Evaluation of the Effect of Drought on the Vegetation of Iran Using Satellite Images and Meteorological Data

Document Type : Original Article


1 Ph.D. Student, Dep. of Geography, Faculty of Literature and Humanities, Lorestan University, Khorram Abad, Lorestan, Iran

2 Assistant prof., Department of Geography, Faculty of Literature and Humanities, Lorestan University, Khorram Abad, Lorestan, Iran

3 Associate Prof., Department of Geography, Faculty of Literature and Humanities, Lorestan University, Khorram Abad, Lorestan, Iran


Introduction: Drought conditions can vary from moderate to severe and have different durations, necessitating continuous and operational monitoring. The longer the drought persists, the more pronounced its impact on vegetation and water resources becomes, and the more severe the drought, the greater the limitation of services for humans and the alteration of natural systems. Habitat destruction for wildlife, reduced water quality, and reduced access to water resources could be consider as most effects of drought. Drought monitoring is essential for researchers, managers, and decision-makers to identify vulnerable areas, which can be used to reduce the consequences of drought.
Material and Methods: In this study, an attempt has been made to investigate the vegetation drought situation in Iran by using Suomi NPP infrared sensor images obtained from the Earth Data website (earthdata.nasa.gov) and using (NDVI), (VCI), (TCI), and (VHI) indices. The study period, spanning from April 1st to July (the 13th to 26th week), was selected as it encompasses the typical drought duration in Iran. The Standard Precipitation Index (SPI) was calculated for Iran using daily precipitation data from 143 synoptic stations. Subsequently, the correlation coefficient was calculated between SPI and each of the indices (NDVI), (VCI), (TCI), and (VHI). In infrared images, M bands have a resolution of 750 meters, while I bands have a resolution of 375 meters.
Results and Discussion: Based on the rainfall data recorded in synoptic meteorological stations, there is minimal rainfall during the summer months (July, August, and September). Conversely, the majority of rainfall occurs during the autumn, winter, and spring seasons. Consequently, the water year in most regions of Iran commences approximately in the third decade of September and continues until the second and third decade of June annually. In this study area, the optimal temporal base for monitoring and estimating drought on the vegetation is from April 1st to June 30th. In this article, the effect of precipitation on vegetation conditions was investigated using the standardized precipitation index (SPI), derived from monthly precipitation data from synoptic meteorological stations. Iran experiences a dry season in summer, with August being the driest month of the year. The temporal and spatial changes in drought for each vegetation indicator are markedly different.
Conclusion: Based on the majority of years experiencing drought, the vegetation cover is expected to face mild or severe drought. This is demonstrated by a decrease in the values of each indicator. In years  that the vegetation was affected by drought, the values of the indices show a decrease in April, followed by an increase in June and July. This suggests the beginning of a severe drought. Based on the calculated SPI, it was determined that the area experiences low precipitation during the hot months, indicating a lower rate compared to other months.


