City-Level Comparison of Urban Land-Cover Configurations from 2000-2015 across 65 Countries within the Global Belt and Road

The configuration of urban land-covers is essential for improving dwellers' environments and ecosystem services. A city-level comparison of land-cover changes along the Belt and Road is still unavailable due to the lack of intra-urban land products. A synergistic classification methodology of sub-pixel un-mixing, multiple indices, decision tree classifier, unsupervised (SMDU) classification was established in the study to examine urban land covers across 65 capital cities along the Belt and Road during 2000-2015. The overall accuracies of the 15 m resolution urban products (i.e., the impervious surface area, vegetation, bare soil, and water bodies) derived from Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) images were 92.88% and 93.19%, with kappa coefficients of 0.84 and 0.85 in 2000 and 2015, respectively. The built-up areas of 65 capital cities increased from 23,696.25 km(2) to 29,257.51 km(2), with an average growth rate of 370.75 km(2)/y during 2000-2015. Moreover, urban impervious surface area (ISA) expanded with an average rate of 401.92 km(2)/y, while the total area of urban green space (UGS) decreased with an average rate of 17.59 km(2)/y. In different regions, UGS changes declined by 7.37% in humid cities but increased by 14.61% in arid cities. According to the landscape ecology indicators, urban land-cover configurations became more integrated (oShannon's Diversity Index (SHDI) = -0.063; oPatch Density (PD) = 0.054) and presented better connectivity (oConnectance Index (CON) = +0.594). The proposed method in this study improved the separation between ISA and bare soil in mixed pixels, and the 15 m intra-urban land-cover product provided essential details of complex urban landscapes and urban ecological needs compared with contemporary global products. These findings provide valuable information for urban planners dealing with human comfort and ecosystem service needs in urban areas.

[1]  Meng-Lung Lin,et al.  Application of fuzzy models for the monitoring of ecologically sensitive ecosystems in a dynamic semi‐arid landscape from satellite imagery , 2010 .

[2]  Wenhui Kuang,et al.  Continuous land cover change monitoring in the remote sensing big data era , 2017, Science China Earth Sciences.

[3]  Wanfang Zhou,et al.  Finding harmony between the environment and humanity: an introduction to the thematic issue of the Silk Road , 2017, Environmental Earth Sciences.

[4]  Chen Yang,et al.  Changes in Arable Land Demand for Food in India and China: A Potential Threat to Food Security , 2015 .

[5]  Hui Qian,et al.  Building a new and sustainable “Silk Road economic belt” , 2015, Environmental Earth Sciences.

[6]  Massoud Tajrishy,et al.  Evaluation of permeable pavement responses to urban surface runoff. , 2017, Journal of environmental management.

[7]  Hai-long Ma,et al.  Quantifying the relationship between urban development intensity and carbon dioxide emissions using a panel data analysis , 2015 .

[8]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[9]  Asmala Ahmad,et al.  Analysis of Maximum Likelihood Classificationon Multispectral Data , 2012 .

[10]  Dengsheng Lu,et al.  Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier. , 2011, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[11]  Andrés Manuel García,et al.  Cellular automata models for the simulation of real-world urban processes: A review and analysis , 2010 .

[12]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[13]  Xia Li,et al.  A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area , 2015, Remote. Sens..

[14]  Ken W. F. Howard,et al.  The new “Silk Road Economic Belt” as a threat to the sustainable management of Central Asia’s transboundary water resources , 2016, Environmental Earth Sciences.

[15]  K. Seto,et al.  It's Time for an Urbanization Science , 2013 .

[16]  Evgeny Shvarts,et al.  China’s new Eurasian ambitions: the environmental risks of the Silk Road Economic Belt , 2017 .

[17]  Lincoln C. Chen,et al.  China's Silk Road and global health , 2017, The Lancet.

[18]  G. Rasul.  Food, water, and energy security in South Asia: A nexus perspective from the Hindu Kush Himalayan region☆ , 2014 .

[19]  A. Berg,et al.  Present and future Köppen-Geiger climate classification maps at 1-km resolution , 2018, Scientific Data.

[20]  F. Sager,et al.  How to organize secondary capital city regions: Institutional drivers of locational policy coordination , 2018, Governance.

[21]  A. Cazenave,et al.  The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables , 2013 .

[22]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[23]  Guiying Li,et al.  Methods to extract impervious surface areas from satellite images , 2014, Int. J. Digit. Earth.

[24]  Theodoros N. Arvanitis,et al.  A constrained least‐squares approach to the automated quantitation of in vivo 1H magnetic resonance spectroscopy data , 2011, Magnetic resonance in medicine.

[25]  Bo Huang,et al.  Effects of land use and transportation on carbon sources and carbon sinks: A case study in Shenzhen, China , 2014 .

[26]  Linda Steg,et al.  Climate change perceptions and their individual-level determinants: A cross-European analysis , 2019, Global Environmental Change.

