An Investigation of Remote Sensing Vegetation Indices Ability in Drought Condition Prediction in Iran

Document Type : علمی - پژوهشی

Authors

1 M.Sc. of Geodesy and Geomatics Engineering Faculty, K.N.Toosi University of Technology

2 Professor, Dep. of Photogrammetry and Remote Sensing, College of Geodesy and Geomatics, K. N. Toosi University of Technology

3 Assistant prof., Dep. of Photogrammetry and Remote Sensing, College of Geodesy and Geomatics, K. N. Toosi University of Technology

4 M.Sc. of Remote Sensing and Member of Remote Sensing Centre, ISA, Tehran, Iran

Abstract

Iran as one of the countrieslocated in arid and semi-arid regions of the world, has been in drought danger. Shortage information about long-term weather conditions in many regions of the country, is one of the most important problems in drought monitoring. In this article, spectral vegetation indices (SVIs) have been employed in order to drought modeling and its forecast. To this end, SPI drought indicator (standardized precipitation index) used to represent period of drought and its intensity. Some broad band spectral vegetation indices including Normalized Difference Vegetation Index (NDVI), Temperature Condition Index (TCI) and Vegetation Condition Index (VCI) were extracted by using NOAA-AVHRR satellite imagery. These indices entered to SVM classifier model to gain the SPI index as its result. After comparing the results, TCI was diagnosed as the best index to predict drought condition via 3 months SPI (trimester SPI).

Keywords

References

  1. پرویز، ل.، خیاط خلقی، م.، ولی‌زاده، خ.، عراقی‌نژاد، ش.، و ایران‌نژاد، پ.، 1390، بررسی کارآیی شاخص‌های منتج از فناوری سنجش ‌از دور در ارزیابی خشکسالی هواشناسی، مطالعۀ موردی: حوضۀ آبریز سفیدرود، جغرافیا و توسعه، دورل 9، شمارۀ 22، صص. 164-147.
  2. Anayamba, A.T. & Eastma Jr., C.J., 2001, NDVI Anomaly Patterns over Africa during the 1997/98 ENSO Warm Event, Int’l J. Remote Sensing, Vol. 22, No. 10, PP. 1847-1859.
  3. Bhuiyan, C., Singh, R.P. & Kogan, F.N., 2006, Monitoring Drought Dynamics in the Aravalli Region (India) Using Different Indices Based on Ground and Remote Sensing Data, Int’l J. Applied Earth Observation and Geoinformation, Vol. 8, PP. 289-302.
  4. Edwards, D.C., 1997, Characteristics of 20th Century Drought in the United States at Multiple Time Scales, Master thesis, Colorado State Univ..
  5. Hopfner, C. & Scherer, D., 2011 , Analysis of Vegetation and Land Cover Dynamics in North-Western Morocco during the Last Decade Using Modis NDVI Time Series Data, Biogeosciences, Vol. 8, Issue 11, PP. 3359-3373.
  6. Jain, S.K., Keshri, R., Goswami, A. & Sarkar, A., 2010, Application of Meteorological and Vegetation Indices for Evaluation of Drought Impact: A Case Study for Rajasthan, India, Natural Hazards, Vol. 54, Issue 3, PP. 643-656.
  7. Jalili, M., Gharibshah, J., Ghavami, S.M., Beheshtifar, M. & Farshi, R., 2014, Nationwide Prediction of Drought Conditions in Iran Based on Remote Sensing Data, IEEE Transaction on Computers, Vol. 63, No. 1.
  8. Ji, L. & Peters, A.J., 2003, Assessing Vegetation Response to Drought in the Northern Great Plains Using Vegetation and Drought Indices, Remote Sensing of Environment, Vol. 87, Issue 1, PP. 85-98.
  9. Kinyanjui, M.J., 2011, NDVI-Based Vegetation Monitoring in Mau Forest Complex, Kenya, African J. Ecology, Vol. 49, Issue 2, PP. 165-174.
  10. Kogan, F.N., 1995, Application of Vegetation Index and Brightness Temperature for Drought Detection, Advances Space Research, Vol. 15, Issue 11, PP. 91-100.
  11. _________, 2001, Operational Space Technology for Global Vegetation Assessment, Bull. Am. Meteorological Soc., Vol. 82, PP. 1949-1964.
  12. Li, B. & Tao, S., 2002, Relations between AVHRR NDVI and Ecoclimatic Parameters in China, Int’l J. Remote Sensing, Vol. 23, PP. 989-999.
  13. Lotsch, A., Friedl, M.A., Anderson, B.T. & Tucker, C.J., 2003, Coupled Vegetation- Precipitation Variability Observed from Satellite and Climate Records, Geophysical Research Letters, Vol. 30, Issue 14, PP. 1774-1777.
  14. McKee, T.B., Doesken, N.J. & Kliest, J., 1993, The Relationship of Drought Frequency and Duration to Time Scales, preprints Eighth Conference on Applied Climatology, PP. 179-184.
  15. Palmer, W.C., 1965, Meteorological Drought, US Dept. of Com- merce, Weather Bureau, Research Paper No. 45, Washington DC.
  16. Quiring, S.M. & Ganesh, S., 2010, Evaluating the Utility of the Vegetation Condition Index (VCI) for Monitoring Meteorological Drought in Texas, Agricultural and Forest Meteorology, Vol. 150, No. 3, PP. 330-339.
  17. Raziei, A.P. & Saghafian, B., 2005, Annual Rainfall Trend in Arid & Semi-Arid Regions of Iran, Proc. 21st European Regional Conf., PP. 20-28.
  18. Rulinda, C.M., Dilo, A., Bijker, W. & Steina, A., 2012, Characterising and Quantifying Vegetative Drought in East Africa Using Fuzzy Modelling and NDVI Data, J. Arid Environments, Vol. 78, PP. 169- 178.
  19. Sadeghi, A.R., Kamgar-Haghighi, A.A., Sepaskahah, A.R., Khalili, D. & Zand-Parsa, S., 2002, Regional Classification for Dryland Agriculture in Southern Iran, J. Arid Environments, Vol. 50, No. 2, PP. 333-341.
  20. Song, X., Saito, G., Kodama, M. & Sawada, H., 2004, Early Detection System of Drought in East Asia Using NDVI from NOAA AVHRR Data, Int’l J. Remote Sensing, Vol. 25, PP. 3105-3111.
  21. Tucker, C.J., 1979, Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, Vol. 8, Issue 2, PP. 127-150.
  22. Wilhite, D.A. & Glantz, M.H., 1985, Understanding the Drought Phenomenon, The role of definitions, Water International, Vol. 10, No. 3, PP. 111-120.
Iranian Journal of Remote Sensing & GIS
Volume 8, Issue 2 - Serial Number 2
November 2016
Pages 101-112

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