1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China 2. Chinese Academy of Surveying and Mapping, Beijing 100830, China 3. Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China 4. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
China has undergone a rapid urbanization since the beginning of the 21st century. Urban expansion monitoring has become a hotspot in the field of geographical science. However, methods of urban boundary extraction were inconsistent, and the precision of previous urban boundary products is relatively low due to the coarse image resolution. In this paper, a method of high-precision and unified urban expansion monitoring and analysis of China's 31 provincial capitals was carried out based on high-resolution remote sensing images. First, the urban boundaries of 2000, 2005, 2010 and 2015 were extracted with a series of unified rules by urban landscape characteristics and geographical knowledge based on high-resolution images. Then, urban boundary result was compared with other urban boundary products based on low and mid-resolution images to assess the accuracy. Finally, urban size distribution and urban expansion were analyzed based on urban area and urban boundary results. Results showed that the proposed method of urban boundary extraction was superior to other researches. From 2000 to 2015, China's provincial capitals witnessed a rapid growth trend, and the total urban area increased by 90.15%; the provincial capitals system approximated size distribution of the rank-size law. Urban expansion had a significant regional difference. Urban expansion rate in the eastern region gradually slowed down, while that in the western and northeastern regions had an accelerating mode, and that in the central region expanded steadily. Beijing, Tianjin, Shanghai, Guangzhou and Chongqing, which were designated as the national central cities in 2010, ranked the top five of urban area size in 2015. The five cities increased by 82.45% during the 15 years, and the average annual urban expansion area was 30.66 km2. Urban area of Beijing, Tianjin, Shanghai, Guangzhou and Chongqing increased by about 30%, 100%, 100%, 60% and 200%, respectively. This research provides unified and high-precision spatial urban boundaries data and urban expansion results for local governments and the public, which are useful for scientific urban development and planning of China's urban system.
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WANG Hao et al
. High accuracy urban expansion monitoring and analysis of China's provincial capitals from 2000 to 2015 based on high-resolution remote sensing imagery[J]. Acta Geographica Sinica,
2018, 73(12): 2345-2363.
Wang XR, Hui CM, ChoguillC, et al.The new urbanization policy in China: Which way forward? , 2015, 47: 279-284.https://linkinghub.elsevier.com/retrieve/pii/S0197397515000351
61China has recently published the New Urbanization Policy 2014–2020.61The policy sets forth certain significant changes that will affect the course of urbanization in the next few years.61Although the policy is seen as a significant step forward, there are unanswered questions that are considered in this policy note.
RoselandM.Dimensions of the eco-city. , 1997, 14(4): 197-202.http://linkinghub.elsevier.com/retrieve/pii/S0264275197000036
ABSTRACT The paper explores the evolution of the concept of the eco-city, and shows how it can be linked to issues ranging from urban planning and economic development through to matters of social justice. The challenge is to encourage local democracy within a context of sustainability.
Land development in China has been a popular research topic in existing studies. It is not only mobilized by municipalities to attract investments and promote local economic growth, but also become the trigger to ignite various land-related conflicts. While quite a few researches have focused on the institutional contexts of "land coffer", the effects of urban land expansion on the growth of local fiscal revenue remain poorly examined. Moreover, less attention has been paid to the "land coffer" in different industries in cities with various characteristics. To help narrow this gap, this paper initiates a preliminary inquiry to the causality between expansion of urban built-up land and growth of local fiscal revenue based on the panel vector error correction model (VECM). Using data from City Statistical Yearbook (1985-2011), this study examines the mechanisms, efficiency and regional differences of fiscalization of land among Chinese municipal governments based on random effect models. The findings indicate that the growth of urban land is the Granger cause of local fiscal revenue since 1995. However, the expansion of urban built-up land caused only a transient increase in local fiscal revenue and had no significant long-term effect on the development of housing or manufacturing industries, which suggests that the urban land development in China mainly contributes to local fiscal revenue through short-term land expropriation, which has been excessively extracted and doomed to be unsustainable. As for the spatial differences among the three regions in China, the promoting effects of land development are more significant within the cities in eastern region or those with a larger population size. Nonetheless, land development generally has negative effects on housing and manufacturing industries for Northeast China, central region and western region in the long run. This implies that the intensifying land expropriation across the country is rather short-sighted. To prevent waste of precious farmland in the ongoing urban sprawl and the upcoming round of "small city-township" development (chengzhenhua), we call for more compact land development and reform of current land system.
