An integrated approach to modeling urban growth using modified built-up area extraction technique


Prediction of urban growth is often crucial in urban planning decisions. In this paper, we integrated the whole process of urban growth prediction by SLEUTH simulation for Dhaka Metropolitan Development Plan area of Bangladesh for the year of 2035. SLEUTH requires a rigorous preparation of five data inputs, i.e., slope, exclusion area, urban extent, road network and hillshade. This paper improvised the preparation of urban extent input using both Landsat-8 and Landsat-5 imagery. To increase the accuracy of urban area extraction from Landsat-8 images, we integrated normalized difference vegetation index and modified normalized difference water index with normalized difference built-up index. In the case of normalized difference built-up index, we used the principal component image of band 6 and 7 of Landsat-8 to include the effects of both bands. This technique to extract the built-up area increased the overall accuracy by 17.28% point. SLEUTH model ran through three calibration phases—coarse, fine and final—and an additional calibration was run to generate the forecasting coefficients. After the calibration phase, the best fit coefficient values were determined to run the prediction mode. The predicted outputs were derived as percentiles of development probability, from which a probability of above 90% was selected in this study. The prediction reveals that the urban extent of the study area is likely to increase by 158.66% from 2015 to 2035, and the designated conservation areas will significantly decrease during the same time period. This paper will provide researchers with an accurate and structured methodology to predict urban growth.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. Acevedo W, Masuoka P (1997) Time-series animation techniques for visualizing urban growth. Comput Geosci 23:423–435

  2. Aerts JCJH, Clarke KC, Keuper AD (2003) Testing popular visualization techniques for representing model uncertainty. Cartogr Geograph Inf Sci 30:249–261

  3. Allen J, Lu K (2003) Modeling and prediction of future urban growth in the Charleston region of South Carolina: a GIS-Based integrated approach. Conserv Ecol 8(2):2

  4. Arthur ST (2001) A satellite based scheme for predicting the effects of land cover change on local microclimate and surface hydrology. Ph.D. Dissertation, Department of Meteorology, Pennsylvania State University

  5. Atlas of Urban Expansion (2016) “Dhaka”. Accessed 25 Oct 2016

  6. Bangladesh Institute of Planners (2008) Detail Area Plan (DAP) of Dhaka: Shattering the vision of DMDP. The Daily Star. Retrieved June 7, 2012, from

  7. Bhatti SS, Tripathi NK (2014) Built-up area extraction using Landsat 8 OLI imagery. GIScience Remote Sens 51(4):445–467

  8. Bierwagen B (2003) The effects of land use change on butterfly dispersal and community ecology. Ph.D. Dissertation, Bren School of Environmental Management and Science, University of California—Santa Barbara

  9. Bihamta N, Soffianian A, Fakheran S, Gholamalifard M (2015) Using the SLEUTH urban growth model to simulate future urban expansion of the Isfahan metropolitan area. Iran. J Indian Soc Remote Sens 43(2):407–414

  10. Candau J (2000) Calibrating a cellular automaton model of urban growth in a timely manner. In: Proceedings of the 4th international conference on integrating geographic information systems and environmental modeling: problems, prospects, and needs for research. University of Colorado Boulder, pp 2–8

  11. Candau J (2002) Temporal calibration sensitivity of the Sleuth Urban Growth Model. University of California, Santa Barbara, Santa Barbara

  12. Candau J, Clarke KC (2000) Probabilistic land cover modeling using deltatrons. In: Proceedings of URISA 2000 conference. Urban and Regional Information System Association, Orlando

  13. Candau J, Rasmussen S, Clarke KC (2000) A coupled cellular automaton model for land use/land cover dynamics. In: Proceedings of the 4th international conference on integrating GIS and environmental modeling (GIS/EM4). Banff, Alberta, Canada. Accessed 6 May 2015

