of Matara will soon be outpaced by rapid urbanization. Based on the calibration and validation results, the SLEUTH model proved to be a useful planning tool to understand the near future urbanization of Sri Lankan cities. [ABSTRACT FROM AUTHOR]

Urbanization has proceeded at an unprecedented speed in China during the last 20 years, resulting in extensive natural landscapes being transformed into impervious surface. The “ghost city” phenomenon has emerged due to the unreasonable urban expansion which far exceeds the actual demand of human habitat. Previously, few research studies have provided objective and sufficient knowledge with regard to identify “ghost cities” and their spatial distribution. In this paper, we proposed an effective and feasible framework to monitor and evaluate “ghost cities” utilizing nighttime light imagery obtained from day-night band (DNB) of Visible Infrared Imaging Radiometer Suite (VIIRS). We established a “ghost city” index (GCI) to quantify the intensity of the phenomenon in the northeast of China, and analyzed the spatial pattern of “ghost cities” for different GCI classes. Our results indicate that the intensity of “ghost city” phenomenon decrease from regions adjacent to the border to interior areas, whilst regions with extremely high GCI are mostly districts and county cities. Tests of typical regions show that non-lit built-up area for high GCI regions is spatially clustered and low population regions have a high tendency to suffer from the “ghost city” phenomenon. Therefore, our findings provide a spatial-explicit insight into the “ghost city” phenomenon, and consequently can be beneficial to assist sustainable urban planning. [ABSTRACT FROM AUTHOR]

Detailed studies on the spatiotemporal patterns of urban agglomeration in the Middle Yangtze River Basin (MYRB) are rare. This paper analyzed the spatiotemporal patterns of urbanization in the MYRB using multi-temporal remote sensing data circa 2000, 2008 and 2016 integrated with geographic information system (GIS) techniques and landscape analysis approaches. A multi-level analysis of the rate and intensity, type as well as the landscape changes of urban expansion at regional, prefectural and inner-city levels was performed. Results show that the MYRB experienced rapid urban expansion with an annual expansion rate of 3.199%, especially in the Chang-Zhu-Tan and Poyang Lake metropolitan areas. The small and medium cities presented faster urban expansion than the larger cities with annual growth rates three times the average level. Urban expansion within the three capital cities was further analyzed in detail. It is found that outlying expansion and edge-expansion were the dominant growth patterns at all the three levels. Although urbanization in the MYRB has a remarkable increase in the past sixteen years, its annual growth rate of urban land expansion has fallen behind the three other large urban agglomerations in China as a result. Finally, the spatial evolution of the socioeconomic structure of the MYRB was further explored. It indicated that urban land was distributed mainly along the "northwest-southeast" direction and that the economic spatial interactions among cities showed a pattern of "multi-polarization and fragmentation", which illustrates the weak radiative driving forces of the central cities. The MYRB urban agglomeration faces a great challenge to manage trades-offs between narrowing the intra-regional disparity and maintaining synergetic development among cities. [ABSTRACT FROM AUTHOR]

This paper presents some preliminary results from research on monitoring the urban growth of Shenzhen in China. Agriculture is still the pillar of national economies in many countries including China. Thus, agriculture contributes to population growth. Population growth follows either exponential or logistic growth models. These models can be examined using a time-series of geospatial data, mainly historical earth observation imagery from satellites such as LANDSAT. Such multitemporal data may provide insights into settlement analysis as well as on population dynamics and hence, quantify the loss of agricultural land. In this study, LANDSAT data of ten dates, at approximately five yearly intervals from 1977 to 2017 were used. The remote sensing techniques used for analysis of data for 40 years were image selection, then followed by geometric and radiometric corrections and mosaicking. Also, classification, remote sensing image fusion, and change detection methods were used. This research extracted the information on the amount, direction, and speed of urbanization, and hence, the number of hectares of agricultural land lost due to urban expansion. Several specific elements were used in the descriptive model of landscape changes and population dynamics of the city of Shenzhen in China. These elements are: i) quantify the urban changes, from a small town (37.000 people in the early 1970's) to the megalopolis of around 20 million habitants today. ii) Examining the rate of urban extension on the loss of agricultural landscape and population growth. iii) The loss of food production was analysed against the economic growth in the region. iv) The aspects of loss of agricultural land, area of built-up urban land, and increase in population are studied quantitatively, by the temporal analysis of earth observation geospatial data. The experimental results from this study show that the proposed method is effective in determining loss of agricultural land in any city due to urbanization. It can be used by town planner and other stakeholders such as land surveyors and agriculture experts to mitigate the mushrooming of unplanned settlements in many town / villages and loss of land for agriculture which might cause problems in food security. [ABSTRACT FROM AUTHOR]