SchneiderA, Friedl MA, PotereD.Mapping global urban areas using MODIS 500-m data: New methods and datasets based on 'urban ecoregions'. , 2010, 114(8): 1733-1746.https://linkinghub.elsevier.com/retrieve/pii/S003442571000091X
Although cities, towns and settlements cover only a tiny fraction (< 1%) of the world's surface, urban areas are the nexus of human activity with more than 50% of the population and 70 90% of economic activity. As such, material and energy consumption, air pollution, and expanding impervious surface are all concentrated in urban areas, with important environmental implications at local, regional and potentially global scales. New ways to measure and monitor the built environment over large areas are thus critical to answering a wide range of environmental research questions related to the role of urbanization in climate, biogeochemistry and hydrological cycles. This paper presents a new dataset depicting global urban land at 500-m spatial resolution based on MODIS data (available at http://sage.wisc.edu/urbanenvironment.html). The methodological approach exploits temporal and spectral information in one year of MODIS observations, classified using a global training database and an ensemble decision-tree classification algorithm. To overcome confusion between urban and built-up lands and other land cover types, a stratification based on climate, vegetation, and urban topology was developed that allowed region-specific processing. Using reference data from a sample of 140 cities stratified by region, population size, and level of economic development, results show a mean overall accuracy of 93% ( k = 0.65) at the pixel level and a high level of agreement at the city scale ( R 2 = 0.90).
SchneiderA.A new map of global urban extent from MODIS satellite data. , 2009, 4(4): 44003-44011.http://stacks.iop.org/1748-9326/4/i=4/a=044003?key=crossref.6bde1987a5db9cd29ac85e4c73503481
Although only a small percentage of global land cover, urban areas significantly alter climate, biogeochemistry, and hydrology at local, regional, and global scales. To understand the impact of urban areas on these processes, high quality, regularly updated information on the urban environment-including maps that monitor location and extent-is essential. Here we present results from efforts to map the global distribution of urban land use at 500 m spatial resolution using remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our approach uses a supervised decision tree classification algorithm that we process using region-specific parameters. An accuracy assessment based on sites from a stratified random sample of 140 cities shows that the new map has an overall accuracy of 93% (k = 0.65) at the pixel level and a high level of agreement at the city scale (R= 0.90). Our results (available at http://sage.wisc.edu/urbanenvironment.html) also reveal that the land footprint of cities occupies less than 0.5% of the Earth's total land area.
LiX, GongP, LiangL.A 30-year (1984-2013) record of annual urban dynamics of Beijing City derived from Landsat data. , 2015, 166(1): 78-90.https://linkinghub.elsevier.com/retrieve/pii/S0034425715300377
61An annual sequence of urban land has been produced in Beijing over a 30-year period.61Many Landsat images have been employed to make full use of their temporal contexts.61A temporal consistency check was conducted to make the sequence more reasonable.61The growth rates are different in Beijing during the past three decades.
