Investigating the Trend of Drought Changes with Temperature-Vegetation Dryness Index (TVDI) and Its Relationship with Atmospheric Factors (Case Study: Siah Kooh Watershed)

Document Type : Applied Article

Authors

• A. Safdari Molan ¹
• A. Mardaneh ²

¹ Ph.D. in Geography and Urban Planning, Department of Human Geography, Faculty of Geography, University of Tehran, Tehran, Iran

² M.Sc. in Remote Sensing and GIS, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

10.22067/jwsd.V10I3.2305-1243

Abstract

Statistics show that the occurrence of severe and long-term droughts, especially in sensitive and fragile areas of the country has caused severe economic and social losses, and the occurrence of drought, increased dust, storms, and desertification has caused a decrease in agricultural production. Extensive and dynamic monitoring of dryness by traditional methods is very difficult and costly due to the lack of soil moisture display points. Remote sensing technology is a practical and applied method for large-scale land monitoring. In this study, we tried to identify the drought in the Siah Kooh watershed area with TVDI dryness-temperature indices and the NDVI index resulting from MODIS sensor images and to investigate the relationship between drought and atmospheric elements in the region. The results of correlation as a total showed that the correlation values of TVDI index SPI6 and SPI12 are 0.68 and 0.71, respectively, and the correlation rates of NDVI values with SPI6 and SPI12 are 0.49 and 0.51, respectively. As a result, it can be said that the TVDI index, due to the use of thermal and reflective bands and soil moisture, is more accurate than the NDVI index, which considers only the amount of vegetation in the region. The TVDI index had an inverse correlation with the average of two months of rainfall of 0.54 and a direct correlation of 0.64 with the surface temperature of the earth. In contrast, the NDVI vegetation index with a two-month average rainfall has a direct correlation of 0.54 and an inverse correlation of 0.6 with the temperature. These linkages show that the correlation between vegetation and temperature is inverse (negative) and the correlation between vegetation and rainfall is direct (positive).

Keywords

• Drought
• Remote Sensing
• Vegetation
• Temperature and Precipitation
• TVDI

Main Subjects

• Water, Ecosystem and Environment

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