Benedetti, R. & Rossin, P., 1993, On the Use of NDVI Profiles as a Tool for Agricultural Statistics: The Case Study of Wheat Estimate and Forecast in Emilia, Remote Sensing of Environment,
Brown, J.F., Wardlow, B.D., Tadesse, T., Hayes, M.J. & Reed, B.C., 2008, The Vegetation Drought Response Index (VegDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation, GIS Cience Remote Sensing, 45, PP. 16-46.
Brown, J.F., Howard, D., Wylie, B., Frieze, A., Ji, L. & Gacke, C., 2015, Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition, Remote Sensing, 7, PP. 16226-16240. https://doi.org/10.3390/rs71215825.
Boyte, S.P., Wylie, B.K. & Major, D.J., 2015, Mapping and monitoring cheatgrass dieoff in rangelands of the Northern Great Basin, USA, Rangel, Ecol, Manag., 68, PP. 18-28, https://doi.org/10.1016/j.rama.2014.12.005.
Berhan, G., Hill, S., Tadesse, T. & Atnafu, S., 2011, Using Satellite Images for Drought Monitoring: A Knowledge Discovery Approach, J. Strategic Innov. Sustain., 7(1), P. 135, https://www.researchgate.net/publication/260248307_Using_Satellite.
Bhuiyan, C., 2008, Desert Vegetation during Droughts: Response and Sensitivity, Int. Arch. Photogr. Remote Sens. Spatial Inf. Sci., 37(B8), PP. 907-912, https://www.researchgate.net/publication/228452114_DESERT.
Cracknell, A.P., 1997, The Advanced Very High Resolution Radiometer (AVHRR), London: Taylor & Francis, P. 534, https://www.cambridge.org/core/journals/geological-.
Fuchs, B.A., 2021, National Drought Mitigation Center, University of Nebraska-Lincoln, Lincoln, NE, USA.
Holdren, J.P. & Ehrlich, P.R., 1974, Human Population and Global Environment, Am. Sci., 62, PP. 282-292,
Jahangir, M. & Mashidi, D., 2019, Evaluation of Agricultural Drought Monitoring Based on Remote Sensing Using the Standardized Rainfall Index in the Growing Months (Case Study: Karun Bozor Watershed), Iran Irrigation and Drainage Journal, 14(4), PP. 1252-1264.
Jenkerson, C.B., Maiersperger, T. & Schmidt, G., 2010, eMODIS, A User-Friendly Data Source, U.S. Geological Survey Open-File Report 2010-1055, U.S. Geological Survey EROS Center: Sioux Falls, SD, USA., P. 10. https://pubs.usgs.gov/of/2010/1055/pdf/OF2010-1055.pdf.
Ji, L., Gallo, K., Eidenshink, J.C. & Dwyer, J., 2008, Agreement Evaluation of AVHRR and MODIS 16-Day Composite NDVI Data Sets, Int. J. Remote Sensing of Environment, 29, PP. 4839-4861,
JPSS, 2014, Joint Polar Satellite System, cited 2014, https://doi.org/10.1002/2013JD020389.
Justice, C.O., Roman, M.O., Csiszar, I., Vermote, E.F., Wolfe, R.E., Hook, S.J., Friedl, M., Wang, Z., Schaaf, C.B. & Miura, T., 2013, Land and Cryosphere Products from Suomi NPP VIIRS: Overview and Status, J. Geophys. Res. Atmos., 118, PP. 9753-9765.
Kidwell, KB., 1990, Global Vegetation Index User’s Guide, Washington (DC): US Department of Commerce. (NOAA, 38), https://doi.org/10.1016/0273-1177(95)00079-T.
Kogan F.N., 1995, Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data, Bull. Am. Meteorol. Soc., 76, PP. 655-667, https://www.jstor.org/stable/26232390.
Kogan, FN., 1997, Global Drought Watch from Space, Bull. Am. Meteorol. Soc., 78, PP. 621-636, https://doi.org/10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2.
Kogan, F.N., 2001a, Contribution of Remote Sensing to Drought Early Warning. National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite Data and Information Services (NESDIS), Washington: DC. U.S.A. https://www.researchgate.net/publication/253598539.
Kogan, FN., 2001b, Operational Space Technology for Global Vegetation Assessment, Bull. Am. Meteorol. Soc., 82, PP. 1949-1964, https://www.star.nesdis.noaa.gov/data/smcd1/vhp/VH_doc/Felix/2001_OperationalSpaceTech4GlobalVegetation.pdf.
Kogan, F.N. & Guo, W., 2016, Early Twenty-First-Century Droughts during the Warmest Climate, Geomatics Nat. Hazards Risk, 7, P. 127-137,
Kogan, F.N., Goldberg, M., Schott, T. & Guo, W., 2015, Suomi NPP/VIIRS: Improving Drought Watch, Crop Loss Prediction, and Food Security, Int. J. Remote Sensing of Environment, 36, PP. 5373-5383, http://dx.doi.org/10.1080/01431161.2015.1095370.
