Spatiotemporal Characterization of the Urban Expansion Patterns in the Yangtze River Delta Region
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Methods
2.3.1. Speed Index and the Differentiation Index of Urban Expansion
2.3.2. Gravity Center Migration (GCM)
2.3.3. Landscape Indices
2.3.4. Spatial Autocorrelation
3. Results
3.1. Spatiotemporal Patterns of Urban Expansion at the Regional Scale
3.2. Spatiotemporal Patterns of Urban Expansion at the City Scale
3.3. Differentiation Characteristic of the Urban Expansion
3.4. Migration of the Gravity Center of Urban Built-Up Land
3.5. Landscape Patterns of Urban Built-Up Land
4. Discussion
4.1. Expansion Rates and Differences
4.2. Expansion Directions
4.3. Landscape Patterns
4.4. Innovations and Limitations
5. Conclusions
- The built-up land area of the Yangtze River Delta Region continues to increase with an expansion of nearly double in size, from 29,600.715 to 48,013.895 km2. The expansion speed of the Yangtze River Delta Region shows a significant spatial agglomeration trend. The degree of agglomeration first increases, then decreases with time. The high-speed expansion areas are mainly concentrated in the middle and south of the Yangtze River Delta Region. This is mostly affected by the differences in the levels of development of the cities in the Yangtze River Delta Region.
- There are significant differences in the expansion direction of built-up land in the Yangtze River Delta Region, as each city has a different impact on the entire area. Eventually, the center of gravity is moving toward the faster-developing southwestern region. An in-depth analysis of the locational and movement trends of the center of gravity would have an important and theoretical guiding significance, as well as practical operational implications for future strategic plans that would continue the rapid development of the Yangtze River Delta Region.
- During 1995–2018, the spatial structure of the Yangtze River Delta Region tended to cluster, the shape of built-up land became simpler, compactness improved, and fragmentation decreased. Cities with rapid expansion had simpler shapes and more compact structures, whereas cities with slower expansion had more complex shapes and higher fragmentation.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
City Name | Abbreviation | City Name | Abbreviation | City Name | Abbreviation |
---|---|---|---|---|---|
Anqing | AQ | Jiaxing | JX | Suqian | SQ |
Bengbu | BB | Jinhua | JH | Suzhou | SZ |
Bozhou | BZ | Lishui | LS | Taizhou | TZ |
Changzhou | CA | Lianyungang | LYG | Tongling | TL |
Chizhou | CI | Luan | LA | Wenzhou | WZ |
Chuzhou | CU | Lishui | LS | Wuxi | WX |
Fuyang | FY | Maanshan | MAS | Wuhu | WH |
Hangzhou | HZ | Nanjing | NJ | Xuzhou | XZ |
Hefei | HF | Nantong | NT | Xuancheng | XC |
Huzhou | HZ | Ningbo | NB | Yancheng | YC |
Huaian | HA | Quzhou | QZ | Yangzhou | YZ |
Huaibei | HB | Shanghai | SH | Zhenjiang | ZJ |
Huainan | HN | Shaoxing | SX | Zhoushan | ZS |
Huangshan | HS | Suzhou | SU |
City | 1995–2000 | 2000–2005 | 2005–2010 | 2010–2015 | 2015–2018 | 1995–2018 |
---|---|---|---|---|---|---|
AQ | 0.572 | 1.481 | 4.749 | 1.298 | 1.187 | 2.207 |
BB | 0.412 | 0.154 | 1.331 | 1.671 | 0.201 | 0.851 |
BZ | 0.436 | 0.134 | 0.966 | 0.997 | 0.865 | 0.702 |
CA | 1.558 | 3.062 | 7.179 | 0.814 | 3.388 | 4.072 |
CI | 0.938 | 0.918 | 10.794 | 2.527 | 2.181 | 4.448 |