Since twentieth Century, the process of economic globalization has made great progress, and Southeast Asia has developed rapidly under the background of international industrial transferring. In this paper, the 6 important nodes cities in China - Indochina Peninsula along the economic corridor are took as study area. The main data is time series Landsat data. The method of object-oriented random forest classification was used to extract the classification results of study area from 2000 to 2015. The urban expansion of the node cities was evaluated by calculating the expansion speed of the impervious surface and the landscape pattern metrics. The results indicated that the method of object oriented random forest classification can effectively extract the urban impervious surface. the overall accuracy is over 81%, and the Kappa coefficient is over 0.82. In the past 15 years, the expansion speed of Vientiane city was fastest in 6 countries. The area of urban impervious surface expanded 8 times than that of 2000.The pattern of expansion is summarized as "gather first-diffuse then", "diffuse first-gather then" and "gather". Overall, the process of urbanization of these cities are still in the rising period. [ABSTRACT FROM AUTHOR]

China's cities have been undergoing rapid and intense urbanization processes in the past few decades. Shandong is a coastal province which is located in East China with big economy and population scales, and which also plays an important role in the rapid process of China's modernization. The DMSP/OLS dataset has been widely used for the urban development assessments in long time-series and large spatial scales situations. In this paper, we used a time series of nighttime light data to estimate the landscape spatial pattern changes of cities in Shandong province from 1994 to 2012. Nine landscape metrics were calculated and analyzed to figure out the spatial patterns of urban area developments of the cities in Shandong province. The landscape metrics include the number of patches (NP), the landscape total area (TA), the aggregation index (AI), the largest patch index (LPI), the mean patch area (AREA_MN), the landscape shape index (LSI), the total edge length (TE), the edge density (ED), and the mean radius of gyration (GYRATE_MN). The experimental results reveal that, in 1994-2012, the total urban area of cities in Shandong province expanded for 1.17 times, the average urban area increased by about 93.00%, the average annual growth rate of the TE metric is 2.67%, while the ED metric decreased about 1.44% annually. Bigger cities in this area show relative slower urbanization development processes, such as Jinan and Qingdao. Coastal cities represented much more rapid expansion velocities than inland cities. In the middle area of Shandong province, the connectivity between developed urban areas was constantly increased. [ABSTRACT FROM AUTHOR]

A large number of studies have dealt with the driven forces of land expansion, in which the remote sensing data and statistical data are most commonly used. The recent progress based on the statistical data have not been fully tested and discussed by the remote sensing data, and the remote sensing data used in the previous studies are usually interpreted within certain areas which is not convenient for global comparison. In this paper, the 30-m GlobalLand Cover Dataset (GlobeLand30) and socioeconomic data from 2000 to 2010 are adopted to investigate the factors driving impervious surface expansion in China based on a multilevel regression model. The GlobeLand30 provides a world-wide data framework which has a sound basis for regional comparison research. The variables are selected according to the existing research. Most, but not all, results are consistent with the previous studies when using impervious surface data of GlobeLand30. The main findings are: (1) the market demand caused by economic development, such as the increase in GDP from 2000 to 2010, plays a positive role in the expansion of developed land; (2) the land supply, as reflected by the ratio of the total of land transfer fees to fiscal revenue, also has a positive effect on the increase in impervious surfaces; (3) the percentage of the increase by private workers to the increase in total workers and certain other frequently-used variables are not relevant after controlling for land demand- and supply-related variables; and (4) the growth in impervious surfaces is related to the amount of the cultivated land, which implies the necessity for a more stringent farmland protection policy. Considering the need to compare across regions, we suggest that GlobeLand30 should be used for more studies to better understand the driving forces of land expansion. [ABSTRACT FROM AUTHOR]