Krzanowski, W.J., 1998, Selection of Variables to Preserve Multivariate Data Structure, Using Principal Components, Journal of the Royal Statistical Society, 36(1),
Littell, J.S., Peterson, D.L., Riley, K.L., Liu, Y. & Luce, C.H. (Eds.) Fire and Drought, U.S. Department of Agriculture, Forest Service, Washington Office: Washington, DC, USA. http://www.ncforestservice.gov/Managing_your_forest/pdf/EffectsDroughtForestsRangelands.pdf.
McKee, T.B., Doesken, N.J. & Kliest, J., 1995, Drought Monitoring with Multiple Time Scales, In Proceedings of the 9th Conference of Applied Climatology, 15-20 January, Dallas TX. American Meteorological Society, Boston, MA. PP. 233-236.
Qermwz Cheshme, B., Hosseini., M.Gh., Hosseini, T. & Sherafati, A., 2019, Evaluation of the Relationship between Meteorological Drought and Vegetation Cover of Rainfed Lands in Lorestan Province, Watershed Researches, 34(2), PP. 77-90, https://doi.org 10.22092/wmej.2020.342647.1332.
Rezai Banafsheh, M., Rezaei, A. & Faridpour, M., 2014, Agricultural Drought Analysis of East Azerbaijan Province with Emphasis on Remote Sensing and Vegetation Status Index, Danesh Water and Soil Science, 25(1), PP. 113-123. 13. https://water-soil.tabrizu.ac.ir/article_3509.html.
Roger, J.C., Vermote, E.F., Devadiga, S. & Ray, J.P., 2020, Suomi-NPP VIIRS Surface Reflectance User’s Guide,
Roswintiarti, O., Oarwati, S. & Angraini, N., 2010, Potential Drought Monitoring over Agriculture Area in Java Island, Indonesia. Indonesian National Institute of Aeronautics and Space (LAPAN), Progress Report of SAFE Prototype Year.
Su, Z.B., Yacob, A., Wen, J., Roerink, G., He, Y.B, Gao, B.H., Boogaard, H. & van Diepen, C., 2003, Assessing Relative Soil Moisture with Remote Sensing Data: Theory, Experimental Validation, and Application to Drought Monitoring over the North China Plain, Physics and Chemistry of the Earth, 28(1-3), https://doi 10.1016/S1474-7065(03)00010-X.
Svoboda, M., LeComte, D., Hayes, M., Heim, R., Gleason, K., Angel, J., Rippey, B., Tinker, R., Palecki, M. & Stooksbury, D., 2002, The Drought Monitor, Bull. Am. Meteorol. Soc., 83, PP. 1181-1190, https://doi.org/10.1175/1520-0477-83.8.1181.
Tadesse, T., Demisse, G.B., Zaitchik, B. & Dinku, T., 2014, Satellite-Based Hybrid Drought Monitoring Tool for Prediction of Vegetation Condition in Eastern Africa: A Case Study for Ethiopia, Water Resour. Res., 50, PP. 2176-2190.
Vermote, E., Franch, B. & Claverie, M., VIIRS, NPP Surface Reflectance 8-Day L3 Global 500 m SIN Grid, V001,
Vicente-Serrano, S.M., Cuadrat-Prats, J.M. & Romo, A., 2006, Early Prediction of Crop Production Using Drought Indices at Different Time-Scales and Remote Sensing Data: Application in the Ebro Valley (North-East Spain), International Journal of Remotr Sensing, 27(3).
Wang, D., Morton, D., Masek, J., Wu, A., Nagol, J., Xiong, X., Levy, R., Vermote, E. & Wolfe, R., 2012, Impact of Sensor Degradation on the MODIS NDVI Time Series, Remote Sensing of Environment, 119, PP. 55-61.
Wang, X., Li, Y., Wang, X., Li, Y., Lian, J. & Gong, X., 2015, Temporal and Spatial Variations in NDVI and Analysis of the Driving Factors in the Desertified Areas of Northern China From 1998 to 2015, Front. Environ. Sci., 9, P. 633020,
Wylie, B.K., Zhang, L., Bliss, N., Ji, L., Tieszen, L.L. & Jolly, W.M., 2008, Integrating Modelling and Remote Sensing to Identify Ecosystem Performance Anomalies in the Boreal Forest, Yukon River Basin, Alaska, Int. J. Digit. Earth, 1, PP. 196-220.
Zeng, L., Wardlow, B.D., Xiang, D., Hu, S. & Li, D., 2020, A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data, Remote Sens. Environ., 237, P. 111511, https://doi: 10.1016/j.rse.2019.111511.
Zhang, X., Liu, L., Liu, Y., Jayavelu, S., Wang, J., Moon, M., Henebry, G.M., Friedl, M.A. & Schaaf, C.B., 2018, Generation and Evaluation of the VIIRS Land Surface Phenology Product, Remote Sensing of Environment, 216, PP. 212-229,
Zhu, X. & Liu, D., 2015, Improving Forest Aboveground Biomass Estimation Using Seasonal Landsat NDVI Time-Series, ISPRS J. Photogramm. Remote Sensing of Environment, 102, PP. 222-231,