Monitoring LULC changes and its impact on the LST and NDVI in District 1 of Shiraz City

  • Mehran FatemiEmail author
  • Mahdi Narangifard
Original Paper


Today, immediate and long-term change detection and monitoring using remote sensing (RS) data and geographical information system (GIS) is of paramount importance in generating information about the latest land use/land cover (LULC), land surface temperature (LST), and normalized difference vegetation index (NDVI) in accordance with spatial and temporal changes. Therefore, to obtain these components, a multi-temporal dataset was used consisting of two sets of Landsat Thematic Mapper (TM) images from 1986 to 2011 period across District 1 of Shiraz. Additionally, to investigate the relationship between LST and NDVI over seasons, four Landsat images were used. LULC, LST, and NDVI components were retrieved using Landsat image in ERDAS IMAGINE 9.2 image processing software. Results showed that during the study period, the city had experienced a massive urban (residential) growth. Moreover, change detection suggested that residential areas had increased by 13.17 km2 and vegetation zones (garden) and barren lands had decreased by 4.6 and 8.63 km2, respectively, during 1985–2011 period. The study of the relationship between vegetation index (land cover) and vegetation (land use) in District 1 showed that with reduced vegetation zone (land use), the quality of vegetation (land cover) had deteriorated. These findings indicate that reduced quality of vegetation cover and consequently its Reduction can have a positive effect on the temperature patterns. In general, the negative correlation between vegetation and LST caused by lower vegetation quality was less significant in 2011 compared to 1986, while there the correlation between vegetation and LST in summer was higher than other seasons.