Rapid urbanization has resulted in great changes in rural landscapes globally. Using remote sensing data to quantify the distribution of rural settlements and their changes has received increasing attention in the past three decades, but remains a challenge. Previous studies mostly focused on the residential changes within a grid or administrative boundary, but not at the individual village level. This paper presents a new change detection approach for rural residential settlements, which can identify different types of rural settlement changes at the individual village level by integrating remote sensing and Geographic Information System (GIS) analyses. Using multi-temporal Landsat TM image data, this approach classifies villages into five types: “no change”, “totally lost”, “shrinking”, “expanding”, and “merged”, in contrast to the commonly used “increase” and “decrease”. This approach was tested in the Beijing metropolitan area from 1984 to 2010. Additionally, the drivers of such changes were investigated using multinomial logistic regression models. The results revealed that: (1) 36% of the villages were lost, but the total area of developed lands in existing villages increased by 34%; (2) Changes were dominated by the type of ‘expansion’ in 1984–1990 (accounted for 43.42%) and 1990–2000 (56.21%). However, from 2000 to 2010, 49.73% of the villages remained unchanged; (3) Both topographical factors and distance factors had significant effects on whether the villages changed or not, but their impacts changed through time. The topographical driving factors showed decreasing effects on the loss of rural settlements, while distance factors had increasing impacts on settlement expansion and merging. This approach provides a useful tool for better understanding the changes in rural residential settlements and their associations with urbanization. [ABSTRACT FROM AUTHOR]

Wetlands are important ecosystems as they are known as the "kidney of the earth". Particularly, urban wetlands play an important role in providing both natural and social beneficial services. However, urban wetlands are suffering from various human impacts, such as excessive land use conversion, air and water pollution, especially those in developing countries undergoing rapid industrialization and urbanization. Therefore, it is of great necessity to derive timely biomass information for optimal design, management and protection of urban wetlands. In this paper, we develop a set of models for estimating above ground biomass (AGB) in Xixi National Wetland Park in Hangzhou, China by using optical images and Synthetic Aperture Radar (SAR) images. A series of vegetation indices (VIs) derived from optical data is introduced along with spectral data. The modeling methods consist of (1) curve estimation; (2) linear regression for multivariable model; (3) Back Propagation Neural Network (BPNN) modeling. Curve estimation is a combination of linear and nonlinear regressions. It is applied to generate AGB models from a single variable including Normalized Difference Vegetation Index (NDVI) and radar backscatter coefficient. The models are then compared via three accuracy metrics. According to the results, SAR models generally show better accuracy than optical models and BPNN models show the greatest accuracy among all the models. The BPNN model from the combination of Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and European Remote-Sensing Satellite-2 (ERS-2) SAR (Synthetic Aperture Radar) image has the least root mean square (RMSE, 0.396 kg/m2), least mean absolute error (MAE, 0.256 kg/m2) and the greatest correlation coefficient (0.974). The results indicate that AGB can be estimated by integrating optical and SAR imagery. Four maps of AGB are derived to illustrate the distribution of AGB in the study area. The total AGB in the study area is estimated to be between 165,000 and 210,000 kg/m2. [ABSTRACT FROM AUTHOR]

Historically, change detection reviews have examined and categorized algorithms based on their techniques for the remote sensing community. Here, we synthesize urban land change algorithms by the types of information they provide to a diverse and growing set of user communities. Two goals of the paper are first to synthesize past and current change detection studies to examine urban land change to help users of remote sensing algorithms understand and navigate the vast variety of available methods, and second to identify gaps in knowledge for the urban remote sensing community. We analyzed 644 peer-reviewed research papers published in English-language journals and conducted a systematic review of urban land change algorithms. All papers included in our study focus on urban land change ; studies that concentrate on single-date urban classification or mapping for one point in time without an explicit urban land change component are not included in this analysis. The review showed five key results and knowledge gaps. First, most urban change detection algorithms are being developed and applied for only a few regions, with 75% of studies focused on high or upper-middle-income countries and the majority of these on China or the United States. This suggests a major gap in geographic coverage as well as the need for more studies on cities in low and lower-middle-income countries. Second, the results show that 41% of the algorithms have been developed or applied for cities of over 5 million inhabitants. This focus on large cities is problematic given that only 11% of the world's urban population lives in cities with populations greater than 5 million and that most future urban growth will occur in small- and medium-sized towns and cities with populations of fewer than 1 million people. Third, our analysis shows that 62% of the studies use three or fewer time points to measure urban land change with an average study length of 17 years. Since rapidly growing urban areas are highly dynamic, this suggests that existing algorithms using only a few time points are likely missing urban transitions that can only be captured with high temporal frequency analysis. Fourth, we find that urban expansion is the most commonly monitored type of urban land change. Comparatively fewer studies characterized intra-urban change or three-dimensional structural change. Fifth and finally, the results show that an overwhelming majority—87%—of all studies identify only one urban class, highlighting a need for more studies that distinguish intra-urban variation and differentiate multiple urban classes. Our analysis shows that it is very difficult—nearly impossible—to compare across algorithms. Thus, for users of urban land change information, it is difficult to navigate the literature and know which algorithms are most appropriate for a particular use. Taken together, this points to the need for improved reproducibility, replicability, and comparability of studies in order to help harmonize urban land change information across regions and countries. This is especially important given the growing user communities of urban land change products and analysis, especially from policy and practice. • We conduct a systematic review of the urban land change algorithm literature. • Most studies monitor urban change at low temporal frequencies. • The majority of urban land change is characterized using only one urban class. • Over 75% of studies focus on high or upper-middle level income countries. • Key knowledge gaps include in geographic coverage, city size, and land transitions. [ABSTRACT FROM AUTHOR]

