Spatiotemporal Dynamics and Driving Forces of Urban Land-Use Expansion: A Case Study of the Yangtze River Economic Belt, China

Remote Sensing - Tập 12 Số 2 - Trang 287
Yang Zhong1,2,3,4, Aiwen Lin1,4, Lijie He5, Zhigao Zhou1,4, Moxi Yuan1,4
1Key Laboratory of Geographic Information System, Wuhan University, Wuhan,430079, China
2Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, 330022, China
3School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
4School of Resources and Environmental Sciences, Wuhan University, Wuhan 430079, China
5Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China

Tóm tắt

It is important to analyze the expansion of an urban area and the factors that drive its expansion. Therefore, this study is based on Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night lighting data, using the landscape index, spatial expansion strength index, compactness index, urban land fractal index, elasticity coefficient, the standard deviation ellipse, spatial correlation analysis, and partial least squares regression to analyze the spatial and temporal evolution of urban land expansion and its driving factors in the Yangtze River Economic Belt (YREB) over a long period of time. The results show the following: Through the calculation of the eight landscape pattern indicators, we found that during the study period, the number of cities and towns and the area of urban built-up areas in the YREB are generally increasing. Furthermore, the variations in these landscape pattern indicators not only show more frequent exchanges and interactions between the cities and towns of the YREB, but also reflect significant instability and irregularity of the urbanization development in the YREB. The spatial expansion intensity indices of 1992–1999, 1999–2006, and 2006–2013 were 0.03, 0.16, and 0.34, respectively. On the whole, the urban compactness of the YREB decreased with time, and the fractal dimension increased slowly with time. Moreover, the long axis and the short axis of the standard deviation ellipse of the YREB underwent a small change during the inspection period. The spatial distribution generally showed the pattern of “southwest-north”. In terms of gravity shift, during the study period, the center of gravity moved from northeast to southwest. In addition, the Moran's I values for the four years of 1992, 1999, 2006, and 2013 were 0.451, 0.495, 0.506, and 0.424, respectively. Furthermore, by using correlation analysis, we find that the correlation coefficients between these four driving indicators and the urban expansion of the YREB were: 0.963, 0.998, 0.990 and 0.994, respectively. Through the use of partial least squares regression, we found that in 1992-2013, the four drivers of urban land expansion in the YREB were ranked as follows: gross domestic product (GDP), total fixed asset investment, urban population, total retail sales of consumer goods.

Từ khóa


Tài liệu tham khảo

Fang, 2018, Geographical contribution and responsibility mission of china’s urbanization development, Geogr. Sci., 38, 321

Fang, C.L., Bao, C., and Ma, H.T. (2016). China’s Urban Agglomeration Development Report 2016, Science Press. (In Chinese).

Chen, 2014, Characteristics and mechanism of construction land expansion in Nanjing metropolitan area, Geogr. Res., 33, 427

Anniwaer, 2018, Urban expansion and its driving force analysis of Yi-Ning city based on remote sensing data, Arid Zone Geogr., 41, 9

Wang, Z.Q., Zhang, H.W., and Chai, J. (2018). Differences in urban built-up land expansion in Zhengzhou and Changsha, China—An approach based on different geographical features. Sustainability, 10.

Tong, 2018, Multi-level urban land expansion characterization using spatiotemporal statistics for Hunan, Hubei and Jiangxi, Resour. Sci., 40, 1175

Gao, 2019, Temporal and spatial evolution characteristics and influence mechanism of urban construction land expansion in Xinjiang in the core area of silk road economic belt, J. Ecol., 39, 1263

Feng, 2017, Temporal and spatial characteristics and driving forces of urban land expansion in Jiansanjiang Reclamation area based on remote sensing data, Geogr. Sci., 37, 1178

Li, 2018, Study on the process of urban land expansion and its driving factors in Ha Chang City group, Geogr. Sci., 38, 1273

Li, 2015, Driving force analysis and scenario simulation of urban land expansion in Yan-Huang area along Ningxia, J. Nat. Resour., 30, 1472

