Urban growth simulation in different scenarios using the SLEUTH model: A case study of Hefei, East China

Autoři: Yunqiang Liu aff001;  Long Li aff001;  Longqian Chen aff001;  Liang Cheng aff001;  Xisheng Zhou aff001;  Yifan Cui aff001;  Han Li aff001;  Weiqiang Liu aff001
Působiště autorů: School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, Jiangsu, China aff001;  Engineering Research Center of Ministry of Education for Mine Ecological Restoration, China University of Mining and Technology, Xuzhou, Jiangsu, China aff002;  Department of Geography, Earth System Science, Vrije Universiteit Brussel, Brussels, Belgium aff003;  College of Yingdong Agricultural Science and Engineering, Shaoguan University, Shaoguan, Guangdong, China aff004
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224998


As uncontrolled urban growth has increasingly challenged the sustainable use of urban land, it is critically important to model urban growth from different perspectives. Using the SLEUTH (Slope, Land use, Exclusion, Urban, Transportation, and Hill-shade) model, the historical data of Hefei in 2000, 2005, 2010, and 2015 were collected and input to simulate urban growth from 2015 to 2040. Three different urban growth scenarios were considered, namely a historical growth scenario, an urban planning growth scenario, and a land suitability growth scenario. Prediction results show that by 2040 urban built-up land would increase to 1434 km2 in the historical growth scenario, to 1190 km2 in the urban planning growth scenario, and to 1217 km2 in the land suitability growth scenario. We conclude that (1) exclusion layers without effective limits might result in unreasonable prediction of future built-up land; (2) based on the general land use map, the urban growth prediction took the governmental policies into account and could reveal the development hotspots in urban planning; and (3) the land suitability scenario prediction was the result of the trade-off between ecological land and built-up land as it used the MCR -based (minimum cumulative resistance model) land suitability assessment result. It would help to form a compact urban space and avoid excessive protection of farmland and ecological land. Findings derived from this study may provide urban planners with interesting insights on formulating urban planning strategies.

Klíčová slova:

Grasslands – Land use – Roads – Simulation and modeling – Soil ecology – Urban areas – Urban ecology – Spreading centers


1. Zhou X, Chen H. Impact of urbanization-related land use land cover changes and urban morphology changes on the urban heat island phenomenon. Sci Total Environ. 2018;635: 1467–1476. doi: 10.1016/j.scitotenv.2018.04.091 29710597

2. Estoque RC, Murayama Y. Landscape pattern and ecosystem service value changes: Implications for environmental sustainability planning for the rapidly urbanizing summer capital of the Philippines. Landsc Urban Plan. 2013;116: 60–72. doi: 10.1016/j.landurbplan.2013.04.008

3. Bai Y, Deng X, Jiang S, Zhang Q, Wang Z. Exploring the relationship between urbanization and urban eco-efficiency: Evidence from prefecture-level cities in China. J Clean Prod. 2018;195: 1487–1496. doi: 10.1016/j.jclepro.2017.11.115

4. Hu Z, Lo CP. Modeling urban growth in Atlanta using logistic regression. Comput Environ Urban Syst. 2007;31(6): 667–688. doi: 10.1016/j.compenvurbsys.2006.11.001

5. Vermeiren K, Van Rompaey A, Loopmans M, Serwajja E, Mukwaya P. Urban growth of Kampala, Uganda: Pattern analysis and scenario development. Landsc Urban Plan. 2012;106(2): 199–206. doi: 10.1016/j.landurbplan.2012.03.006

6. Alsharif AAA, Pradhan B. Urban sprawl analysis of Tripoli Metropolitan City (Libya) using remote sensing data and multivariate logistic regression model. J Indian Soc Remote Sens. 2014;42(1): 149–163. doi: 10.1007/s12524-013-0299-7

7. Hosseinali F, Alesheikh AA, Nourian F. Agent-based modeling of urban land-use development, case study: Simulating future scenarios of Qazvin city. Cities. 2013;31: 105–113. doi: 10.1016/j.cities.2012.09.002

8. Santé I, García AM, Miranda D, Crecente R. Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landsc Urban Plan. 2010;96(2): 108–122. doi: 10.1016/j.landurbplan.2010.03.001

9. Triantakonstantis D, Mountrakis G. Urban growth prediction: A review of computational models and human perceptions. J Geogr Inf Syst. 2012;4(6): 555–587. doi: 10.4236/jgis.2012.46060

10. Musa SI, Hashim M, Reba MNM. A review of geospatial-based urban growth models and modelling initiatives. Geocarto Int. 2017;32(8): 813–833. doi: 10.1080/10106049.2016.1213891

