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

آب و توسعه پایدار

Article 9, Volume 10, Issue 3 - Serial Number 29, December 2023, Pages 99-108   

Document Type: Applied Article

DOI: 10.22067/jwsd.V10I3.2305-1243

Authors

A. Safdari Molan^{* 1}; A. Mardaneh²

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

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

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

References

هارپال اس، ماوی، و گرائم جی، تاپر. (1388). آب و هواشناسی کشاورزی اصول و کاربرد مطالعات آب و هوا در کشاورزی. مترجم: حسین محمدی. انتشارات دانشگاه تهران. چاپ اول. تهران. ایران.
نوری، سمیرا، و ثنایی‌نژاد، سیدحسین. (1392). بررسی خشکسالی با استفاده از شاخص‌های خشکی دما-گیاه (TVDI) و دما-گیاه اصلاح شده (MTVDI) و تصاویر سنجنده مودیس. نشریه آب و خاک، 27(4)، 753- 762. doi: 10.22067/jsw.v0i0.28135
Alavipanah, S. K. (2001). Performance evaluation of Landsat TM satellite spectral bands in desert studies in Iran. Journal of Natural Resources, 53 (1),67-78.
Alavipanah, S. K., De Dapper, M., Goossens, R., & Massoudi, M. (2001). The use of TM thermal band for land cover/land use mapping in two different environmental conditions of Iran. Journal of Agricultural Science and Technology, 3(1), 27-36. DOR: 20.1001.1.16807073.2001.3.1.2.5
Chopra, P. (2006). Drought risk assessment using remote sensing and GIS: a case study of Gujarat. ME thesis, Indian Institute of Remote Sensing, Dehradun & International Institute for Geo-information & Earth Observation, the Netherlands.
Fensholt, R., Rasmussen, K., Nielsen, T. T., & Mbow, C. (2009). Evaluation of earth observation based long term vegetation trends—Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sensing of Environment, 113(9), 1886-1898. https://doi.org/10.1016/j.rse.2009.04.004
Gao, Z., Gao, W., & Chang, N. B. (2011). Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. International Journal of Applied Earth Observation and Geoinformation, 13(3), 495-503. https://doi.org/10.1016/j.jag.2010.10.005
Ghulam, A., Qin, Q., Teyip, T., & Li, Z. L. (2007). Modified perpendicular drought index (MPDI): a real-time drought monitoring method. ISPRS Journal of Photogrammetry and Remote Sensing, 62(2), 150-164. https://doi.org/10.1016/j.isprsjprs.2007.03.002
McKee, T.B., Doesken, N.J. and Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. 8th Conference on Applied Climatology, Anaheim, 17-22 January 1993, 179-184.
Patel, N. R., Anapashsha, R., Kumar, S., Saha, S. K., & Dadhwal, V. K. (2009). Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status. International Journal of Remote Sensing, 30(1), 23-39. https://doi.org/10.1080/01431160802108497
Rahimzadeh, B., P., Omasa, K., & Shimizu, Y. (2012). Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 1-12. https://doi.org/10.1016/j.isprsjprs.2011.10.009
Safdari Molan, A., Ziari, K., Pourahmad, A., & Hataminejad, H. (2019). Providing a Livable Housing Development Model for Increasing Urban Livability (Case Study of Tehran). In Computational Science and Its Applications–ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part III 19 (pp. 660-674). https://doi.org/10.1007/978-3-030-24302-9_47
Spinoni, J., Marinho Ferreira Barbosa, P., Cherlet, M., Forzieri, G., Mccormick, N., Naumann, G., Vogt, J. and Dosio, A. (2021). How will the progressive global increase of arid areas affect population and land-use in the 21st century. Global and Planetary Change, 205, 103597. https://doi.org/10.1016/j.gloplacha.2021.103597 
Tamir K.; William R. L. A. (2021). A vast increase in heat exposure in the 21st century is driven by global warming and urban population growth. Sustainable Cities and Society, 73, 103098. https://doi.org/10.1016/j.scs.2021.103098
Wan, Z., Wang, P., & Li, X. (2004). Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. International Journal of Remote Sensing, 25(1), 61-72. https://doi.org/10.1080/0143116031000115328
Xu, P., Zhou, T., Zhao, X., Luo, H., Gao,S., Li,Z., Cao,L. (2018). Diverse responses of different structured forest to drought in Southwest China through remotely sensed data. International Journal of Applied Earth Observations and Geoinformation, 69, 217-225. https://doi.org/10.1016/j.jag.2018.03.009 
Yamani, M., Mazidi, A. (2008). The Investigation of changes in surface and vegetation of Shaikh desert using remote sensing data. Quarterly Journal of Geographical Research, 40(64), 1-12.
Yao, C., Zhang, Z. and Wang, X. (2004). Evaluating Soil Moisture Status in XinJiang Using the Temperature Vegetation Dryness Index(TVDI). Remote Sensing Technology and Application, 6, 473-478.
Yuan, X. Y., Chao, D. Y., Gao, J. P., Zhu, M. Z., Shi, M., & Lin, H. X. (2009). A previously unknown zinc finger protein, DST, regulates drought and salt tolerance in rice via stomatal aperture control. Genes & Development, 23(15), 1805-1817. https://doi.org/10.1101/gad.1812409