Cities are diverse, with their formation and expansion having various driving forces. Xinjiang Production and Construction Corps (XPCC) cities represent an emerging category in China's urban system. They have evolved into a special organisation integrating 'party, government, military, and enterprises' and are located in Xinjiang, the frontier region of north-western China. Investigation of such urban characteristics is helpful to enrich our diverse cognitions on cities. The purpose of this paper is to trace the growth history of XPCC cities and explore their evolution-related characteristics, modes, and driving mechanisms in terms of spatial morphology and landscapes in the process of urban expansion by utilising remote sensing interpretation data. This study led to four main conclusions. (1) The role of the XPCC in maintaining the long-term stability and prosperity of the frontier is increasingly valued across the nation, thus leading to a major increase in the quantity and quality of XPCC cities. (2) These cities have a unique spatial layout around two deserts and the border characterised by 'two rings and one line'; however, their distribution is relatively scattered. (3) The expansion of XPCC cities has become prominent along with the restructuring of land use, with wasteland reclaiming being a salient attribute of their built-up land expansion, while also protecting cultivated land. Most cities reveal a more complex and less compact morphology. (4) The external government's promotion and regulation, the basic shaping of the natural geographical environment, the internal boosting of economic increase and industrial restructuring, and the special reshaping of the XPCC-local relationship are key driving factors behind the urban temporal-spatial evolution. Overall, this study supplements current knowledge on frontier cities within the Chinese context and thus helps to improve understanding of the urban theoretical research of government behaviours and the multi-dynamics of urban development. [ABSTRACT FROM AUTHOR]

Although the mapping of spatiotemporal patterns of urban expansion has been widely studied, relatively little attention has been paid to detailed comparative studies on spatiotemporal patterns of urban growth at the regional level over a relatively longer timeframe. This paper was based on multi-sensor remote sensing image data and employs several landscape metrics and the centroid shift model to conduct a multi-angle quantitative analysis on urban expansion in Beijing, Tianjin and Tangshan (Jing-Jin-Tang) in the period from 1970-2013. In addition, the impact analysis of urban growth on land use was adopted in this research. The results showed that Beijing, Tianjin and Tangshan all experienced rapid urbanization, with an average annual urban growth rate of 7.28%, 3.9%, and 0.97%, respectively. Beijing has especially presented a single choropleth map pattern, whereas Tianjin and Tangshan have presented a double surface network pattern in orientation analysis. Furthermore, urban expansion in Beijing was mainly concentrated in Ring 4 to Ring 6 in the northwest and southeast directions, whereas the major expansion was observed in the southeast in Tianjin, primarily affected by dramatic development of Binhai New Area and Tianjin South Railway Station. Naturally, the urban expansion in Tangshan was significantly influenced by the expansion of Beijing and was primarily southwestward. The hot-zones of urbanization were observed within the ranges of 7-25 km, 6-18 km, and 0-15 km, accounting for 93.49%, 89.44% and 72.44% of the total expansion area in Beijing, Tianjin and Tangshan, respectively. The majority of the newly developed urban land was converted from cultivated land and integrated from other built-up land over the past four decades. Of all new urban land in the Beijing, Tianjin and Tangshan, more than 50% was converted from cultivated land, and there was a general tendency for smaller cities to have higher percentages of converted land, accounting for 50.84%, 51.19%, and 51.58%, respectively. The study revealed significant details of the temporal and spatial distributions of urban expansion in Beijing, Tianjin and Tangshan and provided scientific support for the collaborative development of the Beijing, Tianjin and Hebei (Jing-Jin-Ji) regions. [ABSTRACT FROM AUTHOR]

