Analysis of the recent trends in vegetation dynamics and its relationship with climatological factors using remote sensing data for Caspian Sea watersheds in Iran
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Department of Geography, Yazd University, Yazd 8915818411, Iran
Department of Remote Sensing, Yazd University, Yazd 8915818411, Iran
Department of Physics, Institute for Atmospheric Sciences-Weather and Climate, University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland
Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
Department of Environmental Science and Engineering Jiangwan campus, Fudan University, 2005 Songhu Road, Yangpu District Shanghai 200438, China
Department of Agricultural and Environmental Chemistry, University of Life Sciences in Lublin, Akademicka 15, 20-950 Lublin, Poland
Final revision date: 2022-05-05
Acceptance date: 2022-05-12
Publication date: 2022-06-22
Corresponding author
Iman Rousta   

Department of Geography, Yazd University, Assistant professor of climatology, Iran
Int. Agrophys. 2022, 36(3): 139-153
  • Using integrated GIS and RS to display the land surface biological dynamics
  • Using multiple satellite products and indices to identify the vegetation dynamic
  • A climate change point of view about atmosphere and ground phenomena
  • A comprehensive statistical analysis concerning CSW and its subregions separately
This study used NDVI, ET, and LST satellite images collected by moderate resolution imaging spectroradiometer and tropical rainfall measuring mission sensors to investigate seasonal and yearly vegetation dynamics, and also the influence of climatological factors on it, in the area of the Caspian Sea Watersheds for 2001-2019. The relationships have been assessed using regression analysis and by calculating the anomalies. The results showed that in the winter there is a positive significant correlation between NDVI and ET, and also LST (R = 0.46 and 0.55, p-value = 0.05, respectively). In this season, the impact of precipitation on vegetation coverage should not be significant when LST is low, as was observed in the analysed case. In spring, the correlation between NDVI and ET and precipitation is positive and significant (R = 0.86 and 0.55, p-value = 0.05). In this season, the main factor controlling vegetation dynamics is precipitation, and LST's impact on vegetation coverage may be omitted when precipitation is much higher than usual. In the summer, the correlation between NDVI and ET is positive and significant (R = 0.70, p-value = 0.05), while the correlation between NDVI and LST is negative and significant (R = –0.45, p-value = 0.05). In this season, the main factor that controls vegetation coverage is LST. In the summer season, when precipitation is much higher than average, the impact of LST on vegetation growth is more pronounced. Also, higher than usual precipitation in the autumn is the reason for extended vegetation coverage in this season, which is mainly due to increased soil moisture.
The authors declare that they have no conflict of interest
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