Quantitative Assessment of the Trends and Spatio-Temporal Variability of Vegetation Growth in Iran using Wavelet Transform and Statistical Approaches

Document Type : Original Article


1 Department of Marine Meteorology & Physical Oceanography, Atmospheric Science & Meteorological Research Center

2 Department of surveying Engineering, Islamic Azad University, Taft Branch, Taft, Yazd, Iran

3 Iranian National Institute of Oceanography and Atmospheric Science

4 Aerospace Research Institute (Ministry of Science, Research and Technology)



In this research, the Spatio-temporal variability of vegetation growth was evaluated using Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 Enhanced Vegetation Index (EVI) at 1km resolution data products during 2003-2018 over Iran. The total variability, the amplitude of the annual phenological cycle, seasonal cycle peak, inter-annual variation, minimum level of variations, the timing of maximum vegetation, coefficient of variations, Sen's slope, Mann-Kendall and Hurst exponent indices were calculated as independent variables for all pixels. The results indicated that the variations of inter-annual cycles show a relatively stable trend, and relatively flat trend curves were observed for all types of vegetation in Iran. The seasonal phenological cycles were the most sources of intra-annual variations, and the maximum and minimum values were observed in mid-summer and early winter, respectively. The EVI peaks were observed in spring and summer and spatially have been distributed in 30.2% and 20.6% of the total areas. More than 44% of the total area showed stable vegetation coverages, and 1.7% of the total area showed a large amplitude of vegetation variations. About 89% of vegetated areas (37.7% of the total area) in the north, west, and southwest regions show improved sustainable variations with positive changes. The results show that about 5.5% of the vegetation coverage in the northeast and southwest was decreased. The presented analytical indices in this research are a cost-effective method for managing and predicting future environmental trends in developing regions at risk of desertification.