Due to the rapid urbanization of China, many villages in the urban fringe are enveloped by ever-expanding cities and become so-called urban villages (UVs) with substandard living conditions. Despite physical similarities to informal settlements in other countries (e.g., slums in India), UVs have access to basic public services, and more importantly, villagers own the land legitimately. The resulting socio-economic impact on urban development attracts increasing interest. However, the identification of UVs in previous studies relies on fieldwork, leading to late and incomplete analyses. In this paper, we present three scene-based methods for detecting UVs using high-resolution remotely sensed imagery based on a novel multi-index scene model and two popular scene models, i.e., bag-of-visual-words and supervised latent Dirichlet allocation. In the experiments, our index-based approach produced Kappa values around 0.82 and outperformed conventional models both quantitatively and visually. Moreover, we performed multitemporal classification to evaluate the transferability of training samples across multitemporal images with respect to three methods, and the index-based approach yielded best results again. Finally, using the detection results, we conducted a systematic spatiotemporal analysis of UVs in Shenzhen and Wuhan, two mega cities of China. At the city level, we observe the decline of UVs in urban areas over the recent years. At the block level, we characterize UVs quantitatively from physical and geometrical perspectives and investigate the relationships between UVs and other geographic features. In both levels, the comparison between UVs in Shenzhen and Wuhan is made, and the variations within and across cities are revealed. [ABSTRACT FROM PUBLISHER]

Accelerating urbanization all over the world has renewed interdisciplinary interest and public concerns over sustainable urban forms. Despite abundant literature on the concentric model and its application to urban land-use patterns, a rigorous and straightforward methodology to characterize the model quantitatively in space is especially needed. In this paper we present a diagramatic method and refine five indicators to measure and clarify the circular pattern of urban expansion. With remote sensing data, the approach has been examined for the sample set of fifty-one cities in the Yangtze River Delta (YRD) region of China. The five indicators adopted illustrated that most of YRD cities display a compact centralized form with a relatively small matured core area and are undergoing fast expansion. Furthermore, we have found distinctive local models of density gradient in built-up areas, shaped by urban population size and local institutional structures. The preliminary results related to the methodology are inspiring, which should be useful for elucidating urban growth trends and for decision making. [ABSTRACT FROM AUTHOR]

Sustainable development requires monitoring the state and changes of the city and providing the appropriate information to users anytime and anywhere. This paper takes Ningbo City as the research area, by utilizing two temporal (March 25, 2012 and November 25, 2013) ZY-3 satellite remote sensing data. 3S technology is used for urban grid management. The remote sensing information extraction of Ningbo City includes: extraction of building change information, extraction of green space change information, extraction of water information and analysis of eutrophication correspondingly. When extracting change information, we take "change information" as a special kind of "geographic information" to study the characteristics of different bands in multi-temporal data, and follow the first law of geography, namely adjacent similar principle. The extracted raster information is further converted into GIS vector format data as a basis for dynamic monitoring of Ningbo Urban Management systems: on the one hand, it can meet the demands of multi-source spatial data analysis (such as: overlay analysis, buffer analysis, etc.); on the other hand, it could meet the requirements of daily urban management. Dynamic monitoring system of Ningbo city management adopts the urban grid management mode. Based on GIS and GPS, grid management can satisfy the urban management mode--someone bear responsibility within the grid, somebody do the task under the supervision of the lattice, and at the same time play the role of remote sensing field surveying. To some extent, integrated 3S technology and urban grid management is a practical alternative of minimizing the uncertainty of remote sensing data and information extraction. With multi-scale and multi-dimensional remote sensing data, 3S integration and the urban grid management can monitor the urban state and its spatial-temporal changes. It's helpful for discovery and analysis of urban problems about resources, environment, ecology and disaster, from phenomena to nature, and it is a necessary part of sustainable urbanization. [ABSTRACT FROM AUTHOR]