Land cover and land use LST NDVI District one shiraz 


  1. Abedini M, Said MAM, Ahmad F (2012) Clustering approach on land use land cover classification of Landsat TM over Ulu Kinta catchment. World Appl Sci J 17(7):809–817Google Scholar
  2. Ahmadi M, Narangifard M (2015) Land use change detection and its effects on the temperature range in the one Zone City of shiraz. J Environ Sci 13(2):111–120Google Scholar
  3. Ahmadi M, Ashorlo D, Narangifard M (2013) Temporal–spatial variation and thermal patterns, using ETM+ & TM data for Shiraz city. Remote Sens GIS 4(4):55–67Google Scholar
  4. Ahmadi M, Ashorlo D, Narangifard M (2015) Spatial analysis temperatures the city of Shiraz in the warm seasons and cold using statistical analysis and satellite images. Geogr Res 30(2):147–160Google Scholar
  5. Amiri R, Weng Q, Alimohammadi A, Alavipanah SK (2009) Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sens Environ 113(12):2606–2617. CrossRefGoogle Scholar
  6. Bayarsaikhan U, Boldgiv B, Kim KR, Park KA, Lee D (2009) Change detection and classification of land cover at Hustai National Park in Mongolia. Int J Appl Earth Obs Geoinf 11(4):273–280. CrossRefGoogle Scholar
  7. Benali A, Carvalho AC, Nunes JP, Carvalhais N, Santos A (2012) Estimating air surface temperature in Portugal using MODIS LST data. Remote Sens Environ 124:108–121. CrossRefGoogle Scholar
  8. Bhandari AK, Kumar A, Singh GK (2012) Feature extraction using normalized difference vegetation index (NDVI): a case study of Jabalpur city. Procedia Technol 6:612–621. CrossRefGoogle Scholar
  9. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46CrossRefGoogle Scholar
  10. Du Y, Teillet PM, Cihlar J (2002) Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection. Remote Sens Environ 82(1):123–134. CrossRefGoogle Scholar
  11. Effat HA, Hassan OAK (2014) Change detection of urban heat islands and some related parameters using multi-temporal Landsat images; a case study for Cairo city, Egypt. Urban Climate 10:171–188. CrossRefGoogle Scholar
  12. Fichera CR, Modica G, Pollino M (2012) Land cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. Europ J Remote Sens 45(1):1–18. CrossRefGoogle Scholar
  13. Fu P, Weng Q (2016) A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sens Environ 175:205–214. CrossRefGoogle Scholar
  14. Grigsby SP, Hulley GC, Roberts DA, Scheele C, Ustin SL, Alsina MM (2015) Improved surface temperature estimates with MASTER/AVIRIS sensor fusion. Remote Sens Environ 167:53–63. CrossRefGoogle Scholar
  15. Hafner J, Kidder SQ (1999) Urban heat island modeling in conjunction with satellite-derived surface/soil parameters. J Appl Meteorol 38(4):448–465CrossRefGoogle Scholar
  16. Janzen DT, Fredeen AL, Wheate RD (2006) Radiometric correction techniques and accuracy assessment for Landsat TM data in remote forested regions. Can J Remote Sens 32(5):330–334. CrossRefGoogle Scholar
  17. Jiang J, Tian G (2010) Analysis of the impact of land use/land cover change on land surface temperature with remote sensing. Procedia Environ Sci 2:571–575. CrossRefGoogle Scholar
  18. Julien Y, Sobrino JA, Verhoef W (2006) Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999. Remote Sens Environ 103(1):43–55. CrossRefGoogle Scholar
  19. Kumar D, Shekhar S (2015) Statistical analysis of land surface temperature–vegetation indexes relationship through thermal remote sensing. Ecotoxicol Environ Saf 121:39–44. CrossRefGoogle Scholar
  20. Lazzarini M, Marpu PR, Ghedira H (2013) Temperature-land cover interactions: the inversion of urban heat island phenomenon in desert city areas. Remote Sens Environ 130:136–152. CrossRefGoogle Scholar
  21. Lee SH, Baik JJ (2011) Evaluation of the vegetated urban canopy model (VUCM) and its impacts on urban boundary layer simulation. Asia-Pac J Atmos Sci 47(2):151–165. CrossRefGoogle Scholar
  22. Li YY, Zhang H, Kainz W (2012) Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis, China: using time-series of Landsat TM/ETM+ data. Int J Appl Earth Obs Geoinf 19:127–138. CrossRefGoogle Scholar
  23. Liu Y, Hiyama T, Yamaguchi Y (2006) Scaling of land surface temperature using satellite data: a case examination on ASTER and MODIS products over a heterogeneous terrain area. Remote Sens Environ 105(2):115–128. CrossRefGoogle Scholar
  24. Lu D, Weng Q (2006) Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA. Remote Sens Environ 104(2):157–167CrossRefGoogle Scholar
  25. Ma Y, Kuang Y, Huang N (2010) Coupling urbanization analyses for studying urban thermal environment and its interplay with biophysical parameters based on TM/ETM+ imagery. Int J Appl Earth Obs Geoinf 12(2):110–118. CrossRefGoogle Scholar
  26. Mallick J, Kant Y, Bharath BD (2008) Estimation of land surface temperature over Delhi using Landsat and ETM. J Indian Geophys Union 12(3):131–140Google Scholar
  27. Mallick J, Rahman A, Singh CK (2013) Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city, Delhi-India. Adv Space Res 52(4):639–655. CrossRefGoogle Scholar
  28. Mazidi A, Hoseini FS (2015) Effects of changing land use and land cover on the heat island in urban area of Yazd using remote sensing data. Geogr Dev 38(13):1–12Google Scholar
  29. Merbitz H, Buttstädt M, Michael S, Dott W, Schneider C (2012) GIS-based identification of spatial variables enhancing heat and poor air quality in urban areas. Appl Geogr 33:94–106. CrossRefGoogle Scholar
  30. Miller RB, Small C (2003) Cities from space: potential applications of remote sensing in urban environmental research and policy. Environ Sci Pol 6(2):129–137. CrossRefGoogle Scholar
  31. Mills G, Cleugh H, Emmanuel R, Endlicher W, Erell E, McGranahan G et al (2010) Climate information for improved planning and management of mega cities (needs perspective). Procedia Environ Sci 1:228–246. CrossRefGoogle Scholar
  32. Momeni M, Saradjian MR (2007) Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by day/night LST algorithm. Remote Sens Environ 106(2):190–198. CrossRefGoogle Scholar
  33. Mozafari GA, Narangifard M (2015) The effects of changing level of Maharlou Lake on humidity and temperature level of Shiraz City. Geo - Territ Spat Arrange 14(5):215–230Google Scholar
  34. Packialakshmi S, Ambujam NK, Mahalingam S (2010) Emerging land use changes and their effects on groundwater: a study of the Mambakkam mini watershed, southern suburban area of Chennai, India. J Environ Res Develop 5(2):340–349.
  35. Paria P, Bhatt B (2012) A spatio-temporal land use change analysis of waghodia taluka using RS and GIS. Geosci Res 3(2):96–99Google Scholar
  36. Prasad TL, Sreenivasulu G (2014) Land use/land cover analysis using remote sensing and Gis, a case study on Pulivendula Taluk, Kadapa District, Andhra Pradesh, India. Internat J Sci Res Pub (IJSRP) 4(6):1–5Google Scholar
  37. Pu R, Gong P, Michishita R, Sasagawa T (2006) Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval. Remote Sens Environ 104(2):211–225. CrossRefGoogle Scholar
  38. Rotem-Mindali O, Michael Y, Helman D, Lensky IM (2015) The role of local land-use on the urban heat island effect of Tel Aviv as assessed from satellite remote sensing. Appl Geogr 56:145–153. CrossRefGoogle Scholar
  39. Sasanpour F, Ziaeian P, Bahadori M (2014) Land-use, land cover and thermal islands in Tehran. Geography 39(11):256–270Google Scholar
  40. Senanayake IP, Welivitiya WDDP, Nadeeka PM (2013) Remote sensing based analysis of urban heat islands with vegetation cover in Colombo city, Sri Lanka using Landsat-7 ETM+ data. Urban Clim 5:19–35. CrossRefGoogle Scholar
  41. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90(4):434–440. CrossRefGoogle Scholar
  42. Srivastava PK, Majumdar TJ, Bhattacharya AK (2009) Surface temperature estimation in Singhbhum shear zone of India using Landsat-7 ETM+ thermal infrared data. Adv Space Res 43(10):1563–1574. CrossRefGoogle Scholar
  43. Tan KC, San Lim H, MatJafri MZ, Abdullah K (2010) Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environ Earth Sci 60(7):1509–1521. CrossRefGoogle Scholar
  44. Tan KC, San Lim H, MatJafri MZ, Abdullah K (2012) A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery. Environ Monit Assess 184(6):3813–3829. CrossRefGoogle Scholar
  45. Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384. CrossRefGoogle Scholar
  46. Weng Q, Lu D (2009) Landscape as a continuum: an examination of the urban landscape structures and dynamics of Indianapolis City, 1991–2000, by using satellite images. Int J Remote Sens 30(10):2547–2577CrossRefGoogle Scholar
  47. Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89(4):467–483. CrossRefGoogle Scholar
  48. Yuan F, Bauer ME (2007) Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ 106(3):375–386. CrossRefGoogle Scholar
  49. Zhang J, Wang Y, Li Y (2006) A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6. Comput Geosci 32(10):1796–1805. CrossRefGoogle Scholar
  50. Zhang Y, Odeh IO, Han C (2009) Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. Int J Appl Earth Obs Geoinf 11(4):256–264. CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Department of ClimatologyUniversity of MeybodYazdIran
  2. 2.Department of ClimatologyUniversity of YazdYazdIran

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