11. Aburas MM, Ho YM, Ramli MF, Ash’aari ZH. The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. Int J Appl Earth Obs Geoinf. 2016;52: 380–389. doi: 10.1016/j.jag.2016.07.007

12. Liu Y, Hu Y, Long S, Liu L, Liu X. Analysis of the effectiveness of urban land-use-change models based on the measurement of spatio-temporal, dynamic urban growth: A cellular automata case study. Sustain. 2017;9(5): 1–15. doi: 10.3390/su9050796

13. Hua L, Tang L, Cui S, Yin K. Simulating urban growth using the SLEUTH model in a coastal peri-urban district in China. Sustain. 2014;6(6): 3899–3914. doi: 10.3390/su6063899

14. Zheng Q, Yang X, Wang K, Huang L, Shahtahmassebi AR, Gan M, et al. Delimiting urban growth boundary through combining land suitability evaluation and cellular automata. Sustain. 2017;9(12): 2213. doi: 10.3390/su9122213

15. Rafiee R, Mahiny AS, Khorasani N, Darvishsefat AA, Danekar A. Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM). Cities. 2009;26(1): 19–26. doi: 10.1016/j.cities.2008.11.005

16. Dietzel C, Clarke KC. Replication of spatio-temporal land use patterns at three levels of aggregation by an urban cellular automata. 6th Int Conf Cell Autom Res Ind ACRI 2004. 2004; 523–532. doi: 10.1007/978-3-540-30479-1_54

17. Chen Y, Liu X, Li X. Calibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning. Int J Geogr Inf Sci. 2017;31(12): 2480–2504. doi: 10.1080/13658816.2017.1367004

18. Al-Darwish Y, Ayad H, Taha D, Saadallah D. Predicting the future urban growth and it’s impacts on the surrounding environment using urban simulation models: Case study of Ibb city–Yemen. Alexandria Eng J. 2018;57(4): 2887–2895. doi: 10.1016/j.aej.2017.10.009

19. Waddell P. UrbanSim: Modeling urban development for land use, transportation, and environmental planning. J Am Plan Assoc. 2002;68(3): 297–314. doi: 10.1080/01944360208976274

20. Deng Z, Zhang X, Li D, Pan G. Simulation of land use/land cover change and its effects on the hydrological characteristics of the upper reaches of the Hanjiang Basin. Environ Earth Sci. 2015;73(3): 1119–1132. doi: 10.1007/s12665-014-3465-5

21. Yang Q, Li X, Shi X. Cellular automata for simulating land use changes based on support vector machines. Comput Geosci. 2008;34(6): 592–602. doi: 10.1016/j.cageo.2007.08.003

22. Kamusoko C, Gamba J. Simulating urban growth using a Random Forest-Cellular Automata (RF-CA) model. ISPRS Int J Geo-Information. 2015;4(2): 447–470. doi: 10.3390/ijgi4020447

23. Liang X, Liu X, Li D, Zhao H, Chen G. Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model. Int J Geogr Inf Sci. 2018;32(11): 2294–2316. doi: 10.1080/13658816.2018.1502441

24. Clarke KC, Hoppen S, Gaydos L. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environ Plan B Plan Des. 1997;24(2): 247–261. doi: 10.1068/b240247

25. Silva EA, Clarke KC. Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Comput Environ Urban Syst. 2002;26(6): 525–552. doi: 10.1016/S0198-9715(01)00014-X

26. Dietzel C, Clarke KC. Toward optimal calibration of the SLEUTH land use change model. Trans GIS. 2007;11(1): 29–45. doi: 10.1111/j.1467-9671.2007.01031.x

27. Jantz CA, Goetz SJ, Donato D, Claggett P. Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Comput Environ Urban Syst. 2010;34(1): 1–16. doi: 10.1016/j.compenvurbsys.2009.08.003

28. Li F, Wang L, Chen Z, Clarke KC, Li M, Jiang P. Extending the SLEUTH model to integrate habitat quality into urban growth simulation. J Environ Manage. 2018;217: 486–498. doi: 10.1016/j.jenvman.2018.03.109 29631238

29. Serasinghe Pathiranage IS, Kantakumar LN, Sundaramoorthy S. Remote sensing data and SLEUTH urban growth model: As decision support tools for urban planning. Chinese Geogr Sci. 2018;28(2): 274–286. doi: 10.1007/s11769-018-0946-6

30. Bihamta N, Soffianian A, Fakheran S, Gholamalifard M. Using the SLEUTH urban growth model to simulate future urban expansion of the Isfahan metropolitan area, Iran. J Indian Soc Remote Sens. 2015;43(2): 407–414. doi: 10.1007/s12524-014-0402-8

