Document Type : Original Article


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

2 Department of Natural Geography, Faculty of Humanities Sciences, Tarbiat Modares, Tehran, Iran


Change detection is a process to find the paradoxical regions in different temporal imageries of a similar area. Vegetation is very effective at the absorption of grime and lead, prevention of the spread of contaminations in urban environments, clearing the air, and reduction of heat islands. The need to investigate the decrease or increase in vegetation is extremely important in Tehran as a metropolis, as well as its satellite counties because of the increase in population and construction. The purpose of this research was to investigate the spatiotemporal changes in the vegetation of Tehran and its satellite cities in association with temperature during different temporal periods. The results of this research can be useful in studies concerning urban viability, reducing the effects of urban heat islands, and environmental sciences.
Material and methods:
Initially, the extraction and preparation of data were carried out using the ETM+ sensor of Landsat 7 satellite from 2001 to 2015, with June being selected as the hottest month of the study area. Then, the selection of days to be studied and correcting imagery, preparing LULC maps and plotting the area percentage was done. The computation of vegetation indices and built-up areas and the calculation of land surface temperature along with the assessment of the accuracy of surface temperature data were other stages of the research methodology. Finally, the area percentage of each index, as well as the scatter plot and Gaussian function chart were produced and the spatial variation of vegetation was studied.
Results and discussion:
According to the land use and land cover map (LULC) in 2001, 2005, 2010, and 2015, the vegetation significantly increased in 2015 compared to previous courses. The development of the residential area in the west region was higher than in other regions. In the charts of the area percentage for each land use class and its change over the four selected years, the vegetation percentage has been decreased since 2001, which continued in 2010 and 2015. In this study, the relationships between surface temperature and NDVI and SAVI indices were not linear, which showed that there is another controlling factor. In the normal density function chart, which is usually described by mean and standard deviation, variations of NDVI and SAVI indices were similar in the second and third periods, and the mean increased in these two periods compared to the first period, and the height of curve increased due to the reduction of variance. In this study, the results showed a decrease in the value of LST in the second and third periods from 2006 to 2015. On the other hand, the vegetation area was increasing in the region. From a spatial point of view, Tehran has the highest percentage of class one of NDVI that have no vegetation surfaces. 
Regarding the study of NDVI, SAVI and NDBI indices, the overall trend of vegetation cover in the study area was increasing. Considering the values of 15R2"> , the vegetation in Ray County was defunct. In the cities of Robat Karim and Tehran, vegetation cover was increasing. The high growth of satellite towns in the surroundings of Tehran has led to the allocation of land and fields and vegetation to residential areas, which exacerbate the heat islands and the unfavorable conditions of life.


  1. Ahmadi, M., and Dadashiroudbari A., 2017. The Identification of urban thermal islands based on an environmental approach, case study: Isfahan province. Geography and Environmental Planning, 28, 1-20. (In Persian with English abstract)
  2. Alavipanah, S. K., Rezaei, A. Azadighatar, S., Azghandi, H, J., 2016. "Investigation of incontinuous levels of normalized vegetation difference as display parameters of urban thermal isles using satellite images," Geography and Planning, 55, 183-208. (In Persian with English abstract)
  3. Amanollahi, J., Abdullah, A., Ramli, M., Pirasteh S., 2012. Land surface temperature assessment in semi-arid residential area of Tehran, Iran using Landsat imagery, 20, 319-326.
  4. Babayan,I., Rezaei pour, A., and Ahangarzadeh, Z., 2014. Simulation of climatic comfort profile in khorasan razavi province under climate change scenarios. Geographical Study of Drought Areas, 5, 95-112. (In Persian with English abstract)
  5. Bokaie, M., Zarkesh, MK., Arasteh PD, Hosseini A., 2016. Assessment of urban heat island based on the relationship between land surface temperature and land use/ land cover in Tehran. Sustainable Cities and Society. 23, 94-104.
  6. Bolch T., 2007. Climate change and glacier retreat in northern tien shan (kazakhstan/kyrgyzstan) using remote sensing data. Global and Planetary Change, 56, 1-12.
