Assessment of the relationship between drought and vegetation cover using remote sensing

Document Type : Original Article

Authors

1 Associate Professor Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Iran

2 Department of Arid and Mountainous Reclamation Regions, Faculty of Natural Resources, University of Tehran, Tehran, Iran

3 Faculty of Science, School of Biology & Environmental Science, Queensland University of Technology, Australia

10.52547/sustainearth.2022.102539

Abstract

Drought is a natural phenomenon and the occurrence of this phenomenon is likely in all parts of the world. Drought depends on its severity and one of the its affected resources is vegetation and its conditions. This study tries to present the relationship between vegetation cover and drought in northwest of Iran. To assess the effect of drought on vegetation, the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI) were determined during 2002–2016 using remote sensing and several software including ArcGIS 10.3, ENVI, SPSS Statistics 17.0 and Microsoft Excel. At the first the correlations between NDVI and VCI were compared with SPI for each year then they were compared with SPI of the last year using MODIS sensor. Pearson correlation was used to calculate the correlation between the indices. The results showed that the correlations between NDVI and VCI with the SPI of same year are 0.568 and 0.481 respectively, and also the correlations between NDVI and VCI with the SPI of last year are 0.377 and 0.269, respectively. These results demonstrate that the correlations between NDVI and VCI with the SPI of same year are more than the SPI of next year also the correlation between NDVI and SPI in the same year is more than NDVI and SPI of the last year which indicate that the density vegetation of each year is directly related to the amount of rainfall of same year.

Keywords


Article Title [فارسی]

Assessment of the relationship between drought and vegetation cover using remote sensing

Authors [فارسی]

  • Hassan Khosravi 1
  • Pouyan Dehghan Rahimabadi 2
  • Hossein Azarnivand 3
1 Associate Professor Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Iran
2 Department of Arid and Mountainous Reclamation Regions, Faculty of Natural Resources, University of Tehran, Tehran, Iran
3 Faculty of Science, School of Biology & Environmental Science, Queensland University of Technology, Australia
Abstract [فارسی]

Drought is a natural phenomenon and the occurrence of this phenomenon is likely in all parts of the world. Drought depends on its severity and one of the its affected resources is vegetation and its conditions. This study tries to present the relationship between vegetation cover and drought in northwest of Iran. To assess the effect of drought on vegetation, the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI) were determined during 2002–2016 using remote sensing and several software including ArcGIS 10.3, ENVI, SPSS Statistics 17.0 and Microsoft Excel. At the first the correlations between NDVI and VCI were compared with SPI for each year then they were compared with SPI of the last year using MODIS sensor. Pearson correlation was used to calculate the correlation between the indices. The results showed that the correlations between NDVI and VCI with the SPI of same year are 0.568 and 0.481 respectively, and also the correlations between NDVI and VCI with the SPI of last year are 0.377 and 0.269, respectively. These results demonstrate that the correlations between NDVI and VCI with the SPI of same year are more than the SPI of next year also the correlation between NDVI and SPI in the same year is more than NDVI and SPI of the last year which indicate that the density vegetation of each year is directly related to the amount of rainfall of same year.

Keywords [فارسی]

  • Drought Index
  • MODIS
  • Pearson correlation
  • Vegetation