31. Sakieh Y, Salmanmahiny A, Jafarnezhad J, Mehri A, Kamyab H, Galdavi S. Evaluating the strategy of decentralized urban land-use planning in a developing region. Land use policy. 2015;48: 534–551. doi: 10.1016/j.landusepol.2015.07.004

32. Han H, Hwang YS, Ha SR, Kim BS. Modeling future land use scenarios in South Korea: Applying the IPCC special report on emissions scenarios and the SLEUTH model on a local scale. Environ Manage. 2015;55(5): 1064–1079. doi: 10.1007/s00267-015-0446-8 25588808

33. Butsch C, Kumar S, Wagner P, Kroll M, Kantakumar L, Bharucha E, et al. Growing ‘smart’? Urbanization processes in the Pune Urban Agglomeration. Sustain. 2017;9(12): 2335. doi: 10.3390/su9122335

34. Yan Y, Zhou R, Ye X, Zhang H, Wang X. Suitability evaluation of urban construction land based on an approach of vertical-horizontal processes. ISPRS Int J Geo-Information. 2018;7(5): 198. doi: 10.3390/ijgi7050198

35. Chen M, Gong Y, Lu D, Ye C. Build a people-oriented urbanization: China’s new-type urbanization dream and Anhui model. Land use policy. 2019;80: 1–9. doi: 10.1016/j.landusepol.2018.09.031

36. Chen M, Liu W, Lu D. Challenges and the way forward in China’s new-type urbanization. Land use policy. 2016;55: 334–339. doi: 10.1016/j.landusepol.2015.07.025

37. Huang C, Davis LS, Townshend JRG. An assessment of support vector machines for land cover classification. Int J Remote Sens. 2002;23(4): 725–749. doi: 10.1080/01431160110040323

38. Poursanidis D, Chrysoulakis N, Mitraka Z. Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping. Int J Appl Earth Obs Geoinf. 2015;35: 259–269. doi: 10.1016/j.jag.2014.09.010

39. Zhou X, Li L, Chen L, Liu Y, Cui Y, Zhang Y, et al. Discriminating urban forest types from Sentinel-2A image data through linear spectral mixture analysis: A case study of Xuzhou, East China. Forests. 2019;10(6): 478. doi: 10.3390/f10060478

40. Li L, Bakelants L, Solana C, Canters F, Kervyn M. Dating lava flows of tropical volcanoes by means of spatial modeling of vegetation recovery. Earth Surf Process Landforms. 2018;43(4): 840–856. doi: 10.1002/esp.4284

41. van der Linden S, Rabe A, Held M, Jakimow B, Leitão P, Okujeni A, et al. The EnMAP-Box—A toolbox and application programming interface for EnMAP data processing. Remote Sens. 2015;7(9): 11249–11266. doi: 10.3390/rs70911249

42. Cui Y, Li L, Chen L, Zhang Y, Cheng L, Zhou X, et al. Land-use carbon emissions estimation for the Yangtze River Delta Urban Agglomeration using 1994–2016 Landsat image data. Remote Sens. 2018;10(9): 1334. doi: 10.3390/rs10091334

43. Li L, Solana C, Canters F, Kervyn M. Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image. J Volcanol Geotherm Res. 2017;345: 109–124. doi: 10.1016/j.jvolgeores.2017.07.014

44. Kuo HF, Tsou KW. Modeling and simulation of the future impacts of urban land use change on the natural environment by SLEUTH and cluster analysis. Sustain. 2017;10(2): 72. doi: 10.3390/su10010072

45. Yang X, Lo CP. Modelling urban growth and landscape changes in the Atlanta Metropolitan Area. Int J Geogr Inf Sci. 2003;17(5): 463–488. doi: 10.1080/1365881031000086965

46. Nigussie TA, Altunkaynak A. Modeling urbanization of Istanbul under different scenarios using SLEUTH urban growth model. J Urban Plan Dev. 2017;143(2): 04016037. doi: 10.1061/(ASCE)UP.1943-5444.0000369

47. Wu X, Hu Y, He HS, Bu R, Onsted J, Xi F. Performance evaluation of the SLEUTH model in the Shenyang Metropolitan Area of Northeastern China. Environ Model Assess. 2009;14(2): 221–230. doi: 10.1007/s10666-008-9154-6

48. Kim J, Park S. Simulating the impacts of the greenbelt policy reform on sustainable urban growth: The case of Busan Metropolitan Area. J Korean Soc Surv Geod Photogramm Cartogr. 2015;33(3): 193–202. doi: 10.7848/ksgpc.2015.33.3.193

