Analysis of the Trends in Land Use and Land Cover Changes in Tehran (1991–2021)

Document Type : Research

Authors

1 Researcher, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.

2 Associate prof., at Soil and Water research institute, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran.

3 Assistant prof., at Soil and Water research institute, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran.

Abstract

Land use and land cover (LULC) changes have always had profound impacts on environmental sustainability and natural resources, especially in a city like Tehran as one of the most densely populated metropolises in the Middle East. The urban dynamics in Tehran was investigated in this study using remote sensing data, including Landsat (TM, ETM+, and OLI) and Sentinel-2 images from 1991 to 2021 collected from Google Earth Engine and USGS platforms before being pre-processed using geometric, radiometric, and atmospheric corrections via the FLAASH algorithm. To identify the LULC patterns, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were calculated, followed by unsupervised classification using the K-means algorithm to delineate five primary land use classes: built-up areas, agricultural lands, orchards, barren lands, and water bodies. Subsequently, supervised classification methods, including Support Vector Machine (SVM), Minimum Distance (MD), and Maximum Likelihood Classifier (MLC), were evaluated to reveal the superior performance (an overall accuracy of 91% and a Kappa coefficient of 0.89) of SVM. Change detection using the post-classification comparison method revealed a 39.7% increase in built-up areas (from 410.36 to 573.51 km²) but decreases of 69.3% and 9% in agricultural and orchard lands, respectively. The expansion in built-up areas, primarily towards western and southern stretches of Tehran, has led to the conversion of fertile agricultural lands into urban and industrial uses, posing such risks as intensified urban heat islands (projected to increase by 3.43 °C by 2050), diminished food security, and water and soil resource degradation. These findings underscore the urgent need for sustainable urban policies, including protection of the remaining agricultural lands, promotion of vertical urban development, and integrated water resource management, to steer Tehran towards environmental sustainability and provide a model for other developing metropolises.

Keywords


  1. Abdi, A.M., 2020. Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing, 57(1), pp.1-20. https://doi.org/10.1080/15481603.2019.1650447
  2. Abebe, G., Getachew, D. and Ewunetu, A., 2022. Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia. SN Applied Sciences4(1), p.30.  

https://doi.org/10.1007/s42452-021-04915-8

  1. Akiner, M.E. and Ghasri, M., 2025. Integrating Remote Sensing and Machine Learning to Analyze Urban Growth and Its Environmental Effects: A 30-Year Assessment in Başakşehir, Turkey. Pure and Applied Geophysics, pp.1-25. https://doi.org/10.1007/s00024-025-03727-w
  2. Alizadeh-Choobari, O. and Najafi, M.S., 2018. Extreme weather events in Iran under a changing climate. Climate dynamics, 50(1), pp.249-260.

https://doi.org/10.1007/s00382-017-3602-4

  1. Amani, M., Ghorbanian, A., Ahmadi, S.A., Kakooei, M., Moghimi, A., Mirmazloumi, S.M., Moghaddam, S.H.A., Mahdavi, S., Ghahremanloo, M., Parsian, S. and Wu, Q., 2020. Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5326-5350. https://doi.org/10.1109/JSTARS.2020.3021052
  2. Amini, S., Saber, M., Rabiei-Dastjerdi, H. and Homayouni, S., 2022. Urban land use and land cover change analysis using random forest classification of landsat time series. Remote Sensing, 14(11), p.2654.
  3. Angel, S., Parent, J., Civco, D.L., Blei, A. and Potere, D., 2011. The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Progress in planning, 75(2), pp.53-107.

https://doi.org/10.1016/j.progress.2011.04.001

  1. Anthony, T., Shohan, A.A.A., Oludare, A., Alsulamy, S., Kafy, A.A. and Khedher, K.M., 2024. Spatial analysis of land cover changes for detecting environmental degradation and promoting sustainability. Kuwait Journal of Science51(2), p.100197. https://doi.org/10.1016/j.kjs.2024.100197
  2. Aziz, G., Minallah, N., Saeed, A., Frnda, J. and Khan, W., 2024. Remote sensing based forest cover classification using machine learning. Scientific reports, 14(1), p.69. https://doi.org/10.1038/s41598-023-50863-1
  3. Chang, D., Wang, Q., Xie, J., Yang, J. and Xu, W., 2022. Research on the extraction method of urban built-up areas with an improved night light index. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5.
  4. Chen, G., Li, X., Liu, X., Chen, Y., Liang, X., Leng, J., Xu, X., Liao, W., Qiu, Y.A., Wu, Q. and Huang, K., 2020. Global projections of future urban land expansion under shared socioeconomic pathways. Nature communications, 11(1), p.537. https://doi.org/10.1038/s41467-020-14386-x
  5. Cooley, T., Anderson, G.P., Felde, G.W., Hoke, M.L., Ratkowski, A.J., Chetwynd, J.H., Gardner, J.A., Adler-Golden, S.M., Matthew, M.W., Berk, A. and Bernstein, L.S., 2002. FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. In IEEE international geoscience and remote sensing symposium, 3, pp. 1414-1418. https://doi.org/ 10.1109/IGARSS.2002.1026134
  6. Dadras, M., Shafri, H.Z.M., Ahmad, N., Pradhan, B. and Safarpour, S., 2014, June. Six decades of urban growth using remote sensing and GIS in the city of Bandar Abbas, Iran. In IOP Conference Series: Earth and Environmental Science (Vol. 20, No. 1, p. 012007). IOP Publishing.