CU | 2.654 | 0.329 | 1.951 | 2.935 | 0.821 | 2.107 |
FY | 0.705 | 0.311 | 0.979 | 0.651 | 1.353 | 0.804 |
HZ | 1.519 | 8.216 | 3.677 | 3.721 | 4.059 | 6.047 |
HF | 1.636 | 1.210 | 4.130 | 1.892 | 2.110 | 2.657 |
HU | 0.173 | 5.873 | 5.245 | 3.475 | 5.535 | 5.453 |
HA | 0.141 | 0.172 | 3.598 | 0.317 | 1.008 | 1.105 |
HB | 0.376 | 0.870 | 2.252 | 1.327 | 4.230 | 1.832 |
HN | 0.704 | 0.665 | 1.449 | 0.765 | 2.085 | 1.154 |
HS | 1.503 | 0.945 | 16.122 | 5.288 | −3.300 | 5.725 |
JX | 0.995 | 6.883 | 2.829 | 1.858 | 3.088 | 4.014 |
JH | 4.908 | 22.429 | 3.560 | 2.516 | 6.272 | 13.752 |
LS | 3.170 | 17.715 | 10.437 | 3.027 | 19.221 | 21.892 |
LYG | −0.110 | 0.190 | 3.354 | 0.180 | −3.386 | 0.273 |
LA | 0.335 | 0.619 | 1.562 | 1.660 | 3.324 | 1.504 |
MAS | 3.785 | 0.687 | 5.024 | 2.704 | −1.111 | 2.996 |
NJ | 2.547 | 1.993 | 6.694 | 0.378 | 2.917 | 3.623 |
NT | 0.935 | 2.408 | 15.322 | 5.508 | −1.033 | 6.782 |
NB | 1.754 | 14.744 | 1.732 | 3.049 | 1.234 | 6.321 |
QZ | 0.385 | 24.404 | 3.714 | 4.466 | 9.032 | 13.791 |
SH | 1.150 | 5.501 | 5.418 | 1.808 | 3.994 | 4.750 |
SX | 1.730 | 13.251 | 3.220 | 3.502 | 3.128 | 7.373 |
SU | 4.307 | 7.141 | 11.761 | 1.008 | 2.195 | 8.401 |
SQ | 0.255 | 0.079 | 3.590 | 0.363 | 0.727 | 1.077 |
SZ | 0.977 | 0.303 | 0.712 | 0.764 | 1.427 | 0.843 |
TZ | 0.940 | 13.164 | 2.894 | 7.325 | 9.443 | 10.802 |
TA | 0.915 | 1.359 | 6.728 | 1.591 | 1.746 | 3.024 |
TL | 0.559 | 0.940 | 7.401 | 2.472 | −6.099 | 1.538 |
WZ | 3.878 | 14.769 | 2.364 | 3.430 | 7.519 | 10.140 |
WX | 2.226 | 4.824 | 7.256 | 0.672 | 0.911 | 4.331 |
WH | 1.639 | 0.734 | 6.584 | 6.125 | −2.293 | 3.537 |
XZ | 0.988 | 0.017 | 3.895 | 0.331 | 0.989 | 1.363 |
XC | 1.651 | 0.788 | 7.951 | 6.823 | −2.108 | 4.242 |
YC | 1.061 | 0.557 | 2.170 | 0.937 | −4.674 | 0.347 |
YZ | 0.279 | 0.985 | 7.018 | 0.522 | 2.141 | 2.476 |
ZJ | 1.663 | 0.979 | 6.293 | 0.982 | 3.032 | 3.085 |
ZS | −2.880 | 8.704 | 1.921 | 0.190 | 34.233 | 7.632 |
References
- Li, S.; He, Y.; Xu, H.; Zhu, C.; Dong, B.; Lin, Y.; Si, B.; Deng, J.; Wang, K. Impacts of urban expansion forms on ecosystem services in urban agglomerations: A case study of Shanghai-Hangzhou Bay urban agglomeration. Remote Sens. 2021, 13, 1908. [Google Scholar] [CrossRef]
- Lei, H.; Koch, J.; Shi, H. An analysis of spatio-temporal urbanization patterns in Northwest China. Land 2020, 9, 441. [Google Scholar] [CrossRef]
- Zhu, C.; Zhang, X.; Wang, K.; Yuan, S.; Yang, L.; Skitmore, M. Urban-rural construction land transition and its coupling relationship with population flow in China’s urban agglomeration region. Cities 2020, 101, 102701. [Google Scholar] [CrossRef]
- Zhao, C.; Wu, Y.; Ye, X.; Wu, B.; Kudva, S. The direct and indirect drag effects of land and energy on urban economic growth in the Yangtze River Delta, China. Environ. Dev. Sustain. 2019, 21, 2945–2962. [Google Scholar] [CrossRef]
- Han, J.; Meng, X.; Zhou, X.; Yi, B.; Liu, M.; Xiang, W.-N. A long-term analysis of urbanization process, landscape change, and carbon sources and sinks: A case study in China’s Yangtze River Delta region. J. Clean. Prod. 2017, 141, 1040–1050. [Google Scholar] [CrossRef]
- Han, J.; Liu, J. Urban spatial interaction analysis using inter-city transport big data: A case study of the Yangtze River Delta urban agglomeration of China. Sustainability 2018, 10, 4459. [Google Scholar] [CrossRef] [Green Version]