Air quality problems caused by atmospheric particulate have drawn broad public concern in the global scope. In the paper, the spatiotemporal distributions of fine particle (PM2.5) and inhalable particle (PM10) concentrations estimated with the artificial neural network (ANN) over China during 2006 to 2010 have been discussed. Most high PM10 concentration appears in Xinjiang, Qinghai, Gansu, Ningxia, Hubei, and parts of Inner Mongolia. The distribution of PM2.5 concentration is consistent with China's three gradient terrains. The seasonal variations of PM2.5 and PM10 concentrations both indicate that they are higher in north China in spring and winter, lowest in summer. In autumn, most provinces in south China appear high value. In particular, high PM2.5 concentration appears in the southeast coastal cities while high PM10 concentration prefers the central regions in south China. On this basis, seasonal Mann-Kendall test method is utilized to analyze the short-term trends. The results also show significant changes of PM2.5 and PM10 concentrations of China in the past 5 years, and most provinces present the tendency of reduction (3-5 μg/m for PM2.5 and 10-20 μg/m for PM10 per year) while a fraction of provinces appear the increasing trend of 8-16 μg/m (PM2.5) and 16-30 μg/m (PM10). Simultaneously, PM2.5 population exposure is discussed with the combination of population density-gridded data. Municipalities get much higher exposure level than other provinces. Shanghai suffers the highest population exposure to PM2.5, followed by Beijing and then Tianjin, Jiangsu province. Most provincial capitals, such as Guangzhou, Nanjing, Chengdu, and Wuhan, face much higher exposure level than other regions of their province. Moreover, the PM2.5 exposure situation is more serious in southeast than northwest regions for Beijing-Tianjin-Hebei region. Also, per capita PM2.5 concentration and population-weighted PM2.5 concentration are calculated. The former shows that the high-level regions distribute in Guangdong, Shanghai, and Tianjin, while the latter in Hebei, Chongqing, and Shandong provinces. Further studies may consider optimizing concentration estimation model and use it to discuss the effects of particulate matters on human health. [ABSTRACT FROM AUTHOR]

Urban villages (UVs) are a typical informal settlement in China resulting from the rapid urbanization in recent decades. Their formation and demolition are attracting increasing interest. In the remote sensing community, UVs have been detected based on hand-crafted features. However, the hand-crafted features just consider one or several characteristics of UVs, and ignore many effective cues hiding in the image. Recently, deep learning has been used to automatically learn suitable feature representations from a huge amount of data, without much expertise or effort in designing features. Motivated by its great success, this paper aims to use deep learning for detecting UVs. Because of the scarce labeled samples, this paper presents a novel unsupervised deep learning method to learn a data-driven feature. Experiments show the data-driven feature obtained with the proposed method outperform the existing unsupervised deep neural networks, and achieve results comparable to that obtained using the best hand-crafted features. [ABSTRACT FROM AUTHOR]

• Day/Night remote sensing scheme was used to monitor coastal zone eco-environment. • The feasibility of large-scale RSEI was explored based on medium-resolution imagery. • Reveals the ecological changes in coastal zone at different urbanization intensities. With the rapid development of urbanization and population migration, since the 20th century, the natural and eco-environment of coastal areas have been under tremendous pressure due to the strong interference of human response. To objectively evaluate the coastal eco-environment condition and explore the impact from the urbanization process, this paper, by integrating daytime remote sensing and nighttime remote sensing, carried out a quantitative assessment of the coastal zone of China in 2000–2019 based on Remote Sensing Ecological Index (RSEI) and Comprehensive Nighttime Light Index (CNLI) respectively. The results showed that: 1) the overall eco-environmental conditions in China's coastal zone have shown a trend of improvement, but regional differences still exist; 2) during the study period, the urbanization process of cities continued to advance, especially in seaside cities and prefecture-level cities in Jiangsu and Shandong, which were much higher than the average growth rate; 3) the Coupling Coordination Degree (CCD) between the urbanization and eco-environment in coastal cities is constantly increasing, but the main contribution of environmental improvement comes from non-urbanized areas, and the eco-environment pressure in urbanized areas is still not optimistic. As a large-scale, long-term series of eco-environment and urbanization process change analysis, this study can provide theoretical support for mesoscale development planning, eco-environment condition monitoring and environmental protection policies from decision-makers. [ABSTRACT FROM AUTHOR]

