Document Type : Research Paper
Authors
1
MSc. Graduate of Forest Sciences and Engineering, Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, I. R. Iran
2
Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, I. R. Iran
3
3Assistant Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, I. R. Iran.
4
Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, I. R. Iran.
Abstract
Urban vegetation monitoring can play a vital role in sustainable city management because of their diverse environmental, social, cultural, and economic functions. In this study, the vegetation trend was assessed using the Normalized Vegetation Difference Index (NDVI) obtained from the Landsat 5 and 8 at a maximum vegetation greenness time by applying a parametric approach (i.e. Ordinary Least Square Linear Regression) and a non-parametric approach (i.e. Theil-Sen and Mann-Kendall) over Tehran city during 2008 - 2019. The MOD13Q1 NDVI product was used as a complementary data due to the lack of appropriate Landsat data for 2011 and 2012. The data collected from four synoptic meteorological stations located in Tehran were used to extract the mean temperature and the total precipitation parameters. The relationship between NDVI variations and climatic characteristics, i.e. the mean temperature and total precipitation of one month before and one year before the maximum NDVI values were analyzed. The NDVI trend analysis showed a slight increase in Tehran vegetation cconditions during 12 years, however, the result of the Mann-Kendall test was not statistically significant (α = 0.05). Trend analysis for 22 individual districts showed a significant negative trend for five districts, and the remaining districts showed a non-significant trend. The NDVI was negatively correlated with temperature, and there was a positive correlation between NDVI and precipitation. The NDVI variations showed a more similar trend to the climate data of one month before NDVI data-set than one year.
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