Effects of urban sprawl on land use change in the peripheral villages of Tehran metropolis (case study: Tehran-Damavand axis)

Document Type : Original Article


1 Department of Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran

2 Center for Remote Sensing and GIS Studies, Shahid Beheshti University, Tehran, Iran


One of the major implications of accelerated urbanization is the spatial expansion of urban sprawl and the corrosive of villages and peripheral lands that have been numerous in metropolitan areas. The irregular sprawl and extension of the Tehran metropolis into surrounding areas have led to disturbances and imbalances in the social, economic, and spatial organization of peripheral villages. In recent decades, urban growth analysis has started from a variety of perspectives. Over the past half century this phenomenon has been prominent in Iran. It originally took place in metropolises and large cities, but gradually moved to middle cities due to the centralized policies of the settlement .The study area has been expanding rapidly in the last three decades and has caused many environmental problems and rapid changes in the economic performance of villages and the transformation of valuable natural resources. Therefore, this research intends to investigate the manner and extent of land use changes in the study area by analyzing and accurately analyzing the phenomenon of creep and reducing the adverse effects by providing scientific solutions. Therefore, this research is intended by look up and accurate analysis of the sprawl phenomenon, study the method and extent of land use change in the study area and reduces its adverse effects by providing scientific solutions.
Material and methods:
For accurate analysis of the effects of sprawl phenomena, descriptive and analytical methods have been used. In this method, after collecting data contains Land sat satellite images with TM, ETM and OLI sensors and after visual interpretation of satellite images due to the absence of stroke errors, cloud spots by using remote sensing techniques and spatial information systems, the land use change process began in 1986, 2002, 2018, and divided into four residential and non-residential construction, vegetation, rangelands and roads. After that, the supervised classification operation was monitored by the SVM algorithm and the detection and determination of the sprawl pattern in the study area.
Results and discussion:
The calculations indicate that in the region of Tehran -Damavand, due to the crawling growth in discrete form and in some points continuous, the most changes in terms of increase is related to the use of residential construction 9.69% and the use of the road 1%, that this growing trend has reduced the use of pasture and vegetation by about 9.07% and 0.1%, respectively. After field operation and harvesting of samples with two-frequency GPS receivers and introducing it to the software, the classification of complications was performed by support vector machines with a mean total accuracy of 62.69% and a mean Kappa coefficient of 85.33%. Most changes were related to residential and non-residential classes and roads and in the study area, most vegetation coverings and agricultural land became industrial estates and recreational villas. This led to an increase the migration from villages to Tehran's metropolis, followed by the need for urban landscapes and finally fragility and instability of environmental resources. In Tehran- Damavand axis, these changes have been made by various factors and forces during its uneven spatial expansion.
In the study of spatial and land use changes, it is important to pay attention to which side effects are slowly changing and which side effects change more quickly. In this research, it was revealed that the study of vegetation compared to other lands had the greatest change. Therefore, if there is no precise planning and policies and continuous monitoring to prevent this trend, there will be harmful and irreparable environmental impacts.