  14. Claggett P, Jantz CA, Goetz SJ, Bisland C (2004) Assessing development pressure in the Chesapeake Bay watershed: an evaluation of two land-use change models. Environ Monit Assess 94:129–146

  15. Clarke KC (2005) The limits of simplicity: toward geocomputational honesty in urban modeling. In: Atkinson P, Foody G, Darby S, Wu F (eds) International geo dynamics. CRC Press, Boca Raton, pp 215–232

  16. Clarke KC (2008) Mapping and modelling land use change: an application of the SLEUTH model. In: Pettit C (ed) Landscape analysis and visualisation: spatial models for natural resource management and planning. Springer, Berlin, pp 353–366

  17. Clarke KC, Gaydos L (1998) Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inf Sci 12(7):699–714

  18. Clarke KC, Hoppen S, Gaydos L (1996) Methods and techniques for rigorous calibartion of a cellular automaton model of urban growth. University of Santa Barbara. Accessed 19 Dec 2015

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

  20. Cogan CB, Davis FW, Clarke KC (2001) Application of urban growth models and wildlife habitat models to assess biodiversity losses. University of California-Santa Barbara Institute for Computational Earth System Science. U.S. department of the Interior, US geological Survey, Biological Resources Division, Gap Analysis Program, Santa Barbara, CA

  21. Deng C, Wu C (2013) A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution. Remote Sens Environ 133:62–70

  22. Dietzel C, Clarke KC (2004a) Replication of spatio-temporal land use patterns at three levels of aggregation by an urban cellular automata. Lecture notes in computer science, 3304. Springer, Berlin, pp 523–532

  23. Dietzel C, Clarke KC (2004b) Spatial differences in multi-resolution urban automata modeling. Trans GIS 8:479–492

  24. Dietzel C, Clarke KC (2006) The effect of disaggregating land use categories in cellular automata during model calibration and forecasting. Comput Environ Urban Syst 30(1):78–101

  25. Dietzel C, Clarke KC (2007) Toward optimal calibration of the SLEUTH land use change model. Trans GIS 11(1):29–45

  26. Dietzel C, Herold M, Hemphill JJ, Clarke KC (2005a) Spatio-temporal dynamics in California’s Central Valley: empirical links to urban theory. Int J Geogr Inf Sci 19(2):175–195

  27. Dietzel C, Oguz H, Hemphill JJ, Clarke KC, Gazulis N (2005b) Diffusion and coalescence of the Houston Metropolitan Area: evidence supporting a new urban theory. Environ Plan 32(2):231–246

  28. Firl GJ, Carter L (2011) Lesson 10: calculating vegetation indices from Landsat 5 TM and Landsat 7 ETM+ Data. Tutorial conducted from Colorado State University, Fort Collins, Colorado, United States. Retrieved February 19, 2012, from

  29. Gazulis N, Clarke KC (2006) Exploring the DNA of our regions: classification of outputs from the SLEUTH model. In: El Yacoubi S, Chapard B, Bandini S (eds) Cellular automata. 7th international conference on cellular automata for research and industry, ACRI 2006. Perpignan, France, September 2006, Proceedings. Lecture notes in computer science. No. 4173. Springer, New York

  30. Goldstein NC (2004a) Brains vs. brawn—comparative strategies for the calibration of a cellular automata–based urban growth model. In: Atkinson P, Foody G, Darby S, Wu F (eds) International GeoDynamics. CRC Press, Boca Raton

  31. Goldstein NC (2004b) A methodology for tapping into the spatiotemporal diversity of information in simulation models of spatial spread. In: Proceedings of the third international conference on geographic information science, College Park, MD

  32. Goldstein NC, Candau J, Moritz M (2000) Burning Santa Barbara at both ends: a study of fire history and urban growth predictions. In: Proceedings of the 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4). Banff, Alberta, Canada. Accessed 6 May 2015