- Liu, Y.Q.; Li, L.; Chen, L.Q.; Cheng, L.; Zhou, X.S.; Cui, Y.F.; Li, H.; Liu, W.Q. Urban growth simulation in different scenarios using the SLEUTH model: A case study of Hefei, East China. PLoS ONE 2019, 14, e0224998. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Magarotto, M.G.; da Costa, M.F.; Tenedório, J.A.; Silva, C.P. Vertical growth in a coastal city: An analysis of Boa Viagem (Recife, Brazil). J. Coast. Conserv. 2016, 20, 31–42. [Google Scholar] [CrossRef]
- Mandarino, A.; Pepe, G.; Cevasco, A.; Brandolini, P. Quantitative Assessment of Riverbed Planform Adjustments, Channelization, and Associated Land Use/Land Cover Changes: The Ingauna Alluvial-Coastal Plain Case (Liguria, Italy). Remote Sens. 2021, 13, 3775. [Google Scholar] [CrossRef]
- Li, W.; Han, C.; Li, W.; Zhou, W.; Han, L. Multi-scale effects of urban agglomeration on thermal environment: A case of the Yangtze River Delta megaregion, China. Sci. Total Environ. 2020, 713, 136556. [Google Scholar] [CrossRef]
- Wang, X.; Sun, X.; Tang, J.; Yang, X. Urbanization-induced regional warming in Yangtze River Delta: Potential role of anthropogenic heat release. Int. J. Climatol. 2015, 35, 4417–4430. [Google Scholar] [CrossRef]
- Wu, Q.; Guo, R.; Luo, J.; Chen, C. Spatiotemporal evolution and the driving factors of PM2.5 in Chinese urban agglomerations between 2000 and 2017. Ecol. Indic. 2021, 125, 107491. [Google Scholar] [CrossRef]
- Delia, K.A.; Haney, C.R.; Dyer, J.L.; Paul, V.G. Spatial analysis of a Chesapeake Bay Sub-Watershed: How land use and precipitation patterns impact water quality in the James River. Water 2021, 13, 1592. [Google Scholar] [CrossRef]
- Hemmati, M.; Ellingwood, B.R.; Mahmoud, H.N. The Role of Urban Growth in Resilience of Communities Under Flood Risk. Earths Future 2020, 8, e2019EF001382. [Google Scholar] [CrossRef] [Green Version]
- Chi, A. Human interference and environmental instability: Addressing the environmental consequences of rapid urban growth in Bamenda, Cameroon. Environ. Urban. 1998, 10, 161–174. [Google Scholar] [CrossRef]
- Mbow, C.; Diop, A.; Diaw, A.T.; Niang, C.I. Urban sprawl development and flooding at Yeumbeul suburb (Dakar-Senegal). Afr. J. Environ. Sci. Technol. 2008, 2, 75–88. [Google Scholar]
- Rojas, O.; Mardones, M.; Rojas, C.; Martinez, C.; Flores, L. Urban Growth and Flood Disasters in the Coastal River Basin of South-Central Chile (1943–2011). Sustainability 2017, 9, 195. [Google Scholar] [CrossRef] [Green Version]
- Sahana, M.; Hong, H.; Sajjad, H. Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India. Sci. Total Environ. 2018, 628–629, 1557–1566. [Google Scholar] [CrossRef] [PubMed]
- Yan, Y.; Ju, H.; Zhang, S.; Jiang, W. Spatiotemporal patterns and driving forces of urban expansion in coastal areas: A study on urban agglomeration in the Pearl River Delta, China. Sustainability 2020, 12, 191. [Google Scholar] [CrossRef] [Green Version]
- Zeng, C.; Zhao, Z.; Wen, C.; Yang, J.; Lv, T. Effect of complex road networks on intensive land use in China’s Beijing-Tianjin-Hebei urban agglomeration. Land 2020, 9, 532. [Google Scholar] [CrossRef]
- Fang, C.; Yu, D. Urban agglomeration: An evolving concept of an emerging phenomenon. Landsc. Urb. Plan. 2017, 162, 126–136. [Google Scholar] [CrossRef]
- Fu, Y.; Zhang, X. Mega urban agglomeration in the transformation era: Evolving theories, research typologies and governance. Cities 2020, 105, 102813. [Google Scholar] [CrossRef]
