Analysis of relationship between drought and NDVI variations in different vegetation types (Case study: Southern rangelands of Yazd Province)

Document Type : Research Paper

Authors

1 MSc Student of Range Management, College of Natural Resources and Desert, Yazd University, Yazd, Iran

2 Assistant Professor, College of Natural Resources and Desert, Yazd University, Yazd, Iran

3 Assistant Professor, Department of Geography, Yazd University, Yazd, Iran

10.29252/aridbiom.7.2.85

Abstract

Drought can be caused by reducing rainfall and/or increasing temperature. Drought has negative impact on water resources and vegetation, accelerates the desertification. In order to investigate the relationship between annual droughts and vegetation changes in southern part of the Yazd province, meteorological drought indices and remote sensing technology were employed. Firstly, annual drought intensities were determined using SPI and RDI indices. Five interpolation methods have been investigated and compared for drought zoning. In the next step, mean annual and seasonal NDVI were calculated using time series of MODIS images of 2000 to 2014 years. Then, relationship between drought indices and NDVI in 16 vegetation types were determined. According to the results, the drought intensity of the study area during time span of 1999-2000 and 2007-2008 were moderate and very high, respectively. Analyzing of correlation between NDVI and drought indices in different vegetation types indicates a significant correlation between annual, spring, and summer NDVI in most of the vegetation types (P<0.01). Coefficient of determination (R2) between annual variations of NDVI and annual SPI was obtained in Artemisia sieberi-Ebenus stellata, Zygophyllum eurypterum-Artemisia sieberi, Artemisia sieberi - Amygdalus scoparia and Amygdalus scoparia-Acer cineracens-Pistasia atlantica vegetation type with R2 = 0.75, 0,68, 0.65 and 0.63, respectively. So, in these plant types, 75, 68, 65 and 63 percent of variations of the annual NDVI index are in order subject to changes in the SPI drought index. Moreover, depending on ecological condition, species type, life forms, and accompany species; effect of drought on vegetation types is different.