  1. Afrakhteh, H. and Hoji Poor, M., 2013. Urban sprawl and the consequence on sustainable rural development (Case study: periphery villages in Brigand city). International Quarterly Geography Institution. 39(11), 158-185.
  2. Audrey n.clark, 1985, Longman Dictionary of Geography; human and physical, Longman;
  3. Ambatwati, L., Verhaeghe R, J., Pal, A. and Van Arem, B., 2014. Controlling urban sprawl with integrated approach of space-transport development strategies. Procedia - Social and Behavioral Sciences. 138, 679-694.
  4. A. akram , A.safarian,SH.hoje ,1999 , Estimation and zoning of soil erosion using the methods of the modified global equation of soil erosion and AHP
  5. Arsanjani, Jamal Jokar. Wolfgang Kainz and Ali Jafar Mousivand. 2011. Tracking dynamic land use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran”, International, Journal of Image and Data Fusion, 2: 4, United Kingdom.
  6. Azizi and Arasteh , 2012:6 Urban Sprawl based on construction density index, city identity
  7. Barry, K. and Lee, D., 2013. Measuring sprawl across the Urban Rural continuum using an amalgamated sprawl index. Sustainability. 5(5), 1806-1828.
  8. Bhatta, B. 2010. Analysis of Urban Growth and Sprawl from Remote Sensing Data. Springer, Heidelberg, 172.
  9. B. C. Pijanowski, D. G. Brown, G. Manik, Using neural nets and GIS to forecast land use changes: a land transformation model, Computers, Environment and Urban Systems 26 (6) (2002) 553–575.
  10. Beesly Kenneth, B., 2010. Book rid Rural Development institute the rural- urban Fringe, in Canada, conflict and controversy.
  11. Robert Burchell , Catherine Galley , 2003 .Projecting Incidence and Costs of Sprawl in the United States
  12. Courage Kamusoko, Masamu Aniya, Bongo Adi, et al., Rural sustainability under threat in Zimbabwe-simulation of future land use/cover changes inthe Bindura district based on the Markov-cellular automata model, Applied Geography 29 (2009) 435–447.
  13. Clark Labs. IDRISI Geographic Information Systems and Remote Sensing Software; Clark Labs: Worcester, MA, USA, 2006.
  14. Davis, C., & Schaub, T. 2005. A Tran’s boundary study of urban sprawl in the Pacific Coast region of North America: The benefits of multiple measurement methods. International Journal of Applied Earth Observation and Geo information, 7(4), 268-283.
  15. Deep, S. and Saklani, 2014. Urban sprawl modeling using cellular automata. The Egyptian Journal of Remote Sensing and Space Science. 17(2), 179-187.
  16. Ebrahimi, A. Taleb, J., 2013. Software training in Arc GIS 10.1, Diagram bookmaker. [In Persian with English abstract].
  17. Ed. Richard T. Wright, Dorothy Boorse , 2013 .Environmental Science: Toward Sustainable Future
  18. Eglin, R., 2010. Land Prioritization. The journal for development and governance issues Transformer. Anew Village Region. 16(2), 3-11.
  19. EEA, 2006a, Land accounts for Europe 1990–2000 — Towards integrated land and ecosystem accounting, EEA Report No 11/2006, European Environment Agency.
  20. European Environment Agency. 2006. Urban sprawl in Europe: The ignored challenge. EEA Report.ISBN. Luxembourg, Office for Official Publications of the European Communities.
  21. European environment's Agency, 2014. Trends and prospects, in a global contextt. http://www.eea.europa.eu/soer#tab-global-megatrends.
  22. Ewing RH, 1994. Characteristics, Causes, and Effects of Sprawl: A Literature Review. Environmental and Urban Issues. 21(2): 1-15
  23. Ewing RH, 1997. Is Los Angeles style sprawl desirable? 107-108
  24. Ewing, R, Hamidi, SH, Preuss, L, and Dodds, A., 2014. Measuring Sprawl and Its Impacts an Update. Journal of Planning Education and Research. 35(1), 35-50.
  25. Fulton, W., Pendall R., Nguyen, M., Harrison, A., 2001. “Who Sprawls Most? How Growth Patterns Differ Across the U.S.” The Brookings Institution Survey Series, July, p. 1-23.
  26. Fengming, x., Hong, S., Keith, C., Yuanman Hu., Xiaoqing, Wu, Miao L., Tiemao, S., Yong, G, Chang, G., 2012. The potential impacts of sprawl on farmland in Northeast China—evaluating a new strategy for rural development. Landscape and Urban Planning. 104, 34-46.
  27. Garcia-Lopez M. Sole-Olle, A. and Viladecans-Marsal, E., 2015. Does zoning follow highways? Regional Science and Urban Economics. 53, 148-155.
  28. General Population and Housing Census,.1986 a. 1991 b, 1996 c, 2006 d, 2011 e, 2015 f.. Iranian Statistics Agency. Www. Amar.org.
  29. Gross, JE, Goetz, SJ, Cihlar, J., 2009, Application of remote sensing to parks and protected area monitoring: Introduction to the special issue, Remote Sensing of Environment, 113, 7, 1343-1345.
  30. Garcia, A M Sante, I, Miranda, D, Crecente,R(2009), Analysis of Factors Influencing Urban Growth Patterns on Small Towns, Proceedings of the 2nd WSEAS International Conference on URBAN PLANNING and TRANSPORTATION
  31. Hu, Zhiyong, and Lo, C. P. 2007. Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31: 6. United Kingdom.
  32. Hartter, J.; Southworth, J. Dwindling resources and fragmentation of landscapes around parks: Wetlands and forest patches around Kibale National Park, Uganda. Landsc. Ecol. 2009, 24, 643–656.
  33. H.S. Sudhiraa, T.V. Ramachandraa, K.S.Jagadishb,2003.Urban sprawl: metrics, dynamics and modelling using GIS
  34. Iqbal Sarwar Md. Billa M. Paul, Alak. , (2016). Urban land use change analysis using RS and GIS in Sulakbahar ward in Chittagong city, Bangladesh. Internatinal Journal of Geomatics and geosciences. 1: 7, Pp 1-10.