Xu, 2018, Analysis of urban expansion measurement and driving mechanism in typical areas of the Pearl River Delta, Surv. Sci., 43, 45

Fang, 2017, Proposal for the theoretical analysis of the interactive coupled effects between urbanization and the eco-environment in mega-urban agglomerations, J. Geogr. Sci., 27, 1431, 10.1007/s11442-017-1445-x

Cui, 2019, Advances in research on dynamic simulation model of coupled urbanization and ecological environment, Prog. Geogr., 38, 111

Liang, 2019, Urbanization of Beijing-Tianjin-Hebei urban agglomeration and spatial and temporal differentiation of ecological environment and coordinated development pattern, J. Ecol., 39, 1212

Ke, 2019, The influence of the coupling of urban expansion and farmland protection on the carbon storage of Terrestrial Ecosystem: A case study of Hubei Province, J. Ecol., 39, 672

Chen, Y.M., Liu, X.P., and Li, X. (2017). Analyzing parcel-level relationships between urban land expansion and activity changes by Integrating Landsat and Nighttime Light data. Remote Sens., 9.

Gao, 2019, Research on urban expansion and driving factors of Xi’an based on night light data, Remote Sens. Technol. Appl., 34, 207

Dong, 2017, Urban expansion and vegetation change around Hangzhou Bay based on nighttime lighting data, J. Appl. Ecol., 28, 231

Wang, 2018, Driving force analysis of urban land expansion in Wuhan city circle based on Logistic-GTWR Model, J. Agric. Eng., 34, 248

Liu, 2015, Simulation and analysis of urban space expansion in Beijing-Tianjin-Hebei Region, Prog. Geogr., 34, 217

Hermosilla, 2014, Using street based metrics to characterize urban typologies. Computers, Environ. Urban Syst., 44, 68, 10.1016/j.compenvurbsys.2013.12.002

Kraff, 2018, The morphology of the arrival city-a global categorization based on literature surveys and remotely sensed data, Appl. Geogr., 92, 150, 10.1016/j.apgeog.2018.02.002

Middel, 2019, Urban form and composition of street canyons: A human-centric big data and deep learning approach, Landsc. Urban Plan., 183, 122, 10.1016/j.landurbplan.2018.12.001

Sun, 2018, Urban expansion simulation and the spatio-temporal changes of ecosystem services, a case study in Atlanta Metropolitan area, USA, Sci. Total Environ., 622, 974, 10.1016/j.scitotenv.2017.12.062

Zhang, 2019, Effect of urban expansion on summer rainfall in the Pearl River Delta, South China, J. Hydrol., 568, 747, 10.1016/j.jhydrol.2018.11.036

Zhang, 2018, High-precision expansion monitoring and analysis of provincial capital cities in China from 2000 to 2015 based on high-resolution remote sensing images, J. Geogr. Sci., 73, 2345

Li, 2019, Urban sprawl in China: Differences and socioeconomic drivers, Sci. Total Environ., 673, 367, 10.1016/j.scitotenv.2019.04.080

Shen, 2019, Spatiotemporal patterns of recent PM. urban agglomerations in China 2.5 concentrations over typical urban agglomerations in China, Sci. Total Environ., 655, 13, 10.1016/j.scitotenv.2018.11.105

Huang, 2018, Urban land expansion and air pollution: Evidence from China, J. Urban Plan. Dev., 144, 1, 10.1061/(ASCE)UP.1943-5444.0000476

Zhang, 2016, A study on the differences of driving mechanisms of urban land expansion in China, Resour. Sci., 38, 30

Wang, 2018, The border effect on urban land expansion in China: The case of Beijing-Tianjin-Hebei region, Land Use Policy, 78, 287, 10.1016/j.landusepol.2018.06.050

Wu, 2019, Drivers of urban expansion over the past three decades: A comparative study of Beijing, Tianjin, and Shijiazhuang, Environ. Monit. Assess., 191, 1, 10.1007/s10661-018-7151-z