SchneiderA, Woodcock CE.Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. , 2008, 45(3): 659.http://journals.sagepub.com/doi/10.1177/0042098007087340
Friedl MA, Mciver DK, HodgesJ C F, et al. Global land cover mapping from MODIS: Algorithms and early results. , 2002, 83(1/2): 287-302.http://linkinghub.elsevier.com/retrieve/pii/S0034425702000780
Until recently, advanced very high-resolution radiometer (AVHRR) observations were the only viable source of data for global land cover mapping. While many useful insights have been gained from analyses based on AVHRR data, the availability of moderate resolution imaging spectroradiometer (MODIS) data with greatly improved spectral, spatial, geometric, and radiometric attributes provides significant new opportunities and challenges for remote sensing-based land cover mapping research. In this paper, we describe the algorithms and databases being used to produce the MODIS global land cover product. This product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP. To generate these maps, a supervised classification methodology is used that exploits a global database of training sites interpreted from high-resolution imagery in association with ancillary data. In addition to the IGBP class at each pixel, the MODIS land cover product provides several other parameters including estimates for the classification confidence associated with the IGBP label, a prediction for the most likely alternative class, and class labels for several other classification schemes that are used by the global modeling community. Initial results based on 5 months of MODIS data are encouraging. At global scales, the distribution of vegetation and land cover types is qualitatively realistic. At regional scales, comparisons among heritage AVHRR products, Landsat TM data, and results from MODIS show that the algorithm is performing well. As a longer time series of data is added to the processing stream and the representation of global land cover in the site database is refined, the quality of the MODIS land cover product will improve accordingly.
WanB, GuoQ, FangF, et al.Mapping US urban extents from MODIS data using one-class classification method. , 2015, 7(8): 10143-10163.http://www.mdpi.com/2072-4292/7/8/10143
Urban areas are one of the most important components of human society. Their extents have been continuously growing during the last few decades. Accurate and timely measurements of the extents of urban areas can help in analyzing population densities and urban sprawls and in studying environmental issues related to urbanization. Urban extents detected from remotely sensed data are usually a by-product of land use classification results, and their interpretation requires a full understanding of land cover types. In this study, for the first time, we mapped urban extents in the continental United States using a novel one-class classification method, i.e., positive and unlabeled learning (PUL), with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) night stable light data were used to calibrate the urban extents obtained from the one-class classification scheme. Our results demonstrated the effectiveness of the use of the PUL algorithm in mapping large-scale urban areas from coarse remote-sensing images, for the first time. The total accuracy of mapped urban areas was 92.9% and the kappa coefficient was 0.85. The use of DMSP-OLS night stable light data can significantly reduce false detection rates from bare land and cropland far from cities. Compared with traditional supervised classification methods, the one-class classification scheme can greatly reduce the effort involved in collecting training datasets, without losing predictive accuracy.
BartholoméE, Belward AS.GLC2000: A new approach to global land cover mapping from Earth observation data. , 2005, 26(9): 1959-1977.https://www.tandfonline.com/doi/full/10.1080/01431160412331291297
A new global land cover database for the year 2000 (GLC2000) has been produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre. The database contains two levels of land cover information—detailed, regionally optimized land cover legends for each continent and a less thematically detailed global legend that harmonizes regional legends into one consistent product. The land cover maps are all based on daily data from the VEGETATION sensor on‐board SPOT 4, though mapping of some regions involved use of data from other Earth observing sensors to resolve specific issues. Detailed legend definition, image classification and map quality assurance were carried out region by region. The global product was made through aggregation of these. The database is designed to serve users from science programmes, policy makers, environmental convention secretariats, non‐governmental organizations and development‐aid projects. The regional and global data are available free of charge for all non‐commercial applications from http://www.gvm.jrc.it/glc2000.