  33. GPAD. Geo Planning for Advanced Development 2015. Accessed 11 Sept 2015

  34. Greensboro (2003) Greensboro Connections 2025 Comprehensive Plan. Planning Department, City Council, City of Greensboro, North Carolina

  35. He C, Shi P, Xie D, Zhao Y (2010) Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sens Lett 1(4):213–221.

  36. Herold M, Goldstein NC, Menz G, Clarke KC (2002) Remote sensing based analysis of urban dynamics in the Santa Barbara region using the SLEUTH urban growth model and spatial metrics. In: Proceedings of the 3rd symposium on remote sensing of urban areas, Istanbul, Turkey

  37. Herold M, Goldstein NC, Clarke KC (2003) The spatio-temporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 86:286–302

  38. Hung M (2002) Urban Land Cover Analysis from Satellite Images. Accessed 22 Sept 2015

  39. IRIN (2012) Bangladesh: Dhaka’s shrinking wetlands raise disaster risks. Humanitarian News and Analysis. Retrieved June 21, 2012, from IRIN News. Accessed 6 May 2015

  40. Jantz CA, Goetz SJ, Shelley MK (2003) Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore/Washington metropolitan area. Environ Plan B 31:251–271

  41. Jantz CA, Goetz SJ, Donato D, Claggett P (2010) Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Comput Environ Urban Syst 34(2010):1–16

  42. Jensen JR (2000) Remote sensing of the environment: an earth resource perspective. Prentice Hall, Upper Saddle River, p 544

  43. Jolliffe IT (2002) Graphical representation of data using principal components. In: Jolliffe IT (ed) Principal component analysis. Springer, New York, pp 78–110

  44. Landis JD (1994) The California urban futures model: a new generation of metropolitan simulation models. Environ Plan 21:399–420

  45. Leão S, Bishop I, Evans D (2001) Assessing the demand of solid waste disposal in urban region by urban dynamics modelling in a GIS environment. Resour Conserv Recycl 33:289–313

  46. Leão S, Bishop I, Evans D (2004) Spatial-temporal model for demand allocation of waste landfills in growing urban regions. Comput Environ Urban Syst 28:353–385

  47. Li X, Yeh A (1998) Modeling sustainable urban development by the integration of constrained cellular automata and GIS. Int J Geogr Inf Sci 14(2):131–152

  48. Mahmud MA (2007) Corruption in plan permission process in RAJUK: a study of violations and proposals. Retrieved January 23, 2013, from Transparency International Bangladesh

  49. Onsted JA (2002) SCOPE: a modification and application of the Forrester Model to the South Coast of Santa Barbara County. Master’s Thesis, Department of Geography, University of California—Santa Barbara

  50. Onsted JA (2007) Effectiveness of the Williamson act: a spatial analysis. Ph.D. Dissertation, Department of Geography, University of California, Santa Barbara

  51. Pramanik MMA, Stathakis D (2016) Forecasting urban sprawl in Dhaka city of Bangladesh. Environ Plan 43(4):756–771

  52. Priyodesk (2011) Move to protect arable land now in cold storage. Retrieved November 9, 2012, from Priyo News

  53. Rafiee R, Mahiny AS, Khorasani N, Darvishsefat AA, Danekar A (2009) Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM). Cities 26:19–26

  54. RAJUK, Ministry of Housing and Public Works (2010) Preparation of Detailed Area Plan for Dhaka Metropolitan Development Planning (DMDP) Area: Group A-E, Rajdhani Unnayan Kartripakkha. RAJUK, Dhaka

  55. Richards JA (2013) Remote sensing digital image analysis: an introduction, 5th edn. Springer, New York

  56. Rossiter DG (2004) Technical note: statistical methods for accuracy assessment of classified thematic maps. Retrieved January 9, 2013, from

  57. Shubho MTH, Islam SR, Ayon BD, Islam I (2015) An improved semiautomatic segmentation approach to land cover mapping for identification of land cover change and trend. Int J Environ Sci Technol 12(8):2593–2602