  35. Jaeger, J.A.G., 2002. Land schaftszerschneidung. Eine transdisziplina¨re Studie gema¨ß dem Konzept der Umweltgefa¨hrdung. Eugen Ulmer, Stuttgart.
  36. Jaeger, J. Rene, B. Christian, S, and Felix K., 2010. Suitability criteria for measures of urban sprawl. Ecological Indicators. 10, 397-40
  37. Jensen, J. R. 2015. Introductory digital image processing 4 rd edition, In Upper Saddle River: Prentice hall.
  38. Kuldeep, Tiwari. , and Kamlesh, Khanduri. , (2011). Land Use / Land cover change detectionin Doon valley (Dehradun Tehsil), Uttarakhand: using GIS& Remote Sensing Technique, International Journal of Geomatics and Geosciences, 2 (1): Pp 34-41.
  39. Kamila, A. and Pal, S. C., 2015. Urban Growth Monitoring and Analysis of Environmental Impacts on Bandura-I and II Block using Landsat Data. International Journal of Advanced Remote Sensing and GIS. 4, 965-975.
  40. Krieger, D., 1999. Saving open spaces: Public support for farmland protection (Working Paper Series wp99-1). Chicago: Center for Agriculture in the Environment.
  41. Lawrence, K., 2012. Urban Sprawl to Triple by 2030, science daily. 2(4), 384-423.
  42. Li, HaiFeng. Inohae Takuro and Su Weici and Nagaie Tadashi and Hokao Kazunori. 2011. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222: 20, Netherlands.
  43. Li, S., and Nadolniyak, D., 2013. Agricultural Land Development in Lee County Florida: Impacts of Economic and Natural Risk Factors in a Coastal Area, Southern Agricultural Economics Association. Annual Meeting.
  44. Laurent, B., 2002. Rural America's new problem: handling sprawl, Joplin M. elsewhere an influx of newcomers alters land scape, http://www.csmonitor.com/2002/1210/p03s01.
  45. Mas, Jean-François, Melanie, Kolb, Martin, Paegelow, Maria Teresa, Camacho lmedo, and Thoma, Houet, 2014, Inductive pattern-based land use/cover change models, A comparison of four software packages, Environmental Modelling & Software, 51, 94-111.
  46. Messina, J. P.; Walsh, S. J., 2. 5D Morphogenesis: Modeling landuse and landcover dynamics in the Ecuadorian Amazon. Plant Ecol. 2001, 156, 75–88.
  47. Meyer, W. B., B.L. Turner II, 1994, change in land use and land cover: a global perspective, Cambridge University Press, Cambridge;
  48. Morote and Hernandez, 2016. Urban sprawl and its effects on water demand: A case study of Alicante, Spain
  49. Ostrom, E, 1990, is governing the commons, Cambridge University Press, Cambridge;
  50. Parry M.L, 1990, climate change and world agriculture, EarthSacan, London;
  51. Schwick , 2012.Environmental Impact Assessment Review, Pages 165-169
  52. Seto, Karen C, Güneralp, Burak, Hutyra, Lucy R., 2012. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences. 109(40), 16083-16088.
  53. Sierra, 1998. 30 Most Sprawl-Threatened Cities, Ten Most Sprawl-Threatened Large Cities Number Two.
  54. Scott, A., 2002. Urban Planning and Intergroup Conflict: Confronting a Fractured Public Interest. Journal of the American Planning Association. 68(1), 22-42.
  55. Shafieisabet, N., 2008.Tehran metropolitan sprawl and unsustainable agriculture in the peripheral villages (1976-2003): case villages’ area in Robat Karim, Thesis in Shahid Beheshti University.
  56. Shafieisabet, N, and Saeedi, A., 2008. Role policies concentrative habitation in revolution agricultural function villages’ periphery in Tehran metropolitan. Case study: villages’ area in Robat Karim.Geographical Community Iran. 397-422. [In Persian with English abstract.
  57. Shafieisabet, N. Bozorgniya, F., 2013. Spatial effects Tehran metropolitan on the agriculture land use periphery villages. The first international conference of the ecology of land. [In Persian with English abstract.
  58. Shafieisabet, N. Harati Fard, S., 2011. Analysis of agricultural land - use changes villages in Rabat Karim with the use of satellite imagery and GIS, 11th Congress Geographer – Iran. P13. [In Persian with English abstract.
  59. Shafieisabet, N. Harati Fard, S., 2011. Analysis of agricultural land - use changes villages in Rabat Karim with the use of satellite imagery and GIS, 11th Congress Geographer – Iran. P13. [In Persian with English abstract.
  60. Shafieisabet, N., 2014. Sprawl metropolis Tehran and uncertainty periphery villages, Environment preparation magazine. 24, 145-162. [In Persian with English abstract.
  62. Taubenböck, Hannes. Thomas Esch and Andreas Felbier and Michael Wiesner and Achim Roth, and Stefan Dech. 2012. Monitoring urbanization in mega cities from space. Remote sensing of Environment, 117, Netherland.
  63. Verda Kocabas, Suzana Dragicevic, Assessing cellular automata model behaviour using a sensitivity analysis approach, Computers, Environment andUrban Systems 30 (2006) 921–953.
  64. Vaz, Eric. Nijkamp Peter and Painho Marco, and Caetano Mario. 2012. A multi-scenario forecast of urban change: a study on urban growth in the Algarve, Landscape and Urban Planning, 104: 20, Netherland.
  65. Verburg, P. H. ; de Nijs, T. C. M. ; Ritsema van Eck, J. ; Visser, H. ; de Jong, K. , A. Method to analyse neighbourhood characteristics of land use patterns. Comput. Environ. Urban Syst. 2004, 28, 667–690.
  66. Zeng, C.H, Liu, Y, Stein, A, Jiao, L., 2015. Characterization and spatial modeling of urban sprawl in the Wuhan Metropolitan Area, China, International Journal of Applied Earth Observation and Geoformation. 35, 10-24. ussc.html, The Christian Science Monitor- and taxes septic systems.