MayauxP, EvaH, GallegoJ, et al.Validation of the global land cover 2000 map. , 2006, 44(7): 1728-1739.http://ieeexplore.ieee.org/document/1645273/
The Joint Research Centre of the European Commission (JRC), in partnership with 30 institutions, has produced a global land cover map for the year 2000, the GLC 2000 map. The validation of the GLC2000 product has now been completed. The accuracy assessment relied on two methods: a confidence-building method (quality control based on a comparison with ancillary data) and a quantitative accuracy assessment based on a stratified random sampling of reference data. The sample site stratification used an underlying grid of Landsat data and was based on the proportion of priority land cover classes and on the landscape complexity. A total of 1265 sample sites have been interpreted. The first results indicate an overall accuracy of 68.6%. The GLC2000 validation exercise has provided important experiences. The design-based inference conforms to the CEOS Cal-Val recommendations and has proven to be successful. Both the GLC2000 legend development and reference data interpretations used the FAO Land Cover Classification System (LCCS). Problems in the validation process were identified for areas with heterogeneous land cover. This issue appears in both in the GLC2000 (neighborhood pixel variations) and in the reference data (cartographic and thematic mixed units). Another interesting outcome of the GLC2000 validation is the accuracy reporting. Error statistics are provided from both the producer and user perspective and incorporates measures of thematic similarity between land cover classes derived from LCCS
BicheronP, DefournyP, BrockmannC, et al.GLOBCOVER: Products description and validation report. , 2011, 17(3): 285-287.
BaganH, YamagataY.Landsat analysis of urban growth: How Tokyo became the world's largest megacity during the last 40 years. , 2012, 127: 210-222.https://linkinghub.elsevier.com/retrieve/pii/S0034425712003653
78 Remote sensing data and census data were integrated in grid cells each with an area of 1 km2. 78 Urban expansion has strong correlation with population changes and cropland changes. 78 Population and urban/built-up area decreased in the city core during 1972–2011.
GuindonB, ZhangY, DillabaughC.Landsat urban mapping based on a combined spectral-spatial methodology. , 2004, 92(2): 218-232.http://linkinghub.elsevier.com/retrieve/pii/S0034425704001749
Urban mapping using Landsat Thematic Mapper (TM) imagery presents numerous challenges. These include spectral mixing of diverse land cover components within pixels, spectral confusion with other land cover features such as fallow agricultural fields and the fact that urban classes of interest are of the land use and not the land cover category. A new methodology to address these issues is proposed. This approach involves, as a first step, the generation of two independent but rudimentary land cover products, one spectral-based at the pixel level and the other segment-based. These classifications are then merged through a rule-based approach to generate a final product with enhanced land use classes and accuracy. A comprehensive evaluation of derived products of Ottawa, Calgary and cities in southwestern Ontario is presented based on conventional ground reference data as well as inter-classification consistency analyses. Producer accuracies of 78% and 73% have been achieved for urban ‘residential’ and ‘commercial/industrial’ classes, respectively. The capability of Landsat TM to detect low density residential areas is assessed based on dwelling and population data derived from aerial photography and the 2001 Canadian census. For low population densities (i.e. below 3000 persons/km 2), density is observed to be monotonically related to the fraction of pixels labeled ‘residential’. At higher densities, the fraction of pixels labeled ‘residential’ remains constant due to Landsat's inability to distinguish between high-rise apartment dwellings and commercial/industrial structures.
GaoF, DeColstoun E B, MaR, et al. Mapping impervious surface expansion using medium-resolution satellite image time series: A case study in the Yangtze River Delta, China. , 2012, 33(24): 7609-7628.https://www.tandfonline.com/doi/full/10.1080/01431161.2012.700424
Cities have been expanding rapidly worldwide, especially over the past few decades. Mapping the dynamic expansion of impervious surface in both space and time is essential for an improved understanding of the urbanization process, land-cover and land-use change, and their impacts on the environment. Landsat and other medium-resolution satellites provide the necessary spatial details and temporal frequency for mapping impervious surface expansion over the past four decades. Since the US Geological Survey opened the historical record of the Landsat image archive for free access in 2008, the decades-old bottleneck of data limitation has gone. Remote-sensing scientists are now rich with data, and the challenge is how to make best use of this precious resource. In this article, we develop an efficient algorithm to map the continuous expansion of impervious surface using a time series of four decades of medium-resolution satellite images. The algorithm is based on a supervised classification of the time-series image stack using a decision tree. Each imerpervious class represents urbanization starting in a different image. The algorithm also allows us to remove inconsistent training samples because impervious expansion is not reversible during the study period. The objective is to extract a time series of complete and consistent impervious surface maps from a corresponding times series of images collected from multiple sensors, and with a minimal amount of image preprocessing effort. The approach was tested in the lower Yangtze River Delta region, one of the fastest urban growth areas in China. Results from nearly four decades of medium-resolution satellite data from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and China razil Earth Resources Satellite (CBERS) show a consistent urbanization process that is consistent with economic development plans and policies. The time-series impervious spatial extent maps derived from this study agree well with an existing urban extent polygon data set that was previously developed independently. The overall mapping accuracy was estimated at about 92.5% with 3% commission error and 12% omission error for the impervious type from all images regardless of image quality and initial spatial resolution.
