Vegetation trend analysis using NDVI time series of Modis satellite in the northeast of Iran

Document Type : Complete scientific research article


1 M.Sc. Student of Agrometeorology, Dept. of Water Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

2 . Corresponding Author, Assistant Prof. of Agrometeorology, Dept. of Water Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.

3 Professor of Agrometeorology, Dept. of Water Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.

4 Ph.D. Graduate of Forestry Sciences, University of Tehran, Tehran, Iran.


Background and objectives: Vegetation is very important in providing organic matter, regulating the carbon cycle and exchanging energy at the surface. In recent years, climate change and global warming have created frequent events such as floods, high temperatures and droughts that can damage terrestrial ecosystems. Weather fluctuation due to climate change directly affects vegetation growth, on the other hand, vegetation respond to climate change by regulating water, energy exchange, and carbon dioxide concentrations.
Materials and methods: In this study, which was conducted to investigate the trend of vegetation changes in the study area during the period 2001-2018, 16-day combined time series data of MODIS-NDVI called MOD13Q1 with a spatial resolution of 250 meters was used. In this study in order to investigate the significance trend of vegetation cover, the non-parametric Mann-Kendall method was taken. Also the relationship between vegetation changes and altitude was investigated.
Results: Of the total area of the study area, 52% of the area had a decreasing trend of vegetation and the rest showed an increasing trend of vegetation, although a significant decrease in vegetation at the level of 5 and 1% occurred in 36% and 32% of the area, respectively. Also, 31 and 26 percent of the study area had a significant increase in vegetation at the level of 5 and 1 percent. In the study of the relationship between Z-Kendall statistic and height, the results showed that with increasing the height of Z-Kendall statistic increases and the correlation coefficient of height with Z-statistic is about 0.62. It seems that significant positive trends in vegetation occur at higher altitudes and significant negative trends in vegetation occur at lower altitudes. 99% and altitudes of 670 and 840 were obtained for the negative trend of 95 and 99%. In other words, at altitudes above 2030 and 1860, the trend of vegetation changes is positive and at altitudes below 670 and 840 meters, the trend of vegetation changes is significantly decreasing.
Conclusion: The results of this study showed a significant trend of greening at altitudes of more than 2030 meters in the region. It seems that with the increase of temperature due to climate change at elevated area, suitable temperature conditions and increasing of growing season length is provided for crop growth at altitudes. This increase in vegetation was further observed in the east and northeast of the study area. Also, the significant decrease in vegetation in low altitude areas less than 670 meters can be due to increased water requirement of low altitude plants and the occurrence of temperature stresses in these areas, which are mostly in the eastern, southern and low altitudes of the study area. However, it seems that the area between these two altitudes have not had a significant trend in vegetation changes.