Bie, 2018, The spatial expansion of Beijing-Tianjin-Hebei urban agglomeration and its economic spillover effect, J. Ecol., 38, 4276

Wang, 2013, Simulating urban expansion using a cloud-based cellular automata model: A case study of Jiangxia, Wuhan, China, Landsc. Urban Plan., 110, 99, 10.1016/j.landurbplan.2012.10.016

He, 2019, Collaborative optimization of rural residential land consolidation and urban construction land expansion: A case study of Huangpi in Wuhan, China. Computers, Environ. Urban Syst., 74, 218, 10.1016/j.compenvurbsys.2018.11.005

Wang, 2018, Study on the temporal and spatial patterns of urban land expansion and the differentiation of scale and scale models in Wuhan City circle, Resour. Environ. Yangtze River Basin, 27, 272

Zhong, 2016, Impact of land revenue on the urban land growth toward decreasing population density in Jiangsu Province, China, Habitat Int., 58, 34, 10.1016/j.habitatint.2016.09.005

Shi, G., Jiang, N., Li, Y., and He, B. (2018). Analysis of the dynamic urban expansion based on multi-sourced data from 1998 to 2013: A case study of Jiangsu Province. Sustainability, 10.

Qian, 2015, Changes in urban expansion space form in Su-xi-chang area based on improved landscape expansion index, Geogr. Sci., 35, 314

Liu, 2018, Identifying the relationship between urban land expansion and human activities in the Yangtze River Economic Belt, China, Appl. Geogr., 94, 163, 10.1016/j.apgeog.2018.03.016

Jin, 2018, Spatiotemporal patterns in urbanization efficiency within the Yangtze River Economic Belt between 2005 and 2014, J. Geogr. Sci., 28, 1113, 10.1007/s11442-018-1545-2

Xie, H.L., Zhu, Z.H., Wang, B.H., Liu, G.Y., and Zhai, Q.L. (2018). Does the expansion of urban construction land promote regional economic growth in China? Evidence from 108 cities in the Yangtze River economic belt. Sustainability, 10.

Xu, 2017, Spatial connection research on the coupling and coordination between urban function and regional innovation—Based on 107 cities in the Yangtze River economic belt, Geogr. Sci., 37, 1659

Mi, 2017, Research on the innovation network characteristics of innovative loose industry and its impact on innovation performance-taking the logistics industry of the Yangtze River economic belt as an example, Geogr. Res., 36, 1653

Zhu, 2017, Temporal and spatial characteristics of regional innovation performance in the Yangtze River economic belt, Resour. Environ. Yangtze River Basin, 26, 1954

Huang, 2018, Do urban agglomerations outperform non-agglomerations? A new perspective on exploring the eco-efficiency of Yangtze River Economic Belt in China, J. Clean. Prod., 202, 1056, 10.1016/j.jclepro.2018.08.202

Xu, 2018, Ecosystem services trade-offs and determinants in China’s Yangtze River economic belt from 2000 to 2015, Sci. Total Environ., 634, 1601, 10.1016/j.scitotenv.2018.04.046

Xing, 2018, Total-factor ecological efficiency and productivity in Yangtze River Economic Belt, China: A non-parametric distance function approach, J. Clean. Prod., 200, 844, 10.1016/j.jclepro.2018.08.015

Lu, 2017, Regional disparities and influencing factors of Average CO2 Emissions from transportation industry in Yangtze River Economic Belt, Transp. Res. Part D, 57, 112, 10.1016/j.trd.2017.09.005

Ren, F.R., Tian, Z., Shen, Y.T., Chiu, Y.H., and Lin, T.Y. (2019). Energy, CO2, and AQI efficiency and improvement of the Yangtze River economic belt. Energies, 12.