- Chan, R.C.; Shimou, Y. Urbanization and sustainable metropolitan development in China: Patterns, problems and prospects. GeoJournal 1999, 49, 269–277. [Google Scholar] [CrossRef]
- Pawe, C.K.; Saikia, A. Decumbent development: Urban sprawl in the Guwahati Metropolitan Area, India. Singap. J. Trop. Geogr. 2020, 41, 226–247. [Google Scholar] [CrossRef]
- Kumar, A.; Pandey, A.C.; Hoda, N.; Jeyaseelan, A.T. Evaluating the Long-term Urban Expansion of Ranchi Urban Agglomeration, India Using Geospatial Technology. J. Indian Soc. Remote Sens. 2011, 39, 213–224. [Google Scholar] [CrossRef]
- Mondal, B.; Das, D.N.; Bhatta, B. Integrating cellular automata and Markov techniques to generate urban development potential surface: A study on Kolkata agglomeration. Geocarto Int. 2017, 32, 401–419. [Google Scholar] [CrossRef]
- Wang, J.; Fang, C.; Wang, Z. Advantages and dynamics of urban agglomeration development on Yangtze River Delta. J. Geogr. Sci. 2012, 22, 521–534. [Google Scholar] [CrossRef]
- Liu, L.; Liu, J.; Liu, Z.; Xu, X.; Wang, B. Analysis on the spatio-temporal characteristics of urban expansion and the complex driving mechanism: Taking the Pearl River Delta urban agglomeration as a case. Complexity 2020, 2020, 8157143. [Google Scholar] [CrossRef]
- Li, Y.; Liu, G. Characterizing spatiotemporal pattern of land use change and its driving force based on GIS and Landscape Analysis Techniques in Tianjin during 2000–2015. Sustainability 2017, 9, 894. [Google Scholar] [CrossRef] [Green Version]
- Zhao, X.; Zhang, M.; Li, Y.; Huang, X.; Wang, B.; Zhang, L. Urban residential land expansion and agglomeration in China: A spatial analysis approach. Environ. Dev. Sustain. 2020, 22, 5317–5335. [Google Scholar] [CrossRef]
- Wang, Z.; Fang, C.; Zhang, X. Spatial expansion and potential of construction land use in the Yangtze River Delta. J. Geogr. Sci. 2015, 25, 851–864. [Google Scholar] [CrossRef] [Green Version]
- Zhu, J.; Ding, N.; Li, D.; Sun, W.; Xie, Y.; Wang, X. Spatiotemporal Analysis of the Nonlinear Negative Relationship between Urbanization and Habitat Quality in Metropolitan Areas. Sustainability 2020, 12, 669. [Google Scholar] [CrossRef] [Green Version]
- Yu, X.; Wu, Z.; Zheng, H.; Li, M.; Tan, T. How urban agglomeration improve the emission efficiency? A spatial econometric analysis of the Yangtze river delta urban agglomeration in China. J. Environ. Manag. 2020, 263, 110061. [Google Scholar] [CrossRef]
- Tenedório, J.A.; Rebelo, C.; Estanqueiro, R.; Henriques, C.D.; Marques, L.; Gonçalves, J.A. New Developments in Geographical Information Technology for Urban and Spatial Planning. In Technologies for Urban and Spatial Planning: Virtual Cities and Territories; IGI Global: Hershey, PA, USA, 2014; pp. 196–227. [Google Scholar] [CrossRef]
- Yu, W.; Zhou, W. The spatiotemporal pattern of urban expansion in China: A comparison study of three urban megaregions. Remote Sens. 2017, 9, 45. [Google Scholar] [CrossRef] [Green Version]
- Peng, J.; Liu, Y.; Shen, H.; Xie, P.; Hu, X.; Wang, Y. Using impervious surfaces to detect urban expansion in Beijing of China in 2000s. Chin. Geogr. Sci. 2016, 26, 229–243. [Google Scholar] [CrossRef] [Green Version]
- Chen, M.; Zhou, Y.; Hu, M.; Zhou, Y. Influence of urban scale and urban expansion on the urban heat island effect in Metropolitan areas: Case study of Beijing-Tianjin-Hebei urban agglomeration. Remote Sens. 2020, 12, 3491. [Google Scholar] [CrossRef]