1.Fensholt, R., and Proud, S.R. 2012. Evaluation of earth observation based global long term vegetation trends comparing GIMMS and MODIS global NDVI time series. Remote Sensing of Environment. 119: 131-147.
2.Bonan, G.B. 2008. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science.320: 1444-1449.
3.Craine, J.M., Nippert, J.B., Elmore, A.J., Skibbe, A.M., Hutchinson, S.L., and Brunsell, N.A. 2012. Timing of climate variability and grassland productivity. Proceedings of the National Academy of Sciences. 109: 9. 3401-3405.‏
4.Wang, D., and Alimohammadi, N. 2012. Responses of annual runoff, evaporation, and storage change to climate variability at the watershed scale. Water Resour. Res. 48: 5. 5546.
5.Zhou, L., Tian, Y., Myneni, R.B., Ciais, P., Saatchi, S., Liu, Y.Y., et al. 2014. Widespread decline of congo rainforest greenness in the past decade. Nature.509: 7498. 86-90.
6.Luo, L., Ma, W., Zhuang, Y., Zhang, Y., Yi, S., Xu, J., Zhang, Z. 2018. The impacts of climate change and human activities on alpine vegetation and permafrost in the Qinghai-Tibet Engineering Corridor. Ecological Indicators. 93: 24-35.
7.Pan, N., Feng, X., Fu, B., Wang, S., Ji, F., and Pan, S. 2018. Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends. Remote Sensing of Environment. 214: 59-72.
8.Yang, J., Weisberg, P.J., and Bristow, N.A. 2012. Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: Comparison of vegetation indices and spectral mixture analysis. Remote Sensing of Environment. 119: 62-71.
9.Hashemi Darreh Badami, S., Nouraei Sefat, A., Karimi, S., and Theoretical, S. 2015. Analysis of the development trend of urban heat island in relation to land use change / cover using the time series of Landsat images. Remote Sensing and Geographic Information System in Natural Resources. 6: 3. 28-15. (In Persian(
10.Mirahsani, M., Salman Mahini, A.R., Sufyanian, A.R., Modares, R., Jafari, R., and Mohammadi, J. 2017. Evaluation of Vegetation Water Storage Index (VSWI) Time series images of Madis sensor in drought monitoring of Gavkhooni watershed, Journal of Applied Ecology. 4: 47-31. (In Persian (
11.Niromand, H., and Bozornia, M. 2010. Introduction to time series. Ferdowsi University of Mashhad. (In Persian)
12.Willis, K.S. 2015. Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation. 182: 233-242.
13.Moradi, F., Mokhtari, M.H., and Ardakhni, A. 2013. "Compare of Techniques of urban areas and changes in land use optimization models to assess changes using remote sensing and GIS". International congress of Civil and Architectural Engineering Sustainable Urban Development. Tabriz. (In Persian)
14.Xiao, J., and Moody, A. 2005. Geographical distribution of global greening trends and their climatic correlates: 1982-1998. International Journal of Remote Sensing. 26: 11. 2371-2390.
15.De Beurs, K.M., and Henebry, G.M. 2005. Land surface phenology and temperature variation in the International Geosphere-Biosphere Program high-latitude transects. Global Change Biology. 11: 5. 779-790.
16.De Jong, R., de Bruin, S., de Wit, A., Schaepman, M.E., and Dent, D.L. 2011. Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment. 115: 2. 692-702.
17.Colditz, R.R., Ressl, R.A., and Bonilla-Moheno, M. 2015. Trends in 15-year MODIS NDVI time series for Mexico. In 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). Jul. 22-24 Annecy, France, pp. 1-4.
18.Dastigerdi, M., Nadi, M., Raeini, M., and Kiapash, Kh. 2022 .Vegetation trend analysis using NDVI time series of Modis satellite in North Khorasan province. National Conference on Environmental Change using Remote Sensing Technology and GIS. Sari, Iran. (In Persian)
19.Kiapasha, K., Darvishsefat, A.A., Zargham, N., Attarod, P., Nadi, M., and Schaepman, M. 2017a. Greening trend in the Hyrcanian forests using NOAA NADVI time series during 1981-2012. Forest and Wood Products. 70: 3. 409-420. (In Persian)
20.Kiapasha, K., Darvishsefat, A., Julien, Y., Sobrino, J., Zargham,N., Attarod, P., Schaepman, M. 2017b. Trends in Phenological Parameters and Relationship Between Land Surface Phenology and Climate Data in the Hyrcanian Forests of Iran. In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10: 11. 4961-4970.
21.Abdi, O., Shirvani, Z., and Buchroithner, M.F. 2018. Spatiotemporal drought evaluation of Hyrcanian deciduous forests and semiā€steppe rangelands using moderate resolution imaging spectroradiometer time series in Northeast Iran. Land Degrad Dev.29: 2525-2541.
22.Abdolalizadeh, Z., Ebrahimi, A., and Mostafazadeh, R. 2019. Landscape pattern change in Marakan protected area, Iran. Reg Environ Change.19: 1683-1699.
23.Abdolalizadeh, Z., Ghorbani, A., Mostafazadeh, R., and Moameri, M. 2020. Rangeland canopy cover estimation using Landsat OLI data and vegetation indices in Sabalan rangelands, Iran. Arabian Journal of Geoscience. 245: 13.
24.Akbarzadeh, M., and Mirhaji, S.T. 2006. Vegetation changes under precipitation in Steppic rangelands Rudshur. Iranian Journal of range and desert research.13: 3. 222-235. (In Persian)
25.Arefzadeh, M., Race Abbasi, H., Solar, M., Mahmoudzadeh, A., Shamsi, M., Farrokhi, H., Mohammadpour, T., Aghamalaei, E., Nodehi, F., and Shadloo, M. 2020. Khorasan Razavi Province, 10th grade, high school, Iran Textbook Publishing Company, Tehran, Iran. pp. 15-30. (In Persian)
26.Jafari, T., Maghami Moghim, Gh., and Azimian, M. 2020. North Khorasan Province, Tenth Grade, Secondary School, Iran Textbook Publishing Company, Tehran, Iran. 172p. (In Persian)
27.Tucker, C.J. 1979, Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment. 8: 127-150.
28.Wu, D., Wu, H., Zhao, X., Zhou, T., Tang, B., Zhao, W., and Jia, K. 2014. Evaluation of Spatiotemporal Variations of Global Fractional Vegetation Cover Based on GIMMS NDVI Datafrom 1982 to 2011. Remote Sensing.6: 5. 4217-4239.
29.De Jong, R., de Bruin, S., de Wit, A., Schaepman, M.E., and Dent, D.L. 2011. Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment. 115: 2. 692-702.
30.Masihpour, M., Darvish Sefat, A., and Rahmani, R. 2019. Analysis of long-term trend of vegetation changes using MODIS-NDVI time series (Case study: Kurdistan province). Forests and wood products (Iranian natural resources).
72: 3. 193-204. (In Persian)