Fan, 2019, Spatial differentiation of land use carbon emissions in the Yangtze River economic belt from a low-carbon perspective, Econ. Geogr., 39, 190

Croft, 1978, Nighttime images of the earth from space, Sci. Am., 239, 68, 10.1038/scientificamerican0778-86

Li, 2015, On night light remote sensing data mining, J. Surv. Mapp., 44, 591

Ghosh, 2017, Exploring the lateral expansion dynamics of four metropolitan cities of India using DMSP/OLS night time image, Spat. Inf., 25, 779, 10.1007/s41324-017-0141-3

Li, Q.T., Lu, L.L., Weng, Q.H., Xie, Y.H., and Guo, H.D. (2016). Monitoring urban dynamics in the Southeast U.S.A. Using time-series DMSP/OLS nightlight imagery. Remote Sens., 8.

Chen, 2018, Urban spatial expansion and spatial correlation measurement in Beijing-Tianjin-Hebei Region based on DMSP/OLS night light data, Geogr. Res., 37, 898

Zheng, 2018, DMSP/OLS nighttime light data desaturation method for unit road network length, J. Remote Sens., 22, 161

Zhuo, 2015, DMSP/OLS nighttime light data desaturation method based on EVI index, J. Geogr. Sci., 70, 1339

Wu, 2018, Research on saturation correction of long-term nighttime light remote sensing data in China DMSP-OLS, J. Remote Sens., 22, 621

Wu, 2013, Exploring factors affecting the relationship between light consumption and GDP based on DMSP/OLS nighttime satellite imagery, Remote Sens. Environ., 134, 111, 10.1016/j.rse.2013.03.001

Lu, 2019, Spatial difference analysis of GDP in Yunnan Border Area based on night light and land use data, J. Earth Inf. Sci., 21, 455

Li, 2016, Estimation of GDP and its spatialization in contiguous destitute areas based on nighttime lighting data, Remote Sens. Land Resour., 28, 168

Yan, 2017, Mapping urban CO2 emissions using DMSP/OLS ‘city lights’ satellite data in China, Environ. Plan. A, 49, 248, 10.1177/0308518X16656374

Shi, 2016, Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis, Appl. Energy, 168, 523, 10.1016/j.apenergy.2015.11.055

Su, 2013, Characteristics and mechanism of carbon emissions in China’s energy consumption based on nighttime light data, J. Geogr. Sci., 68, 1513

Xie, 2016, Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries, Energy, 100, 177, 10.1016/j.energy.2016.01.058

Tripathy, 2018, Modeling of electric demand for sustainable energy and management in India using spatio-temporal DMSP-OLS night-time data, Environ. Manag., 61, 615, 10.1007/s00267-017-0978-1

Chou, 2019, Differences in radiance between DMSP/OLS and VIIRS luminous images and their comparison in power consumption estimation, J. Appl. Sci., 37, 99

Huang, 2016, Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lights, Remote Sens. Environ., 175, 92, 10.1016/j.rse.2015.12.042

Xu, 2014, Characterizing spatio-temporal dynamics of urbanization in china using time series of DMSP/OLS night light data, Remote Sens., 6, 7708, 10.3390/rs6087708

Hu, Y.N., Peng, J., Liu, Y.X., Du, Y.Y., Li, H.L., and Wu, J.S. (2017). Mapping development pattern in Beijing-Tianjin-Hebei urban agglomeration using DMSP/OLS nighttime light data. Remote Sens., 9.

Kum, 2019, Modeling the luminous intensity of Beijing, China using DMSP-OLS night-time lights series data for estimating population density, Phys. Chem. Erath, 109, 26

(2018, December 12). DMSP-OLS Night light data, Available online: https://www.ngdc.noaa.gov.

(2018, December 15). China’s 1995, 2000, and 2005 Phase III Land Cover Data (NLCD). Available online: http://www.geodata.cn/index.html.

LIU, 2016, Study on the spatial-temporal differences of coordinated development between population and urbanization in the Yangtze River economic belt, China Popul. Resour. Environ., 26, 160

(2018, December 20). NOAA/AVHRR NDVI Data, Available online: https://www.usgs.gov.

(2018, December 17). SPOT/VGT Data. Available online: http://free.vgt.vito.be.

(2018, December 20). Surface Temperature Data, Available online: http://lad-sweb.nascom.nasa.gov.