https://doi.org/ 10.1088/1755-1315/20/1/012007

  1. El Garouani, A., Mulla, D.J., El Garouani, S. and Knight, J., 2017. Analysis of urban growth and sprawl from remote sensing data: Case of Fez, Morocco. International Journal of Sustainable Built Environment, 6(1), pp.160-169.
  2. Ewing, R. and Hamidi, S., 2017. Costs of sprawl. Routledge.
  3. Farhoudi, R., Zanganeh, S., & Moucheshi, R. (2009). The situation of spatial distribution of population in Iranian urban system (1956-2006). Quarterly Geographical Research, 68, 55-68.
  4. Figliomeni, F.G., Guastaferro, F., Parente, C. and Vallario, A., 2023. A proposal for automatic coastline extraction from landsat 8 OLI images combining modified optimum index factor (MOIF) and k-means. Remote Sensing, 15(12), p.3181. https://doi.org/10.3390/rs15123181
  5. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, pp.18-27.

https://doi.org/10.1016/j.rse.2017.06.031

  1. Guastella, G. and Pareglio, S., 2014. Urban systems, urbanization dynamics and land use in Italy: Evidence from a spatial analysis. Current urban studies, 2(03), p.291.
  2. Hao, H. and Wang, Y., 2022. Disentangling relations between urban form and urban accessibility for resilience to extreme weather and climate events. Landscape and urban planning, 220, p.104352.

https://doi.org/10.1016/j.landurbplan.2022.104352

  1. Hosseiny, B., Abdi, A.M. and Jamali, S., 2022. Urban land use and land cover classification with interpretable machine learning–A case study using Sentinel-2 and auxiliary data. Remote Sensing Applications: Society and Environment28, p.100843. https://doi.org/10.1016/j.rsase.2022.100843
  2. Islami, F.A., Tarigan, S.D., Wahjunie, E.D. and Dasanto, B.D., 2022. Accuracy assessment of land use change analysis using Google Earth in Sadar Watershed Mojokerto Regency. In IOP Conference Series: Earth and Environmental Science (Vol. 950, No. 1, p. 012091). IOP Publishing.
  3. Karimi, F. and Sultana, S., 2024. Urban expansion prediction and land use/land cover change modeling for sustainable urban development. Sustainability, 16(6), p.2285. https://doi.org/10.3390/su16062285
  4. Kharazmi, R., MohammadEsmail, Z., Chatrenour, M. 2025. Using Sentinel radar and optical data in the Google Earth Engine platform to determine the extent of land use changes in Alborz Province, Land Management Journal, 12(2), pp. 89-102. doi: 10.22092/lmj.2025.367197.368
  5. Kharazmi, R., Rahdari, M.R., Rodríguez-Seijo, A. and Elhag, M., 2023. Long-term time series analysis of land cover changes in an arid environment using Landsat data :( a case study of Hamoun Biosphere Reserve, Iran). Desert28(1), pp.123-144. https://doi.org/ 22059/jdesert.2023.93547
  6. Kharazmi, R., Tavili, A., Rahdari, M.R., Chaban, L., Panidi, E. and Rodrigo-Comino, J., 2018. Monitoring and assessment of seasonal land cover changes using remote sensing: A 30-year (1987–2016) case study of Hamoun Wetland, Iran. Environmental monitoring and assessment, 190(6), p.356.