49. Ministry of Land and Resources of the PRC. Guideline for the county-level general land use planning (TD/T 1024–2010). Beijing: Standards Press of China; 2010. (in Chinese)

50. Tong H, Shi P, Bao S, Zhang X, Nie X. Optimization of urban land development spatial allocation based on ecology-economy comparative advantage perspective. J Urban Plan Dev. 2018;144(2): 05018006. doi: 10.1061/(ASCE)UP.1943-5444.0000444

51. Ying C, Ling H, Kai H. Change and optimization of landscape patterns in a basin based on remote sensing images: A case study in China. Polish J Environ Stud. 2017;26(5): 2343–2353. doi: 10.15244/pjoes/70007

52. Knaapen JP, Scheffer M, Harms B. Estimating habitat isolation in landscape planning. Landsc Urban Plan. 1992;23(1): 1–16. doi: 10.1016/0169-2046(92)90060-D

53. Liu X, Shu J, Zhang L. Research on applying minimal cumulative resistance model in urban land ecological suitability assessment: as an example of Xiamen City. Acta Ecol Sin. 2010. 2010;30(2): 421–428. (in Chinese)

54. Yu K. Security patterns and surface model in landscape ecological planning. Landsc Urban Plan. 1996;36(1): 1–17. doi: 10.1016/S0169-2046(96)00331-3

55. Gao Y, Ma L, Liu J, Zhuang Z, Huang Q, Li M. Constructing ecological networks based on habitat quality assessment: A case study of Changzhou, China. Sci Rep. Nature Publishing Group; 2017;7: 1–11. doi: 10.1038/srep46073 28393879

56. Liu G, Liang Y, Cheng Y, Wang H, Yi L. Security patterns and resistance surface model in urban development: Case study of Sanshui, China. J Urban Plan Dev. 2017;143(4): 05017011. doi: 10.1061/(ASCE)UP.1943-5444.0000402

57. Li F, Ye Y, Song B, Wang R. Evaluation of urban suitable ecological land based on the minimum cumulative resistance model: A case study from Changzhou, China. Ecol Modell. 2015;318: 194–203. doi: 10.1016/j.ecolmodel.2014.09.002

58. Yu Q, Yue D, Wang J, Zhang Q, Li Y, Yu Y, et al. The optimization of urban ecological infrastructure network based on the changes of county landscape patterns: a typical case study of ecological fragile zone located at Deng Kou (Inner Mongolia). J Clean Prod. 2017;163: S54–S67. doi: 10.1016/j.jclepro.2016.05.014

59. Ye Y, Su Y, Zhang H ou, Liu K, Wu Q. Construction of an ecological resistance surface model and its application in urban expansion simulations. J Geogr Sci. 2015;25(2): 211–224. doi: 10.1007/s11442-015-1163-1

60. Sakieh Y, Salmanmahiny A. Treating a cancerous landscape: Implications from medical sciences for urban and landscape planning in a developing region. Habitat Int. 2016;55: 180–191. doi: 10.1016/j.habitatint.2016.03.008

61. Dietzel C, Clarke KC. Spatial differences in multi-resolution urban automata modeling. Trans GIS. 2004;8(4): 479–492. doi: 10.1111/j.1467-9671.2004.00197.x

62. Mitsova D, Shuster W, Wang X. A cellular automata model of land cover change to integrate urban growth with open space conservation. Landsc Urban Plan. 2011;99(2): 141–153. doi: 10.1016/j.landurbplan.2010.10.001

63. Onsted J, Clarke KC. The inclusion of differentially assessed lands in urban growth model calibration: A comparison of two approaches using SLEUTH. Int J Geogr Inf Sci. 2012;26(5): 881–898. doi: 10.1080/13658816.2011.617305

64. Akın A, Clarke KC, Berberoglu S. The impact of historical exclusion on the calibration of the SLEUTH urban growth model. Int J Appl Earth Obs Geoinf. 2014;27: 156–168. doi: 10.1016/j.jag.2013.10.002

65. Shi Y, Wu J, Shi S. Study of the simulated expansion boundary of the simulated expansion boundary of construction land in Shanghai based on a SLEUTH model. Sustain. 2017;9(6):876. doi: 10.3390/su9060876

66. Li G, Sun S, Fang C. The varying driving forces of urban expansion in China: Insights from a spatial-temporal analysis. Landsc Urban Plan. 2018;174: 63–77. doi: 10.1016/j.landurbplan.2018.03.004

67. Deng X, Huang J, Rozelle S, Uchida E. Growth, population and industrialization, and urban land expansion of China. J Urban Econ. 2008;63(1): 96–115. doi: 10.1016/j.jue.2006.12.006

Článek vyšel v časopise


2019 Číslo 11