- Kim, S. Urban development and landscape change in the Yangtze River Delta region in China. Int. J. Sustain. Dev. World Ecol. 2019, 26, 141–153. [Google Scholar] [CrossRef]
- Xu, G.; Jiao, L.; Liu, J.; Shi, Z.; Zeng, C.; Liu, Y. Understanding urban expansion combining macro patterns and micro dynamics in three Southeast Asian megacities. Sci. Total Environ. 2019, 660, 375–383. [Google Scholar] [CrossRef]
- Zhang, D.-D.; Zhang, L. Land cover change in the central region of the lower Yangtze River based on Landsat imagery and the Google Earth Engine: A case study in Nanjing, China. Sensors 2020, 20, 2091. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fei, W.; Zhao, S. Urban land expansion in China’s six megacities from 1978 to 2015. Sci. Total Environ. 2019, 664, 60–71. [Google Scholar] [CrossRef]
- Luo, J.; Xing, X.; Wu, Y.; Zhang, W.; Chen, R.S. Spatio-temporal analysis on built-up land expansion and population growth in the Yangtze River Delta Region, China: From a coordination perspective. Appl. Geogr. 2018, 96, 98–108. [Google Scholar] [CrossRef]
- Sun, W.; Shan, J.; Wang, Z.; Wang, L.; Lu, D.; Jin, Z.; Yu, K. Geospatial analysis of urban expansion using remote sensing methods and data: A case study of Yangtze River Delta, China. Complexity 2020, 3239471. [Google Scholar] [CrossRef]
- Ye, C.; Zhu, J.J.; Li, S.M.; Yang, S.; Chen, M.X. Assessment and analysis of regional economic collaborative development within an urban agglomeration: Yangtze River Delta as a case study. Habitat Int. 2019, 83, 20–29. [Google Scholar] [CrossRef]
- Du, H.; Wang, D.; Wang, Y.; Zhao, X.; Qin, F.; Jiang, H.; Cai, Y. Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration. Sci. Total Environ. 2016, 571, 461–470. [Google Scholar] [CrossRef] [PubMed]
- Cheng, L.; Li, L.; Chen, L.; Hu, S.; Yuan, L.; Liu, Y.; Cui, Y.; Zhang, T. Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014–2017 Period. Int. J. Environ. Res. Public Health 2019, 16, 3522. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Xu, Y.; Xu, Y.; Wang, Y.; Wu, L.; Li, G.; Song, S. Spatial and temporal trends of reference crop evapotranspiration and its influential variables in Yangtze River Delta, eastern China. Theor. Appl. Climatol. 2017, 130, 945–958. [Google Scholar] [CrossRef]
- Liu, J.Y.; Liu, M.L.; Tian, H.Q.; Zhuang, D.F.; Zhang, Z.X.; Zhang, W.; Tang, X.M.; Deng, X.Z. Spatial and temporal patterns of China’s cropland during 1990–2000: An analysis based on Landsat TM data. Remote Sens. Environ. 2005, 98, 442–456. [Google Scholar] [CrossRef]
- Liu, J.; Liu, M.; Deng, X.; Zhuang, D.; Zheng, Z.; Luo, D. The land use and land cover change database and its relative studies in China. J. Geogr. Sci. 2002, 12, 275–282. [Google Scholar]
- Wu, W.; Zhao, S.; Zhu, C.; Jiang, J. A comparative study of urban expansion in Beijing, Tianjin and Shijiazhuang over the past three decades. Landsc. Urb. Plan. 2015, 134, 93–106. [Google Scholar] [CrossRef]
- Guang, X.; Fang, C.; Zhou, M.; Wu, H. Spatial and temporal characteristics of spatial expansion of urban land in Wuhan urban agglomeration. J. Nat. Resour. 2012, 27, 1147–1459. [Google Scholar]
- Wang, H.; Zhang, B.; Liu, Y.; Liu, Y.; Xu, S.; Zhao, Y.; Chen, Y.; Hong, S. Urban expansion patterns and their driving forces based on the center of gravity-GTWR model: A case study of the Beijing-Tianjin-Hebei urban agglomeration. J. Geogr. Sci. 2020, 30, 297–318. [Google Scholar] [CrossRef]