  7. Price, C. J., 1990. Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE TRANSACTIONS ON GEOSCIENCE AND REMUTE SENSING. 5, 940-8.
  8. Carlson, T.N., Capehart, W.J., Gillies, R.R., 1995. New look at the simplified method for remote sensing of daily evapotranspiration. Remote Sensing of Environment, 54, 161-7.
  9. Carlson, T.N., Traci Arthur, S., 2000. The impact of land use — land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective, Global and Planetary Change, 25, 49-65.
  10. Chen, Z.M., Babiker, I.S., Chen, Z.X., Komaki, K., Mohamed, M.A.A., Kato, K., 2004. Estimation of interannual variation in productivity of global vegetation using NDVI data, International Journal of Remote Sensing, 25, 3139-59.
  11. Cohen, W.B., Yang, Z., Kennedy, R., 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation, Remote Sensing of Environment, 114, 2911-24.
  12. Defries, R.S., Belward A., 2000. Global and regional land cover characterization from satellite data: An introduction to the Special Issue, int. j. remote sensing, 21, 1083–1092.
  13. Dewi, R., Bijker, W., Stein, A., Aris Marfai, M., 2016. Fuzzy classification for shoreline change monitoring in a part of the northern coastal area of java, Indonesia. MDPI AG, 190. doi: MDPI AG.
  14. Farina, A., 2012. Exploring the relationship between land surface temperature and vegetation abundance for urban heat island mitigation in Seville, Spain. LUMA-GIS Thesis.
  15. Fensham, R. J., Fairfax, R. J., Archer, S. R., 2005. Rainfall, land use and woody vegetation cover change in semi‐arid Australian savanna. Journal of Ecology 93, 596-606.
  16. Gillies, R.R., Carlson, T.N., 1995. Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. Journal of Applied Meteorology, 34, 745-56.
  17. Gillies, R.R., Kustas, W.P., Humes, K.S., 1997. A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface. International Journal of Remote Sensing, 18, 3145-66.
  18. Goward, S.N., Hope, A.S., 1989. Evapotranspiration from combined reflected solar and emitted terrestrial radiation: Preliminary FIFE results from AVHRR data. Advances in Space Research, 9, 239-49.
  19. Guo, G., Wu, Z., Xiao, R., Chen, Y., Liu, X., Zhang, X., 2015 Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landscape and Urban Planning, 135, 1-10.
  20. Huete, AR. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309.
  21. Irimo, 2018. Available online at:
  22. Ismaeil Zadeh, H., and Shafiee Sabet, N., 2013. Study of land use changes and unsustainable in the northern ecosystem of Tehran (Case study: Darakeh-Velenjak basin). Researches of land knowledge, 4 (3), 83-102. (In Persian with English abstract)
  23. Junfeng, W., Shiyin, L., Wanqin, G., Xiaojun, Y., Junli, X., Weijia, B., 2017. Surface-area changes of glaciers in the Tibetan Plateau interior area since the 1970s using recent Landsat images and historical maps. Annals of Glaciology, 55, 213-22.
  24. Kennedy, R.E., Yang, Z., Cohen, W.B., 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms. Remote Sensing of Environment, 114, 2897-910.
  25. Khan, S., Qasim, S., 2017. Spatial and temporal dynamics of land cover and land use in district pishin through GIS. Science, Technology and Development, 36, 6-10.
  26. Khtan, A., 2016. Estimate the Mean Daily Temperature from Mean Monthly (Using Gaussian Function), International Journal of Advances in Science, Engineering and Technology (IJASEAT), 4, 71-73.
  27. Li, J., Song, C., Cao, L., Zhu, F., Meng, X., Wu, J., 2011. Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China. Remote Sensing of Environment, 115(12), 3249-63.
  28. Liu, D., Cai, S., 2012. A spatial-temporal modeling approach to reconstructing land-cover change trajectories from multi-temporal satellite imagery. Annals of the Association of American Geographers, 102, 1329-1347.
  29. Ma, Y., Chen, F., Liu, J., He, Y., Duan, J., Li, X., 2016. An automatic procedure for early disaster change mapping based on optical remote sensing. Remote Sensing, 8, 272.