SunZ, WangC, GuoH, et al.A modified normalized difference impervious surface index (MNDISI) for automatic urban mapping from landsat imagery. , 2017, 9(9): 942.http://www.mdpi.com/2072-4292/9/9/942
Impervious surface area (ISA) is a key factor for monitoring urban environment and land development. Automatic mapping of impervious surfaces has attracted growing attention in recent years. Spectral built-up indices are considered promising to map ISA distributions due to their easy, parameter-free implementations. This study explores the potentials of impervious surface indices for ISA mapping from Landsat imagery using a case study area in Boston, USA. A modified normalized difference impervious surface index (MNDISI) is proposed, and a Gaussian-based automatic threshold selection method is used to identify the optimal MNDISI threshold for delineating impervious surfaces from background features. To evaluate its effectiveness, comparison analysis is conducted between MNDISI and the original NDISI using Landsat images from three sensors (TM/ETM+/OLI-TIRS) acquired in four seasons. Our results suggest that built-up indices are sensitive to image seasonality, and summer is the best time phase for ISA mapping. With reduced uncertainties from automatic threshold selection, the MNDISI extracts impervious surfaces from all Landsat images in summer with an overall accuracy higher than 87% and an overall Kappa coefficient higher than 0.74. The proposed method is superior to previous index-based ISA mapping from the enhanced thermal integration and automatic threshold selection. The ISA maps from the TM, ETM+ and OLI-TIRS images are not significantly different. With enlarged data pool when all Landsat sensors are considered and automation of threshold selection proposed in this study, the MNDISI could be an effective built-up index for rapid and automatic ISA mapping at regional and global scales.
Son NT, Chen CR.Urban growth mapping from Landsat data using linear mixture model in Ho Chi Minh City, Vietnam. , 2012, 6(1): 100-106.http://spie.org/Publications/Journal/10.1117/1.JRS.6.063543
Rapid urbanization in Ho Chi Minh City (HCMC), Vietnam, is creating societal impacts on the environment attributed to the increasing population. Understanding spatio-temporal dimensions of land-use changes that shape the urbanization is thus critical to the process of urban planning. We explore the urban growth in HCMC through Landsat images for 1990, 2002, and 2010 using the linear mixture model (LMM). The data are processed through four steps: (1) data pre-processing, (2) image classification by LMM using endmembers extracted from the original image using minimum noise fraction, (3) accuracy assessment of the classification results using field verification data, and (4) urban growth analysis to understand the spatial changes of land cover. The results achieved by comparisons between the classification results and ground reference data indicate that the overall accuracy and Kappa coefficient obtained for 1990 were 87.1% and 0.83, respectively, while those for 2002 were 92.5% and 0.89, and those for 2010 were 89.6% and 0.86. The results of urban growth analysis indicate that high albedo class (i.e., built-up areas) expanded from 12.3% in 1990 to 27.2% in 2002 and to 31.1% in 2010. When investigating land-cover conversions to high albedo class from 1990 to 2002, the largest conversion is observed for soil class (9.2%), followed by vegetation class (7.2%), and low albedo class (2.2%). From 2002 to 2010, 4.5% area of soil class was converted to high albedo class, while conversions from vegetation and low albedo classes were 3.5% and 2.5%, respectively.
