Assessing the effects of the climate change on land cover changes in different time periods

  • Hassan KhosraviEmail author
  • Ali Azareh
  • Hadi Eskandari Dameneh
  • Elham Rafiei Sardoii
  • Hamed Eskandari Dameneh
Original Paper


The present research evaluated the relation between the normalized difference vegetation index (NDVI) changes and the climate change during 2000–2014 in Qazvin Plain, Iran. Daily precipitation and mean temperature values during 2015–2040 and 2040–2065 were predicted using the statistical downscaling model (SDSM), and these values were compared with the values of the base period (2000–2014). The MODIS images (MOD13A2) were used for NDVI monitoring. In order to investigate the effects of climate changes on vegetation, the relationship between the NDVI and climatic parameters was assessed in monthly, seasonal, and annual time periods. According to the obtained results under the B2 scenario, the mean annual precipitation at Qazvin Station during 2015–2040 and 2040–2065 was 6.7 mm (9.3%) and 8.2 mm (11.36%) lower than the values in the base period, respectively. Moreover, the mean annual temperature in the mentioned periods was 0.7 and 0.92 °C higher than that in the base period, respectively. Analysis of the correlations between the NDVI and climatic parameters in different periods showed that there is a significant correlation between the seasonal temperature and NDVI (P < 0.01). Moreover, the NDVI will increase 0.009 and 0.011 during 2015–2040 and 2040–2065, respectively.


NDVI Temperature Precipitation Qazvin Plain Remote sensing 



This research forms part of a research project financed by the Iran National Science Foundation (INSF) no: 94011898.