  30. NASA (National Aeronautics and Space Administration). 2016. Landsat 7 Science Data Users Handbook, U.S. Geological Survey (USGS).
  31. Rahlao, S.J., Hoffman, M.T. 2008. Long-term vegetation change in the Succulent Karoo, South Africa following 67 years of rest from grazing. Journal of Arid Environments, 72, 808-19.
  32. Rogan, J., Chen, D., 2004. Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in planning, 61, 301-25.
  33. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1973. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium, Washington DC.
  34. Sadeghinia, A., Alijani, B., and Ziaian, P., 2012. Spatial analysis - temporal analysis of thermal island of Tehran metropolitan area using remote sensing and geographic information system. Geography and environmental hazards, 4, 1-18. (In Persian with English abstract)
  35. Schroeder, T.P., Healey, S.G., Moisen, G., Frescino, T.B., Cohen, W., Huang, C., 2014. Improving estimates of forest disturbance by combining observations from Landsat time series with U.S. Forest Service Forest Inventory and Analysis data. 61–73.
  36. Schroeder, T.A., Wulder, M.A., Healey, S.P., Moisen, G.G., 2011. Mapping wildfire and clear cut harvest disturbances in boreal forests with Landsat time series data. Remote Sensing of Environment, 115, 1421-33.
  37. Shakiba,A., Ziaian Firoozabadi, P., Ashourlou, D., and Namdari, S., 2009. Analysis of the relationship between land use and land cover and thermal islands of Tehran, using ETM + data. Remote sensing journal, Iran GIS, 36-36. (In Persian with English abstract)
  38. Singh, A. 1988. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing 10.
  39. Singh, A., 1989. Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing. 10, 989-1003.
  40. Song, X-P., Sexton, J., Huang, C., Channan, S., Townshend, J., 2016. Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Elsevier BV, 1-13.
  41. Soroudi, M., and Jozi, S. A., 2011. Prognosis of vegetation change using the Markov model (case study: district 4 of Tehran municipality). Remote Sensing and Geographic Information System in Natural Resources 6, 83-96. (In Persian with English abstract)
  42. Soroudi, M., and Jozi, S. A., 2013. Remote sensing and implementation of the Markov model for the study of urban green spaces (case study: District 1 of Tehran Municipality). Ecology 65, 113-122. (In Persian with English abstract)
  43. Soroudi, M., and Jozi, S. A., 2016. Study of quality changes of green space in Tehran from 1990 to 2006 (case study: District 5 of Tehran Municipality). Quarterly Journal of Environmental Science and Technology. 2016; 18 (Special Note No. 3 Design and Administration): 335-344. (In Persian with English abstract)
  44. Tayyebi, A., Shafizadeh-Moghadam, H., Tayyebi, A.H., 2018. Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran. Land Use Policy. 71, 459-69.
  45. Thomas, R.F., Kingsford, R.T., Lu, Y., Hunter, S.J., 2011. Landsat mapping of annual inundation (1979–2006) of the Macquarie Marshes in semi-arid Australia. International Journal of Remote Sensing. 32, 4545-69.
  46. USGS, 2018. U.S. Geological Survey. Available online at
  47. Vitousek, P.M., Mooney, H.A., Lubchenco, J., Melillo, J.M., 1977. Human domination of earth ecosystems. Science. 277, 494.
  48. Weng, Q., Lu, D., Schubring, J., 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment. 89, 467-83.
  49. White, J.D., Gutzwiller, K.J., Barrow, W.C., Randall, L.J., Swint, P., 2008. Modeling mechanisms of vegetation change due to fire in a semi-arid ecosystem. Ecological Modelling. 214, 181-200.
  50. Yang, L., Xian, G., Klaver, J.M., Deal, B., 2003. Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering & Remote Sensing. 69, 1003-10.
  51. Yu, H., Yang, W., Hua, G., Ru, H., Huang, P., 2017. Change detection using high resolution remote sensing images based on active learning and Markov random fields. Remote Sensing. 9, 1233.
  52. Zha, Y., Gao, J., Ni, S., 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing. 24, 583-94.
  53. Zhu, Z., Woodcock, C.E., Olofsson, P., 2012. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sensing of Environment. 122, 75-91.