[27]  B. Liu,et al.  A 2010 update of National Land Use/Cover Database of China at 1:100000 scale using medium spatial resolution satellite images , 2014 .

[28]  Fengqin Yan,et al.  Examining urban land-cover characteristics and ecological regulation during the construction of Xiong’an New District, Hebei Province, China , 2018, Journal of Geographical Sciences.

[29]  Yaoliang Chen,et al.  Mapping the land-cover distribution in arid and semiarid urban landscapes with Landsat Thematic Mapper imagery , 2015 .

[30]  K. Seto,et al.  Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools , 2012, Proceedings of the National Academy of Sciences.

[31]  W. Sanderson,et al.  The end of world population growth , 2001, Nature.

[32]  Le Yu,et al.  Difficult to map regions in 30 m global land cover mapping determined with a common validation dataset , 2018 .

[33]  Ferri Stefano,et al.  Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014 , 2016 .

[34]  P. Marpu,et al.  Temperature-land cover interactions: The inversion of urban heat island phenomenon in desert city areas , 2013 .

[35]  Qihao Weng,et al.  Improving Urban Impervious Surface Mapping by Linear Spectral Mixture Analysis and Using Spectral Indices , 2015 .

[36]  Mohamed Ouessar,et al.  Identification of suitable sites for rainwater harvesting structures in arid and semi-arid regions: A review , 2016, International Soil and Water Conservation Research.

[37]  Xiaoping Liu,et al.  High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform , 2018 .

[38]  Dawen Yang,et al.  Spatio-temporal variation of drought in China during 1961–2012: A climatic perspective , 2015 .

[39]  Jianping Huang,et al.  Global semi-arid climate change over last 60 years , 2016, Climate Dynamics.

[40]  Dengsheng Lu,et al.  Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis , 2016, Science China Earth Sciences.

[41]  H. Nagendra,et al.  Graying, greening and fragmentation in the rapidly expanding Indian city of Bangalore , 2012 .

[42]  None None,et al.  East Asia. , 2018, Global heart.

[43]  Luciano Vieira Dutra,et al.  Examining Spatial Distribution and Dynamic Change of Urban Land Covers in the Brazilian Amazon Using Multitemporal Multisensor High Spatial Resolution Satellite Imagery , 2017, Remote. Sens..

[44]  Chi Zhang,et al.  Impacts of impervious surface expansion on soil organic carbon – a spatially explicit study , 2015, Scientific Reports.

[45]  Kangle Zhang.  Right to Information about, and Involvement in, Environmental Decision Making along the Silk Road Economic Belt , 2017 .

[46]  Bin Zhou,et al.  Vietnam Following in China’s Footsteps : The Third Wave of Emerging Asian Economies , 2008 .

[47]  L. S. Bernstein,et al.  Improved reflectance retrieval from hyper- and multispectral imagery without prior scene or sensor information , 2006, SPIE Remote Sensing.

[48]  Jan Eliasson,et al.  The rising pressure of global water shortages , 2014, Nature.

[49]  E. Ng,et al.  Urban tree design approaches for mitigating daytime urban heat island effects in a high-density urban environment , 2016 .

[50]  C. Colvin,et al.  Multi-functional landscapes in semi arid environments: implications for biodiversity and ecosystem services , 2010, Landscape Ecology.

[51]  Patricia M. Kristjanson,et al.  Is agricultural adaptation to global change in lower-income countries on track to meet the future food production challenge? , 2018, Global Environmental Change.

[52]  Jiyuan Liu,et al.  Spatiotemporal patterns and characteristics of land-use change in China during 2010–2015 , 2018, Journal of Geographical Sciences.

[53]  Edward L. Glaeser,et al.  Entrepreneurship and Urban Growth: An Empirical Assessment with Historical Mines , 2015 .

[54]  P. Gong,et al.  MODIS detected surface urban heat islands and sinks: Global locations and controls , 2013 .

[55]  Shiyuan Xu,et al.  Community-based scenario modelling and disaster risk assessment of urban rainstorm waterlogging , 2011 .

[56]  Jianping Huang,et al.  Accelerated dryland expansion under climate change , 2016 .

[57]  D. Lu,et al.  Use of impervious surface in urban land-use classification , 2006 .

[58]  Manfred Ehlers,et al.  Multi-sensor image fusion for pansharpening in remote sensing , 2010 .

[59]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[60]  S. Linden,et al.  Extending the vegetation–impervious–soil model using simulated EnMAP data and machine learning , 2015 .

[61]  P. Tarolli,et al.  Improving impervious surface estimation: an integrated method of classification and regression trees (CART) and linear spectral mixture analysis (LSMA) based on error analysis , 2018 .

[62]  Justyna Szczudlik-Tatar,et al.  China’s New Silk Road Diplomacy , 2013 .

[63]  J. Ahern.  From fail-safe to safe-to-fail: Sustainability and resilience in the new urban world , 2011 .