Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 783–788, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-783-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 783–788, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-783-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

VEGETATION DYNAMICS TREND USING SATELLITE TIME SERIES IMAGERY

Z. Najafi, P. Fatehi, and A. A. Darvishsefat Z. Najafi et al.
  • Department of Forestry and Forest Economic, Faculty of Natural Resources, University of Tehran, Karaj, Iran

Keywords: Vegetation dynamic, Time Series, Mann-Kendall, NDVI, Regression, Theil-Sen

Abstract. In this study, the trend of vegetation dynamics in Kermanshah city assessed using NDVI MOD13Q1 product over the time period of 2000–2017. Based on time series imagery the pick of vegetation phenology stage (maximum NDVI) identified, then the trend of vegetation dynamic was investigated using the Ordinary Least Square regression and the Theil-Sen approaches. To generate a pixel-wise trend map, a pixel-based vegetation dynamics was also implemented. A non-parametric Mann-Kendall statistics approach was used to examine a statistically significant trend analysis. The mean maximum NDVI observed for the first half or second half of April. Trend analysis using regression and Theil-Sen methods indicated a no-trend in vegetation fractions. The pixel-based trend assessment using regression showed that a 50% of the study area faced a positive trend and reaming part faced a negative trend. The Theil-Sen method revealed the no-trend for a large majority of area. The Mann-Kendall test indicated that only 20 percent of the area shows a statistically significant trend.