- Wu, X.; Fang, X.; Miao, Y.; Wang, K.; Pang, C. Study on urban expansion of Hefei city based on Landsat data. J. Zhejiang Univ. 2017, 44, 631–639. [Google Scholar] [CrossRef]
- Zeng, C.; Liu, Y.; Stein, A.; Jiao, L. Characterization and spatial modeling of urban sprawl in the Wuhan metropolitan area, China. Int. J. Appl. Earth Obs. Geoinf. 2015, 34, 10–24. [Google Scholar] [CrossRef]
- Liu, X.; Dong, G.; Wang, X.; Xue, Z.; Jiang, M.; Lu, X.; Zhang, Y. Characterizing the spatial pattern of marshlands in the Sanjiang Plain, Northeast China. Ecol. Eng. 2013, 53, 335–342. [Google Scholar] [CrossRef]
- Madasa, A.; Orimoloye, I.R.; Ololade, O.O. Application of geospatial indices for mapping land cover/use change detection in a mining area. J. Afr. Earth Sci. 2021, 175, 104108. [Google Scholar] [CrossRef]
- Liu, Y.; Cao, X.; Li, T. Identifying driving forces of built-up land expansion based on the geographical detector: A case study of Pearl River Delta urban agglomeration. Int. J. Environ. Res. Public Health 2020, 17, 1759. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Feng, D.; Bao, W.; Fu, M.; Zhang, M.; Sun, Y. Current and future land use characters of a national central city in eco-fragile region-A case study in Xi’an city based on FLUS model. Land 2021, 10, 286. [Google Scholar] [CrossRef]
- Tian, Y. Mapping suburbs based on spatial interactions and effect analysis on ecological landscape change: A case study of Jiangsu Province from 1998 to 2018, Eastern China. Land 2020, 9, 159. [Google Scholar] [CrossRef]
- Wadduwage, S.; Millington, A.; Crossman, N.D.; Sandhu, H. Agricultural Land Fragmentation at Urban Fringes: An Application of Urban-To-Rural Gradient Analysis in Adelaide. Land 2017, 6, 28. [Google Scholar] [CrossRef] [Green Version]
- Padmanaban, R.; Bhowmik, A.K.; Cabral, P.; Zamyatin, A.; Almegdadi, O.; Wang, S. Modelling urban sprawl using remotely sensed data: A case study of Chennai city, Tamilnadu. Entropy 2017, 19, 163. [Google Scholar] [CrossRef] [Green Version]
- Wang, J.; Li, L.; Zhang, T.; Chen, L.Q.; Wen, M.X.; Liu, W.Q.; Hu, S. Optimal Grain Size Based Landscape Pattern Analysis for Shanghai Using Landsat Images from 1998 to 2017. Pol. J. Environ. Stud. 2021, 30, 2799–2813. [Google Scholar] [CrossRef]
- Li, H.; Li, L.; Chen, L.Q.; Zhou, X.S.; Cui, Y.F.; Liu, Y.Q.; Liu, W.Q. Mapping and characterizing spatiotemporal dynamics of impervious surfaces using Landsat images: A case study of Xuzhou, East China from 1995 to 2018. Sustainability 2019, 11, 1224. [Google Scholar] [CrossRef] [Green Version]
- Sung, C.-H.; Liaw, S.-C. Using spatial pattern analysis to explore the relationship between vulnerability and resilience to natural hazards. Int. J. Environ. Res. Public Health 2021, 18, 5634. [Google Scholar] [CrossRef] [PubMed]
- Ke, X.L.; Wang, X.Y.; Guo, H.X.; Yang, C.; Zhou, Q.; Mougharbel, A. Urban ecological security evaluation and spatial correlation researchbased on data analysis of 16 cities in Hubei Province of China. J. Clean. Prod. 2021, 311, 127613. [Google Scholar] [CrossRef]
- Cui, Y.F.; Li, L.; Chen, L.Q.; Zhang, Y.; Cheng, L.; Zhou, X.S.; Yang, X.Y. Land-Use Carbon Emissions Estimation for the Yangtze River Delta Urban Agglomeration Using 1994–2016 Landsat Image Data. Remote Sens. 2018, 10, 1334. [Google Scholar] [CrossRef] [Green Version]
