Assessing 40 years of spatial dynamics and patterns in megacities along the Belt and Road region using satellite imagery

ABSTRACT The Belt and Road (B&R) region, a vital area with historical, economic, cultural and political significance, has undergone rapid urbanization in the past several decades, especially in the form of urban expansion. In this study, 20 megacities in the B&R region were selected to explore different spatiotemporal patterns of urban expansion. Object-oriented support vector machines (SVM), annual growth rate (AGR) models, and landscape metrics were employed to delineate the urban areas and characterize spatiotemporal characteristics and landscape patterns of these megacities during 1975–2015. All urban maps presented high overall accuracies (80.70%–95.90%) and overall Kappa coefficients (0.76–0.95). The study revealed that megacities in the B&R region have undergone different types of urban sprawl, mainly adopting a ‘concentric circle’ pattern in inland areas and a ‘sector’ pattern in coastal areas. Besides, six expansion modes were summarized according to the AGRs of individual megacities. Differences existed in megacities of the developing and developed countries and among five sub-regions. Moreover, ‘dispersion, gathering, and re-dispersion’ and ‘coalescence’ were two major landscape patterns of megacities in developing and developed countries. Results of this study can provide a scientific reference for urban planning and aid in sustainable development of local areas.

[1]  Muhammad Tanveer,et al.  EEG signal classification using universum support vector machine , 2018, Expert Syst. Appl..

[2]  W. Kuang Mapping global impervious surface area and green space within urban environments , 2019, Science China Earth Sciences.

[3]  Dong Liang,et al.  The Digital Belt and Road program in support of regional sustainability , 2018, Int. J. Digit. Earth.

[4]  T. Esch,et al.  Monitoring urbanization in mega cities from space , 2012 .

[5]  Wenhui Kuang,et al.  Simulating dynamic urban expansion at regional scale in Beijing-Tianjin-Tangshan Metropolitan Area , 2011 .

[6]  L. Tian,et al.  Measuring spatio-temporal characteristics of city expansion and its driving forces in Shanghai from 1990 to 2015 , 2017, Chinese Geographical Science.

[7]  Huadong Guo,et al.  A Hierarchical Multiscale Super-Pixel-Based Classification Method for Extracting Urban Impervious Surface Using Deep Residual Network From WorldView-2 and LiDAR Data , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  M. Luoto,et al.  Urban expansion in Zanzibar City, Tanzania: Analyzing quantity, spatial patterns and effects of alternative planning approaches , 2018 .

[9]  H. Hong,et al.  Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India. , 2018, The Science of the total environment.

[10]  Shuqing Zhao,et al.  A comparative study of urban expansion in Beijing, Tianjin and Shijiazhuang over the past three decades , 2015 .

[11]  Na Li,et al.  Urban expansion in China and its spatial-temporal differences over the past four decades , 2016, Journal of Geographical Sciences.

[12]  Dhekra Souissi,et al.  GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia , 2020, Geocarto International.

[13]  Peter M. Ward,et al.  Globalization, regional development, and mega-city expansion in Latin America: Analyzing Mexico City’s peri-urban hinterland , 2003 .

[14]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[15]  M. Friedl,et al.  Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lights , 2016 .

[16]  B. R. Gurjar,et al.  New Directions: Megacities and global change , 2005 .

[17]  S. An,et al.  The spatiotemporal dynamics of rapid urban growth in the Nanjing metropolitan region of China , 2007, Landscape Ecology.

[18]  Zhongchang Sun,et al.  High-Resolution Urban Land Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine , 2019, Remote. Sens..

[19]  Xiao Wang,et al.  Urban expansion in the megacity since 1970s: a case study in Mumbai , 2019, Geocarto International.

[20]  D. Lu,et al.  A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces , 2014 .

[21]  T. Chakraborty,et al.  A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Xinliang Xu,et al.  Quantifying spatiotemporal patterns of urban expansion in China using remote sensing data , 2013 .

[23]  Xiao Wang,et al.  Assessing Interannual Urbanization of China's Six Megacities Since 2000 , 2019, Remote. Sens..

[24]  F. Liu,et al.  Monitoring population dynamics in the Pearl River Delta from 2000 to 2010 , 2020, Geocarto International.

[25]  Qingying Lin,et al.  Land use and landscape pattern changes of Weihai, China based on object-oriented SVM classification from Landsat MSS/TM/OLI images , 2018 .

[26]  Hongxia Li,et al.  Risk probability predictions for coal enterprise infrastructure projects in countries along the Belt and Road Initiative , 2019 .

[27]  C. Justice,et al.  Analysis of the dynamics of African vegetation using the normalized difference vegetation index , 1986 .

[28]  Zhifeng Liu,et al.  Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008 , 2012 .

[29]  Dean M. Hanink,et al.  Analyzing horizontal and vertical urban expansions in three East Asian megacities with the SS-coMCRF model , 2018, Landscape and Urban Planning.

[30]  Yu Hao,et al.  The dynamic links between CO2 emissions, energy consumption and economic development in the countries along "the Belt and Road". , 2018, The Science of the total environment.

[31]  Jamal Jokar Arsanjani,et al.  Urban change in Goa, India , 2017 .

[32]  Chuanrong Zhang,et al.  Detecting horizontal and vertical urban growth from medium resolution imagery and its relationships with major socioeconomic factors , 2017 .

[33]  Xinwu Li,et al.  Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine , 2011 .

[34]  Dengsheng Lu,et al.  An EcoCity model for regulating urban land cover structure and thermal environment: Taking Beijing as an example , 2017, Science China Earth Sciences.

[35]  C. Fang,et al.  The varying driving forces of urban expansion in China: Insights from a spatial-temporal analysis , 2018, Landscape and Urban Planning.

[36]  Xu Han-qiu,et al.  A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI) , 2005, National Remote Sensing Bulletin.

[37]  Huadong Guo,et al.  Synergistic Use of Optical and PolSAR Imagery for Urban Impervious Surface Estimation , 2014 .

[38]  Ying Fan,et al.  Energy investment risk assessment for nations along China’s Belt & Road Initiative , 2018 .

[39]  Fang Liu,et al.  Urbanization in China from the end of 1980s until 2010 – spatial dynamics and patterns of growth using EO-data , 2019, Int. J. Digit. Earth.