  58. Siddiqui MZ, Everett JW, Vieux BE (1996) Landfill siting using geographic information systems: a demonstration. J Urban Plan Dev 122:515–523

  59. Silva EA (2004) The DNA of our regions: artificial intelligence in regional planning. Futures 36(10):1077–1094

  60. Silva EA, Clarke KC (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Comput Environ Urban Syst 26:525–552

  61. Silva EA, Clarke KC (2005) Complexity, emergence and cellular urban models: lessons learned from applying SLEUTH to two Portuguese Cities. Eur Plan Stud 13(1):93–115

  62. Solecki WD, Oliveri C (2004) Downscaling climate change scenarios in an urban land use change model. J Environ Manag 72:105–115

  63. Syphard AD, Clarke KC, Franklin J (2007) Simulating fire frequency and urban growth in southern California coastal shrublands, USA. Landsc Ecol 22(3):431–445

  64. Tetiz MB, Dietzel C, Fulton W (2005) Urban development futures in the San Joaquin Valley. Public Policy Institute of California, San Francisco

  65. Theobald D (2001) Land-use dynamics beyond the urban fringe. Geogr Rev 91:544–564

  66. USGS. U. S. Geological Survey 2015. Accessed 3 Aug 2015

  67. Wardad Y (2012) Enactment of agriculture land protection act underscored. The Financial Express. Retrieved June 7, 2012, from

  68. Xi F, He HS, Hu Y, Bu R, Chang Y, Wu X et al (2009) Simulating the impacts of ecological protection policies on urban land use sustainability in Shenyang Fushun, China. Int J Urban Sustain Dev 1(1–2):111–127

  69. Xi F, He HS, Clarke KC, Hu Y, Wu X, Liu M et al (2012) The potential impacts of sprawl on farmland in Northeast Chinaea new strategy for rural development. Landsc Urban Plan 104(1):34–46

  70. Xiang WN, Clarke KC (2003) The use of scenarios in land use planning. Environ Plan B 30:885–909

  71. Xu H (2007) Extraction of urban built-up land features from landsat imagery using a thematic-oriented index combination technique. Photogramm Eng Remote Sens 72(12):1381–1391

  72. Yang X, Lo CP (2003) Modelling urban growth and landscape change in the Atlanta metropolitan area. Int J Geogr Inf Sci 17:463–488

  73. Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 24(3):583–594

Download references


We are grateful to the Department of Urban and Regional Planning of the Bangladesh University of Engineering and Technology for providing with the necessary logistic supports to carry out this research. We are thankful to the United State Geological Survey (USGS) website for their open access data. We also acknowledge the use of the open access data provided by the Geo-Planning for Advanced Development (GPAD).

Author information

Correspondence to Md. T. Hossain Shubho.

Additional information

Editorial responsibility: M. Abbaspour.


Appendix 1: Accuracy assessment of land cover mapping

Total 150 samples are taken (50 for each land cover, i.e., built-up area, vegetation, and wetland) to assess the accuracy of land cover mapping through error matrix.

See Tables 4, 5 and 6.

Table 4 Error matrix of land cover mapping following the method proposed by Bhatti and Tripathi (2014)
Table 5 Error matrix of land cover mapping following modified method (excluding band 10, and 11)
Table 6 Accuracy assessment of land cover mapping before and after following the modified method

Appendix 2: Selection of coefficient for different calibrations

See Tables 7, 8 and 9.

Table 7 Coefficient selection from coarse calibration control_stats.log file
Table 8 Coefficient selection from fine calibration control_stats.log file
Table 9 Coefficient selection from final calibration control_stats.log file

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hossain Shubho, M.T., Islam, I. An integrated approach to modeling urban growth using modified built-up area extraction technique. Int. J. Environ. Sci. Technol. (2020).

Download citation


  • Urban growth prediction
  • Built-up area mapping
  • Landsat-8
  • GIS and remote sensing
  • Dhaka