(2018, December 28). The Urban Population Data, the GDP Data, Total Fixed Assets Investment, Total Retail Sales. Available online: http://data.cnki.net/Home/Index.

Cao, 2015, Correction and application of DMSP / OLS night light image in China, J. Geoinform., 17, 1092

Liu, 2018, Estimating spatiotemporal variations of city-level energy-related CO2 emissions: An improved disaggregating model based on vegetation adjusted nighttime light data, J. Clean. Prod., 117, 101, 10.1016/j.jclepro.2017.12.197

Shi, 2018, Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective, Appl. Energy, 211, 218, 10.1016/j.apenergy.2017.11.042

Ma, X.L., Li, C.M., Tong, X.H., and Liu, S.C. (2019). A new fusion approach for extracting urban built-up areas from multisource remotely sensed data. Remote Sens., 11.

Zhou, Q., Zhao, X., Wu, D.H., Tang, R.Y., Du, X.Z., Wang, H., Zhao, J.C., Xu, P.P., and Peng, Y.F. (2019). Impact of urbanization and climate on vegetation coverage in the Beijing–Tianjin–Hebei Region of China. Remote Sens., 11.

He, 2014, Urban expansion dynamics and natural habitat loss in China: A multi-scale landscape perspec-tive, Glob. Chang. Biol., 20, 2886, 10.1111/gcb.12553

Fan, 2013, Analysis on the spatial pattern of the Bohai Rim urban agglomeration based on DMSP-OLS images from 1992 to 2010, J. Geoinform., 15, 280

Huang, 2018, Using landscape indicators and Analytic Hierarchy Process (AHP) to determine the optimum spatial scale of urban land use patterns in Wuhan, China, Earth Sci. Inform., 11, 567, 10.1007/s12145-018-0348-4

Roo, 2000, Environmental conflicts in compact cities: Complexity, decision making, and policy Approaches, Environ. Plan. B Plan. Des., 27, 151, 10.1068/b2614

Mou, 2007, Dynamic monitoring and driving force analysis of urban built-up areas in Beijing from 1973 to 2005 based on multi-source remote sensing data, J. Remote Sens., 11, 257

Tan, 2004, Expansion of construction land in large and medium-sized cities in China in the 1990s and its occupation of cultivated land, Chin. Sci. (D Ser.), 34, 1157

Xiao, 1997, Urbanization process and sustainable use of land resources, Yunnan Geogr. Environ. Res., 9, 32

Wang, 2015, Research on spatial structure of Chengdu city group based on DMSP/OLS night light data, Urban Dev. Res., 22, 20

Zhao, 2014, Spatial differentiation of China’s economic space based on characteristic ellipse, Geogr. Sci., 34, 979

Feng, 2015, Spatial pattern evolution and driving factors analysis of urbanization level in the urban agglomerations of the middle reaches of the Yangtze River, Resour. Environ. Yangtze River Basin, 24, 899

Wu, 2004, A spatial analysis on China’s regional economic growth clustering, Geogr. Sci., 24, 654

Wang, 2018, From dispersed to Clustered: New trend of spatial restructuring in China’s metropolitan region of Yangtze River Delta, Habitat Int., 80, 70, 10.1016/j.habitatint.2018.08.005

Zhao, 2011, Analysis of the time and space differences of the driving forces of urban construction land expansion in China, Resour. Sci., 33, 935

Wang, 2016, Spatial expansion pattern of Beijing-Tianjin-Hebei urban agglomeration in transition period and its dynamic mechanism-based on night light data method, J. Geogr. Sci., 71, 2155

Abolghasem, 2018, Exploring the relationship between spatial driving forces of urban expansion and socioeconomic segregation: The case of Shiraz, Habitat Int., 81, 33, 10.1016/j.habitatint.2018.09.001

Zhang, 2015, Study on urban land use efficiency pattern evolution and driving mechanism in the Yangtze River economic belt, Yangtze River Basin Resour. Environ., 24, 387

Thông tin
Thông tin xuất bản

Remote Sensing

Tập 12 Số 2

287

Thông tin tác giả