AngiuliE, TrianniG.Urban mapping in Landsat images based on normalized difference spectral vector. , 2014, 11(3): 661-665.http://ieeexplore.ieee.org/document/6587128/
In the last decades the number of natural and anthropic changes affecting population worldwide has raised dramatically. This fact, coupled with the increasing world population living in urban areas, requires the development of a detailed and reliable map of global urban extent. This letter reports on a new approach for urban mapping from Landsat images, based on the Normalized Difference Spectral Vector (NDSV). This spectral transformation allows the creation of a normalized signature that becomes peculiar of each land cover class within the scene. The urban extent classification is obtained by analyzing the NDSV data in conjunction with a Spectral Angle Mapper (SAM) based classifier. The experiments presented in this letter show the effectiveness of the proposed technique in detecting urban areas in extremely different environments. The results of the proposed methodology have been compared with the ones obtained by classifying the NDSV using other classifiers [namely, maximum likehood (ML) and support vector machines (SVM)], and also to the results obtained by classifying the calibrated data using the ML, SVM and SAM classifiers. The NDSV+SAM approach has provided the best results, with an overall accuracy of 97%.
WangLei, LiCongcong, YingQing, et al.China's urban expansion from 1990 to 2010 determined with satellite remote sensing. , 2012, 57(22): 2802-2812.http://link.springer.com/10.1007/s11434-012-5235-7
Based on the same data source of Landsat TM/ETM+ in 1990s, 2000s and 2010s, all urban built-up areas in China are mapped mainly by human interpretation. Mapping results were checked and refined by the same analyst with the same set of criteria. The results show during the last 20 years urban areas in China have increased exponentially more than 2 times. The greatest area of urbanization changed from Northeastern provinces in 1990s to the Southeast coast of China in Jiangsu, Guangdong, Shandong, and Zhejiang in 2010s. Urban areas are mostly converted from croplands in China. Approximately 17750 km croplands were converted into urban lands. Furthermore, the conversion from 2000 to 2010 doubled that from 1990 to 2000. During the 20 years, the most urbanized provinces are Jiangsu, Guangdong, Shandong and Zhejiang. We also analyzed built-up areas, gross domestic production (GDP) and population of 147 cities with a population of greater than 500000 in 2009. The result shows coastal cities and resource-based cities are with high economic efficiency per unit of built-up areas, resource-based cities have the highest population density, and the economic efficiency of most coastal provinces are lower than central provinces and Guangdong. The newly created urban expansion dataset is useful in many fields including trend analysis of urbanization in China; simulation of urban development dynamics; analysis of the relationship among urbanization, population growth and migration; studies of carbon emissions and climate change; adaptation of climate change; as well as land use and urban planning and management.
ZhouY, Smith SJ, ZhaoK, et al.A global map of urban extent from nightlights. , 2015, 10(5): 054011.http://stacks.iop.org/1748-9326/10/i=5/a=054011?key=crossref.65bd8a6bc42eba9338a1ed9db6c0096d
Urbanization, a major driver of global change, profoundly impacts our physical and social world, for example, altering not just water and carbon cycling, biodiversity, and climate, but also demography, public health, and economy. Understanding these consequences for better scientific insights and effective decision-making unarguably requires accurate information on urban extent and its spatial distributions. We developed a method to map the urban extent from the defense meteorological satellite program/operational linescan system nighttime stable-light data at the global level and created a new global 1 km urban extent map for the year 2000. Our map shows that globally, urban is about 0.5% of total land area but ranges widely at the regional level, from 0.1% in Oceania to 2.3% in Europe. At the country level, urbanized land varies from about 0.01 to 10%, but is lower than 1% for most (70%) countries. Urbanization follows land mass distribution, as anticipated, with the highest concentration between 30 N and 45 N latitude and the largest longitudinal peak around 80 W. Based on a sensitivity analysis and comparison with other global urban area products, we found that our global product of urban areas provides a reliable estimate of global urban areas and offers the potential for producing a time-series of urban area maps for temporal dynamics analyses.