  1. Bai ZG, Dent DL, Olsson L, Schaepman ME (2008) Proxy global assessment of land degradation. Soil Use Manag 24:223–234CrossRefGoogle Scholar
  2. Bulcock HH, Jewitt GP (2010) Spatial mapping of leaf area index using hyperspectral remote sensing for hydrological applications with a particular focus on canopy interception. Hydrol Earth Syst Sci 14:383–392CrossRefGoogle Scholar
  3. Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon WT, Laprise R, Maga˜na Rueda V, Mearns L, Men’endez CG, R¨ais¨anen J, Rinke A, Sarr A, Whetton Z (2007) Regional climate projections. Climate change 2007: the physical science basis, contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  4. Eckert S, Hüsler F, Liniger H, Hodel E (2015) Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. J Arid Environ 113:16–28CrossRefGoogle Scholar
  5. Etemadi H, Samadi S, Sharifikia M (2014) Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland. Clim Dyn 42:2899–2920CrossRefGoogle Scholar
  6. Fang JY, Piao SL, He JS, Ma W (2004) Increasing terrestrial vegetation activity in China, 1982–1999. Sci China Ser C 47:229–240Google Scholar
  7. Guo N, Zhu Y, Wang J, Deng C (2008) The relationship between NDVI and climate elements for 22 years in different vegetation areas of northwest China. J Plant Ecol 32:319–327Google Scholar
  8. Gao Y, Huang J, Li S, Li S (2012) Spatial pattern of non-stationarity and scale-dependent relationships between NDVI and climatic factors—a case study in Qinghai-Tibet Plateau, China. Ecol Indic 20:170–176CrossRefGoogle Scholar
  9. Intergovernmental Panel on Climate Change (IPCC) (2007) Working group III report, mitigation of climate change, chapter 6, residential and commercial buildings. M. Levine (USA) and D. U¨rge-Vorsatz (Hungary), coordinating lead authors. Geneva, Switzerland: Intergovernmental Panel on Climate ChangeGoogle Scholar
  10. Ji L, Peters AJ (2004) A spatial regression procedure for evaluating the relationship between AVHRR/NDVI and climate in the northern Great Plains. Int J Remote Sens 25:297–311CrossRefGoogle Scholar
  11. Khatir A, Abdelmalik A, Abdalla AM, Elmobark MG, Imad-eldin SA, Babiker A, El-Hag FM (2015) Evaluation of climate change effects on the growing season in Butana region and North Kordofan, Sudan. Sudan Academy of Sciences Journal-Special Issue (Climate Change) 11:43–55Google Scholar
  12. Kulawardhana RW (1999) Determination of spatio-temporal variations of vegetation cover, land surface temperature and rainfall and their relationships over Sri Lanka using NOAA AVHRR data. Thesis for the degree of Masters of Philosophy in Integrated Water Resources Management. Institute of Agriculture University of Peradeniya in Sri Lanka. pp 1–67Google Scholar
  13. Liu Y, Li Y, Li S, Motesharrei S (2015) Spatial and temporal patterns of global NDVI trends: correlations with climate and human factors. Remote Sens 7:13233–13250CrossRefGoogle Scholar
  14. Myneni RB, Ramakrishna R, Nemani R, Running SW (1997) Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Trans Geosci Remote Sens 35:1380–1393CrossRefGoogle Scholar
  15. Ning T, Liu W, Lin W, Song X (2015) NDVI variation and its responses to climate change on the northern Loess Plateau of China from 1998 to 2012. Adv Meteorol 2015:10CrossRefGoogle Scholar
  16. Philippon N, Jarlan L, Martiny N, Camberlin P, Mougin E (2007) Characterization of the interannual and intraseasonal variability of West African vegetation between 1982 and 2002 by means of NOAA, AVHRR, NDVI data. J Clim 20:1202–1218CrossRefGoogle Scholar
  17. Samadi S, Wilson CAME, Moradkhani H (2013) Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model. Theor Appl Climatol 114:673–690CrossRefGoogle Scholar
  18. Semiromi S, Moradi HR, Khodagholi M (2014) Simulation and prediction some of climate variable by using multi line SDSM and global circulation models (case study: Bar Watershed Neishabour). Human and Environment 28:1–15Google Scholar
  19. Shaw A, Sheppard S, Burch S, Flanders D, Wiek A, Carmichael J, Cohen S (2009) Making local futures tangible—synthesizing, downscaling, and visualizing climate change scenarios for participatory capacity building. Glob Environ Chang 19:447–463CrossRefGoogle Scholar
  20. Sims NC, Colloff MJ (2012) Remote sensing of vegetation responses to flooding of a semi-arid floodplain: implications for monitoring ecological effects of environmental flows. Ecol Indic 18:387–391CrossRefGoogle Scholar
  21. Wang J, Rich P, Price K (2003a) Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. Int J Remote Sens 24:2345–2364CrossRefGoogle Scholar
  22. Wessels KJ, Prince SD, Frost PE, Van Zyl D (2004) Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series. Remote Sens Environ 91:47–67CrossRefGoogle Scholar
  23. Wang J, Rich PM, Price KP (2003b) Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. Int J Remote Sens 24:2345–2364CrossRefGoogle Scholar
  24. Yao J, He X, Li X, Chen W, Tao D (2012) Monitoring responses of forest to climate variations by MODIS NDVI: a case study of Hun River upstream, northeastern China. Eur J For Res 131:705–716CrossRefGoogle Scholar
  25. Yin Z, Williams THL (1997) Obtaining spatial and temporal vegetation data from Landsat MSS and AVHRR/NOAA satellite images for a hydrologic model. Photogramm Eng Remote Sens 63:69–77Google Scholar
  26. Zulkarnain H, Shamsudin S, Sobri H (2014) Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theor Appl Climatol 116:243–257CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2017

Authors and Affiliations

  • Hassan Khosravi
    • 1
    Email author
  • Ali Azareh
    • 1
  • Hadi Eskandari Dameneh
    • 2
  • Elham Rafiei Sardoii
    • 1
  • Hamed Eskandari Dameneh
    • 1
  1. 1.University of TehranTehranIslamic Republic of Iran
  2. 2.University of HormozganBandar AbbasIslamic Republic of Iran

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