- Wang, N.; Li, J.; Duan, L.; Chen, C.; Gao, Y.; Fan, P. Comparative study on the urban sprawl and its driving force in two Metropolitan areas, Yangtze River Delta and Central Plains. J. Henan Univ. 2017, 47, 681–692. [Google Scholar] [CrossRef]
- Zhao, S.; Zhou, D.; Zhu, C.; Qu, W.; Zhao, J.; Sun, Y.; Huang, D.; Wu, W.; Liu, S. Rates and patterns of urban expansion in China’s 32 major cities over the past three decades. Landsc. Ecol. 2015, 30, 1541–1559. [Google Scholar] [CrossRef]
- Qian, Z.; Fu, H.; Wang, Y.; Zhang, Y. Characteristics of urban expansion and morphological evolution in Nanjing from 2004 to 2016. Remote Sens. Land Resour. 2019, 31, 149–156. [Google Scholar] [CrossRef]
- Tong, C.; Cheng, L.; Yunjian, L. Changes in landscape pattern of built- up land and its driving factors during urban sprawl. Acta Ecol. Sin. 2020, 40, 3283–3294. [Google Scholar] [CrossRef]
- Liu, Y.; He, Q.; Tan, R.; Liu, Y.; Yin, C. Modeling different urban growth patterns based on the evolution of urban form: A case study from Huangpi, Central China. Appl. Geogr. 2016, 66, 109–118. [Google Scholar] [CrossRef]
- Hu, P.; Li, F.; Hu, D.; Sun, X.; Liu, Y.; Chen, X. Spatial and temporal characteristics of urban expansion in Pearl River Delta urban agglomeration from 1980 to 2015. Acta Ecol. Sin. 2021, 41, 1–11. [Google Scholar] [CrossRef]
Metrics | Acronym | Units | Description |
---|---|---|---|
Largest Patch Index [58] | LPI | Percent | The percentage of the landscape comprised of the largest patch. |
Number of Patches [59] | NP | None | The number of patches of landscape classes. |
Patch Density [60] | PD | Number per km2 | The extent of subdivisions in or the fragmentation of the patch type. |
Clumpiness Index [61] | CLUMPY | Percent | The aggregation degree of the landscape. |
Landscape Shape Index [62] | LSI | None | The complexity of urban growth. |
Patch cohesion index [63] | COHESION | None | The physical connectedness of the corresponding patch type. |
Year | Longitude (°) | Latitude (°) | Direction (°) | Distance (m) | Rate (m/year) | |
---|---|---|---|---|---|---|
Yangtze River Delta | 1995 | 118.214 | 32.316 | |||
2000 | 118.226 | 32.313 | Southeast 13.279 | 1227.763 | 245.553 | |
2005 | 118.229 | 32.311 | Southeast 52.816 | 377.295 | 75.459 | |
2010 | 118.151 | 32.290 | Southwest 18.007 | 7626.807 | 1525.361 | |
2015 | 118.123 | 32.298 | Northwest 17.246 | 2820.397 | 564.079 | |
2018 | 118.146 | 32.287 | Southwest 27.985 | 2489.969 | 829.990 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yu, Z.; Chen, L.; Li, L.; Zhang, T.; Yuan, L.; Liu, R.; Wang, Z.; Zang, J.; Shi, S. Spatiotemporal Characterization of the Urban Expansion Patterns in the Yangtze River Delta Region. Remote Sens. 2021, 13, 4484. https://doi.org/10.3390/rs13214484
Yu Z, Chen L, Li L, Zhang T, Yuan L, Liu R, Wang Z, Zang J, Shi S. Spatiotemporal Characterization of the Urban Expansion Patterns in the Yangtze River Delta Region. Remote Sensing. 2021; 13(21):4484. https://doi.org/10.3390/rs13214484
Chicago/Turabian StyleYu, Ziqi, Longqian Chen, Long Li, Ting Zhang, Lina Yuan, Ruiyang Liu, Zhiqiang Wang, Jinyu Zang, and Shuai Shi. 2021. "Spatiotemporal Characterization of the Urban Expansion Patterns in the Yangtze River Delta Region" Remote Sensing 13, no. 21: 4484. https://doi.org/10.3390/rs13214484