Keywords

References

[1]. Chamaille-jammes, S., & Fritz, H. (2009). Precipitation-NDVI relationships in eastern and southern african savannas vary along a precipitation gradient. International Journal of Remote Sensing, 30 (13): 3409-3422.
[2]. Chenari, M. (2005). Investigation on variation of some drought indices using markov chain in south alborz climate’s samples. Tehran University. M.Sc. Thesis, 159p, (in Farsi).
 [3]. Dutta, D., Kundu, A., Patel, N.R., Saha, S.K., & Siddiqui, A.R. (2015). Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). The Egyptian Journal of Remote Sensing and Space Sciences, 18; 53-63.
[4]. Fatehi marj, A., & Baghernia, M. (2011). Rangeland drought monitoring using MODIS satellite images in west of iran for 2007-2009. Iranian Journal of Watershed Management Science and Engineering, 5(16); 13-22, (in Farsi).
[5]. Frey, R.A., Ackerman, S.A., Liu, Y., Strabala, K.I., Zhang, H., Key, J.R., & Wang, X. (2008). Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for collection 5. Journal of atmospheric and oceanic technology, 25:1057-1072.
[6]. Ghafarian Malamiri, H.R. (2015). Reconstruction of gap-free time series satellite observations of land surface temperature to model spectral soil thermal admittance (Doctoral dissertation), Technische Universiteit Delft, The Netherlands.
[7]. Goldsmith, F.B. (1991). Monitoring for Conservation and Ecology. Chapman and Hall, London. 275p.
[8]. Hadian, F.,  Jafari R.,  Bashari, H., & Soltani, S. (2014). Monitoring the effects of precipitation on vegetation cover changes using remote sensing techniques in 12 years period (Case study: Semirom Isfahan). Journal of Range and Watershed Management, 66 (4): 621-633, (in Farsi).
[9]. Hosseini, S.Z., Kappas, M., & Propastin, P. (2011). Estimating relationship between vegetation dynamic and precipitation in central iran. Toledo, Spain.
[10]. Jagerbrand, A.K., Molau, U., Alatalo, J.M., & Chrimes, D. (2009). Plant community responses to 5 years of simulated climate change in meadow and heath ecosystems at a subarctic-alpine site. Oecologia, 161: 601-610.
[11]. Jahanbakhsh, S., Rezaee Banafshe, M., Esmaeelpour M., & Tadayoni, M. (2012). The evaluation of potential evapotranspiration estimation models and Its spatial distribution in the southern basin of Aras river. Journal of Geogheraphy & Planning, 16 (40): 25-46, (in Farsi).
[12]. Jalali, N., & Khalilpor, A. (2009). Identification of spatial extent of extreme droughts and their impact on forests and rangelands in Iran during 1995-2001 using rainfall data and satellite images. Journal of the Iranian Natural Resources, 61(1): 211-223, (in Farsi).
[13]. Kogan, F.N. (2000). Contribution of remote sensing to drought early warning, National Oceanic and Atmospheric Administration (NOAA), National Environmental satellite Data and laformation serrices (NESDIS), Washigton DC. U.S.A. pp 86-100.
[14]. Liang, E.Y., Shao, X.M., & He, J.C. (2005). Relationships between tree growth and NDVI of grassland in the semiarid grassland of north China. International Journal of Remote Sensing, 26 (13):2901-2908.
[15]. Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25 (12): 2365-2407.
[16]. Mayhew, W. W. (1965). Adaptations of the Amphibian, Scaphiopus couchi, to Desert Conditions, American Midland Naturalist, 74(1): 95-109.
[17]. McKee, T.B., Doesken, N.Y. & Kleist, J.Y. (1993). The relationship of drought frequency and duration to time scales. Eighth Conference on Applied Climatology, American Meteorological Society: Anaheim, CA, 174–184.
[18]. Mesdaghi, M. (2010). Rage management in Iran. Astane ghods publications, Mashhad. 336p, (in Farsi).
[19]. Mirmousavi, S.H., & Karimi, H. (2013). Effect of drought on vegetation cover using MODIS sensing images case: Kurdistan Province. Geography and Development, 11 (31): 57-76, (in Farsi).
[20]. Mukherjee, T., Mukherjee, S., Mukhopadhaya, A., Roy, A.K., & Dutta, S. (2014). Drought monitoring of chhattisgarh using different indices based on remote sensing data. Climate Change and Biodiversity,1: 85-101.
[21]. Peixi, S., Hongbin, C., & Qiaodi, Y. (2008). Plant community characteristics and their relationships with climate in the Hexi Corridor region of northwestern China. Frontiers of Forestry in China, 3(4): 393-400.
[22]. Propastin, P.A., Kappas, M., Erasmi, S., & Muratova, N.R. (2007). Remote sensing based study on intra-annual dynamics of vegetation and climate in drylands of kazakhstan. Basic and Applied Dryland Research, 1(2): 138-154.
[23]. Rezaeimoghadam, M.H., Valizadeh kamran, KH., Rostamizade, H. & Rezaee, A. (2013). Evaluating the adequacy of MODIS in the assessment of drought (Case study: Urmia Lake Basin). Geography and Environmental Sustainability, 2 (5): 37-52, (in Farsi).
[24]. Rhee, J.,  Im, J., & Carbone, G. (2010). Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data, Remote Sensing of Environment, 114 (12): 2875-2887.
[25]. Roerink, G.J., & Menenti, M. & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21: 1911-1917.
[26]. Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In 3rd ERTS Symposium, NASA SP-351 I, 309-317.
[27]. Scanlon, T.M., Albertson, J.D., Caylor, K.K., & Williams, C.A. (2002). Determining land surface fractional cover from NDVI and rainfall time series for a savanna ecosystem. Remote Sensing of Environment, 82: 376-388.
[28]. Shokoohi, A. 2012. Comparison of SPI and RDI in drought analysis at local scale with emphasizing on agricultural drought (Case study: Qazvin and Takestan). Iranian of irrigation water engineering, 3(9): 111-122, (in Farsi).
[29]. Zare khormizie, H., Hosseini, S.Z.,  Mokhtari, M.H., Ghafarian Malamiri, H.R. (2017). Reconstruction of MODIS NDVI time series using harmonic analysis of time series algorithm (HANTS). Spatial Planning. 21(3): 221-255, (in Farsi).  
Journal of Arid Biome
Volume 7, Issue 2
June 2017
Pages 85-101

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