• Tiangong-2 WIS was used as a single source of RSEI inputs in 2018. • Calculated nighttime light index to describe urbanization development. • Relationship between urbanization and environment was investigated in Yanqin Basin. While multi-source remote sensing technology has the advantage of accurate and objective quantification, an understanding of the coupling and coordination between regional urbanization and ecological environment is the basis for achieving regional sustainable development. This paper builds the Remote Sensing Ecological Index (RSEI) based on Landsat data and Tiangong-2 WIS, using multi-source remote sensing data to comprehensively evaluate the coupling and coordination relationship between urbanization and ecological environment with a coupling coordination degree model in China's typical arid area of Yanqin Basin during 2000–2018. The results show the RSEI index has a certain rationality and superiority in assessing the ecological environment. In 2000–2018, the ecological environment of the Yanqi Basin was optimized, the RSEI index increased by 23.06%, and the overall ecological environment was at a good level (0.6–0.8). At the same time, the urbanization level of each town had been significantly improved in Yanqi Basin. Through the urban center of gravity transfer model, it was found that direction of urban development shifted to the southwest direction by 7.49 km, which is basically consistent with the position of poor ecological environment. Finally the current urbanization and ecological environment coupling relationship in urban areas of Yanqi Basin was found to exhibit a sluggish rate of urbanization with a moderate imbalance between urbanization with ecological environment. Therefore, when making decisions for planning the regional economic development, the concept of ecological relationships should be integrated to effectively protect and achieve a healthy and sustainable development of the region. [ABSTRACT FROM AUTHOR]

As the largest developing country, China has experienced dramatic urban expansion since the "reform and opening-up" policy started at the end of the 1970s. In this paper, we monitor three decades of urban expansion in China's 36 major cities, based on the spectral mixture analysis of remotely sensed satellite images. The results demonstrated that these major cities have expanded by 5.85 times from 1986 to 2015, with 15.51 km2 average expansion area per city per year. We found the urban expansion trajectories showed three different modes, i.e., exponential, linear and s-shaped, which were closely related to the city development level. In the old city zones, however, there was an interesting common tendency of the impervious surface area (ISA) first increasing and then decreasing, which could be largely attributed to the phenomenon of urban village reconstruction in China's cities. Based on the Granger Causality Test (GCT), the interaction between urban ISA and Gross Domestic Product (GDP) per capita (GDPPC) suggested that the former was the driver of the latter. Meanwhile, taking the Yangtze River as the division between north and south China, there exists a north–south territorial differentiation for the interaction between ISA and total population at the year-end (TP). [ABSTRACT FROM AUTHOR]

In the last few decades the magnitude and impacts of planetary urban transformations have become increasingly evident to scientists and policymakers. The ability to understand these processes remained limited in terms of territorial scope and comparative capacity for a long time: data availability and harmonization were among the main constraints. Contemporary technological assets, such as remote sensing and machine learning, allow for analyzing global changes in the settlement process with unprecedented detail. The Global Human Settlement Layer (GHSL) project set out to produce detailed datasets to analyze and monitor the spatial footprint of human settlements and their population, which are key indicators for the global policy commitments of the 2030 Development Agenda. In the GHSL, Earth Observation plays a key role in the detection of built-up areas from the Landsat imagery upon which population distribution is modelled. The combination of remote sensing imagery and population modelling allows for generating globally consistent and detailed information about the spatial distribution of built-up areas and population. The GHSL data facilitate a multi-temporal analysis of human settlements with global coverage. The results presented in this article focus on the patterns of development of built-up areas, population and settlements. The article reports about the present status of global urbanization (2015) and its evolution since 1990 by applying to the GHSL the Degree of Urbanisation definition of the European Commission Directorate General for Regional and Urban Policy (DG-Regio) and the Statistical Office of the European Communities (EUROSTAT). The analysis portrays urbanization dynamics at a regional level and per country income classes to show disparities and inequalities. This study analyzes how the 6.1 billion urban dwellers are distributed worldwide. Results show the degree of global urbanization (which reached 85% in 2015), the more than 100 countries in which urbanization has increased between 1990 and 2015, and the tens of countries in which urbanization is today above the global average and where urbanization grows the fastest. The paper sheds light on the key role of urban areas for development, on the patterns of urban development across the regions of the world and on the role of a new generation of data to advance urbanization theory and reporting. [ABSTRACT FROM AUTHOR]