HuangX, SchneiderA, Friedl MA.Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lights. , 2016, 175: 92-108.https://linkinghub.elsevier.com/retrieve/pii/S0034425715302637
61We estimated sub-pixel urban cover at 250m resolution in China for 2001 and 2010.61We fused 250m, 500m, and 1km MODIS data and DMSP/OLS nighttime lights data.61Separate regression models estimated for temperate and subtropical regions of China61City-level assessment showed good agreement with Landsat-based urban information.61Regional mapping demonstrated utility of this method for large-area application.
Imhoff ML, Lawrence WT, Stutzer DC, et al.A technique for using composite DMSP/OLS "City Lights" satellite data to map urban area. , 1997, 61(3): 361-370.http://linkinghub.elsevier.com/retrieve/pii/S0034425797000461
A Tresholding technique was used to convert a prototype “city lights” data set from the National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAAINGDC) into a map of “urban areas” for the continental United States. Thresholding was required to adapt the Defense Meteorological Satellite Program's Operational Linescan System (DMSPIOLS)-based NGDC data set into an urban map because the values reported in the prototype represent a cumulative percentage lighted for each pixel extracted from hundreds of nighttime cloud screened orbits, rather than any suitable land-cover classification. The cumulative percentage lighted data could not be used alone because the very high gain of the OLS nighttime photomultiplier configuration can. lead to a pixel (2.7X2.7 km)
SusakiJ, KajimotoM, KishimotoM.Urban density mapping of global megacities from polarimetric SAR images. , 2014, 155: 334-348.https://linkinghub.elsevier.com/retrieve/pii/S0034425714003496
61We estimated urban areas and density from a single polarimetric SAR image.61We calculated statistics from images to reduce orientation angle effects.61The estimated urban density has a high correlation with building-to-land ratio.61We compared the urban density patterns of global megacities.61Analysis using urban density maps indicates the patterns of urban development.
GongP.Settlement extraction in the North China Plain using Landsat and Beijing-1 multispectral data with an improved watershed segmentation algorithm. , 2010, 31(6): 1411-1426.https://www.tandfonline.com/doi/full/10.1080/01431160903475332
In this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30–40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM65+65) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30–32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85.
JieY, YinZ, ZhongH, et al.Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979-2009) in China. , 2011, 177(1-4): 609-621.http://link.springer.com/10.1007/s10661-010-1660-8
This study explored the spatio-temporal dynamics and evolution of land use/cover changes and urban expansion in Shanghai metropolitan area, China, during the transitional economy period (1979 2009) using multi-temporal satellite images and geographic information systems (GIS). A maximum likelihood supervised classification algorithm was employed to extract information from four landsat images, with the post-classification change detection technique and GIS-based spatial analysis methods used to detect land-use and land-cover (LULC) changes. The overall Kappa indices of land use/cover change maps ranged from 0.79 to 0.89. Results indicated that urbanization has accelerated at an unprecedented scale and rate during the study period, leading to a considerable reduction in the area of farmland and green land. Findings further revealed that water bodies and bare land increased, obviously due to large-scale coastal development after 2000. The direction of urban expansion was along a north-south axis from 1979 to 2000, but after 2000 this growth changed to spread from both the existing urban area and along transport routes in all directions. Urban expansion and subsequent LULC changes in Shanghai have largely been driven by policy reform, population growth, and economic development. Rapid urban expansion through clearing of vegetation has led to a wide range of eco-environmental degradation.