https://doi.org/10.1007/s10661-018-6726-z

  1. Khoshnoodmotlagh, S., Daneshi, A., Gharari, S., Verrelst, J., Mirzaei, M. and Omrani, H., 2021. Urban morphology detection and it's linking with land surface temperature: A case study for Tehran Metropolis, Iran. Sustainable cities and society, 74, p.103228. https://doi.org/10.1016/j.scs.2021.103228
  2. Lambin, E.F., Geist, H.J. and Lepers, E., 2003. Dynamics of land-use and land-cover change in tropical regions. Annual review of environment and resources, 28(1), pp.205-241. https://doi.org/10.1146/annurev.energy.28.050302.105459
  3. Landis J.R., Koch, G.G., 1977. The measurement of observer agreement for categorical data. Biometrics33(1), p.159174. https://doi.org/10.2307/2529310
  4. Mirzakhani, A., Behzadfar, M. and Azizi Habashi, S., 2025. Simulating urban expansion dynamics in Tehran through satellite imagery and cellular automata Markov chain modelling. Modeling Earth Systems and Environment, 11(2), p.145.
  5. Nasiri, V., Deljouei, A., Moradi, F., Sadeghi, S.M.M. and Borz, S.A., 2022. Land use and land cover mapping using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A comparison of two composition methods. Remote Sensing, 14(9): 1977. https://doi.org/10.3390/rs14091977
  6. Onuegbu, F.E. and Egbu, A.U., 2024. Employing post classification comparison to detect land use cover change patterns and quantify conversions in Abakaliki LGA Nigeria from 2000 to 2022. Scientific Reports, 14(1), p.9384.

https://doi.org/10.1038/s41598-024-59056-w

  1. Potapov, P., Hansen, M.C., Kommareddy, I., Kommareddy, A., Turubanova, S., Pickens, A., Adusei, B., Tyukavina, A. and Ying, Q., 2020. Landsat analysis ready data for global land cover and land cover change mapping. Remote Sensing12(3), p.426.
  2. Radyn Majd, G., Jozi, S. A., Hejazi, R., Amiri, M. J., Ghaffarzadeh, H. (2021). 'The Examination of the Landscape Metrics Changes Using Urban-Rural Gradiant Analysis Method: The Case Study of Tehran Metropolis', Town and Country Planning, 13(2), pp. 461-489. doi: 10.22059/jtcp.2021.326563.670236
  3. Rawat, K.S., Kumar, S. and Garg, N., 2024. Statistical comparison of simple and machine learning based land use and land cover classification algorithms: A case study. Journal of Water Management Modeling, vol 32.

DOI: https://doi.org/10.14796/JWMM.H524

  1. Sarvestani, M.S., Ibrahim, A.L. and Kanaroglou, P., 2011. Three decades of urban growth in the city of Shiraz, Iran: A remote sensing and geographic information systems application. Cities, 28(4), pp.320-329.

https://doi.org/10.1016/j.cities.2011.03.002

  1. Seto, K. C., Güneralp, B., & Hutyra, L. 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.

https://doi.org/10.1073/pnas.1211658109

  1. Seto, K.C., Fragkias, M., Güneralp, B. and Reilly, M.K., 2011. A meta-analysis of global urban land expansion. PloS one, 6(8), p.e23777.

https://doi.org/10.1371/journal.pone.0023777

  1. Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P. and Homayouni, S., 2020. Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp.6308-6325.

https://doi.org/10.1109/JSTARS.2020.3026724

  1. Shi, K., Liu, G., Zhou, L., Cui, Y., Liu, S. and Wu, Y., 2023. Satellite remote sensing data reveal increased slope climbing of urban land expansion worldwide. Landscape and Urban Planning, 235, p.104755.

https://doi.org/10.1016/j.landurbplan.2023.104755

  1. Taravat, A., Rajaei, M. and Emadodin, I., 2017. Urbanization dynamics of Tehran city (1975–2015) using artificial neural networks. Journal of Maps, 13(1), pp.24-30. https://doi.org/10.1080/17445647.2017.1305300
  2. Thrall, G.I., 2017. Land use and urban form: The consumption theory of land rent. Routledge.
  3. United Nations, Department of Economic and Social Affairs, Population Division (2020) World Urbanization Prospects: The 2018 Revision. Available at:

https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf

  1. Yuan, Y., Lin, L., Liu, Q., Hang, R. and Zhou, Z.G., 2022. SITS-Former: A pre-trained spatio-spectral-temporal representation model for Sentinel-2 time series classification. International Journal of Applied Earth Observation and Geoinformation106, p.102651. https://doi.org/10.1016/j.jag.2021.102651
  2. Zhang, Y., Zhao, L., Zhao, H. and Gao, X., 2021. Urban development trend analysis and spatial simulation based on time series remote sensing data: A case study of Jinan, China. Plos one, 16(10), p.e0257776.

https://doi.org/10.1371/journal.pone.0257776

  1. Zhao, Q., Haseeb, M., Wang, X., Zheng, X., Tahir, Z., Ghafoor, S., Mubbin, M., Kumar, R.P., Purohit, S., Soufan, W. and Almutairi, K.F., 2024. Evaluation of land use land cover changes in response to land surface temperature with satellite indices and remote sensing data. Rangeland Ecology & Management, 96, pp.183-196. https://doi.org/10.1016/j.rama.2024.07.003