DaiX, GuoZ, ZhangL, et al.Spatio-temporal pattern of urban land cover evolvement with urban renewal and expansion in Shanghai based on mixed-pixel classification for remote sensing imagery. , 2010, 31(23): 6095-6114.https://www.tandfonline.com/doi/full/10.1080/01431160903376407
Research into pixel unmixing in remote sensing imagery led to the development of soft classification methods. In this article, we propose a possibilistic c repulsive medoids (PCRMdd) clustering algorithm which attempts to find c repulsive medoids as a minimal solution of a particular objective function. The PCRMdd algorithm is applied to predict the proportion of each land use class within a single pixel, and generate a set of endmember fraction images. The clustering results obtained on multi-temporal Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper plus (ETM+) images of Shanghai city in China reveal the spatio-temporal pattern of Shanghai land use evolvement and urban land spatial sprawl in course of urbanization from 1989 to 2002. The spatial pattern of land use transformation with urban renewal and expansion indicates the urban land use structure is gradually optimized during vigorous urban renewal and large-scale development of Pudong area, which will have an active influence on improving urban space landscape and enhancing the quality of the ecological environment. In addition, accuracy analysis demonstrates that PCRMdd represents a robust and effective tool for mixed-pixel classification on remote sensing imagery to obtain reliable soft classification results and endmember spectral information in a noisy environment.
LuD, WengQ.A survey of image classification methods and techniques for improving classification performance. , 2007, 28(5): 823-870.https://www.tandfonline.com/doi/full/10.1080/01431160600746456
Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non‐parametric classifiers such as neural network, decision tree classifier, and knowledge‐based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image‐processing chain to improve classification accuracy.
GanL, LiD, SongS.Is the Zipf law spurious in explaining city-size distributions? , 2006, 92(2): 256-262.http://linkinghub.elsevier.com/retrieve/pii/S0165176506000772
The Zipf law, which states that that the rank associated with some size S is proportional to S to some negative power, is a regularity observed in natural and social sciences. One popular application of the Zipf law is the relationship between city sizes and their ranks. This paper examines the rank ize relationship through Monte Carlo simulations and two examples. We show that a good fit (indicated by a high R2 value) can be found for many statistical distributions. The Zipf law's good fit is a statistical phenomenon, and therefore, it does not require an economic theory that determines city-size distributions.
AndersonG, GeY.The size distribution of Chinese cities. , 2005, 35(6): 756-776.http://www.sciencedirect.com/science/article/pii/S0166046205000062
This paper uses urban data to investigate two important issues regarding city sizes in China, the relative growth of cities and the nature of the city size distribution. The manner in which cities of different sizes grow relative to each other is examined and, contrary to the common empirical finding that the relative size and rank of cities remains stable over time, it is found that the Economic Reforms and the One Child Policy since 1979 have delivered significant structural change in the Chinese urban system. The city size distribution remains stable before the reforms but exhibits a convergent growth pattern in the post-reform period. The theoretical literature on city sizes highlights a link between log normal and Pareto distributions for city sizes prompting the employment of Pearson goodness-of-fit tests to examine directly which theoretical distribution provides the best approximation to the empirical city size distribution. Contrary to the evidence for other countries, a log normal rather than Pareto specification turns out to be the preferred distribution.
GangopadhyayK, BasuB.City size distributions for India and China. , 2009, 388(13): 2682-2688.http://www.sciencedirect.com/science/article/pii/S0378437109002076
This paper studies the size distributions of urban agglomerations for India and China. We have estimated the scaling exponent for Zipf’s law with the Indian census data for the years of 1981–2001 and the Chinese census data for 1990 and 2000. Along with the biased linear fit estimate, the maximum likelihood estimate for the Pareto and Tsallis q-exponential distribution has been computed. For India, the scaling exponent is in the range of [1.88, 2.06] and for China, it is in the interval [1.82, 2.29]. The goodness-of-fit tests of the estimated distributions are performed using the Kolmogorov–Smirnov statistic.