Assessing spatial variations of vegetative drought in Razavi-Khorasan province, northeast of Iran
الموضوعات :Ali Bagherzadeh 1 , Reza Mahjoubin 2 , Ehsan Afshar 3 , Ali Bakhshi 4
1 - Associate Professor, Department of Agriculture, Mashhad Branch, Islamic Azad University, P.O Box: 91735-413, Mashhad, Iran
2 - M.Sc., Department of Agriculture, Mashhad Branch, Islamic Azad University, P.O Box: 91735-413, Mashhad, Iran
3 - Ph.D., Department of Agriculture, Mashhad Branch, Islamic Azad University, P.O Box: 91735-413, Mashhad, Iran
4 - Assistant Professor, Department of Agriculture, Mashhad Branch, Islamic Azad University, P.O Box: 91735-413, Mashhad, Iran
الکلمات المفتاحية: TCI, NDVI, LST, VHI, VCI,
ملخص المقالة :
Background and objective: Information concerning the spatial and temporal characteristics of vegetative drought is essential for decision-making in environmental and agricultural practices. The present study is a comprehensive Spatio-temporal analysis of vegetative drought over thirty years of observations.Materials and Methods: The data obtained from NOAA/AVHRR (National Oceanic and Atmospheric Administration/ Advanced Very High-Resolution Radiometer) to reveal the vegetative drought patterns across Khorasan-e-Razavi province, northeast of Iran from 1990 to 2019. Three satellite-based drought indices including the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation health index (VHI) as well as NDVI were used to characterize the dynamics of drought severity conditions and their fluctuations in the study area.Results and conclusion: It was found a strong correlation between land surface temperature (LST), and TCI with VHI which indicates a definite influence of thermal stress on vegetation health in the study area. The analysis of Pearson (R), and the correlation between vegetative drought indices over the 1990–2019 period in Khorasan-e-Razavi province revealed no significant differences among drought indices except P-values. Analyzing long-term drought indices in the study area showed high thermal stress, very poor vegetation condition, and mainly weak VHI in most years of the study. Results from this study highlight the potential for including satellite-based drought indices in agricultural decision support systems (e.g. agricultural drought early warning systems, crop yield forecasting models, or water resource management tools) complementing meteorological drought indices derived from precipitation grids.
Bento, V.A., Gouveia, C.M., DaCamara, C.C., & Trigo, I.F. (2018). A climatological assessment of drought impact on vegetation health index. Agric For Meteorol, 259, 286–295. https://doi.org/10.1016/j.agrformet.2018.05.014
Bhuiyan, C., Saha, A.K., Bandyopadhyay N., & Kogan, F.N. (2017). Analyzing the impact of thermal stress on vegetation health and agricultural drought – a case study from Gujarat, India. GIScience & Remote Sensing, https://doi.org/10.1080/15481603.2017.1309737
Bhuiyan, C. (2008). Desert Vegetation during Droughts: Response and Sensitivity. In: Proceedings of the XXIth ISPRS Congress, International Society for Photogrammetry and Remote Sensing, Beijing, July 2008.
Bhuiyan. C., & Kogan, F.N. (2010). “Monsoon Variation and Vegetative Drought Patterns in the Luni Basin under Rain-Shadow Zone.” International Journal of Remote Sensing, 31(12):3223–3242. https://doi.org/10.1080/01431160903159332
Bhuiyan, C., Singh, R.P., & Kogan, F.N. (2006). “Monitoring Drought Dynamics in the Aravalli Terrain (India) Using Different Indices Based on Ground and Remote Sensing Data.” International Journal of Applied Earth Observation and Geoinformation, 8:289–303. https://doi.org/10.1016/j.jag.2006.03.002
Dehestani Ardakani, M. R. (2021). Dust time series analysis using long-term monthly images of MERRA2 satellites and Sentinel5 images in Google Earth Engine. Journal of Nature and Spatial Sciences (JONASS), 1(2), 16-26.
Dracup, J.A., Lee, K.S., & Paulson, Jr.E.G. (1980). On the Definition of Droughts. Water Resources Research, 16(2): 297-302. https://doi.org/10.1029/WR016i002p00297
García-León, D., Contreras, S., & Hunink, J. (2019). Comparison of meteorological and satellite-based drought indices as yield predictors of Spanish cereals. Agricultural Water Management, 213: 388-396. https://doi.org/10.1016/j.agwat.2018.10.030
Ghane Ezabadi, N., Azhdar, S., & Jamali, A. A. (2021). Analysis of dust changes using satellite images in Giovanni NASA and Sentinel in Google Earth Engine in western Iran. Journal of Nature and Spatial Sciences (JONASS), 1(1), 17-26.
Gidey, E., Dikinya, O., Sebego R., Segosebe, E., & Zenebe, A. (2018). Analysis of the long‑term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia. Environ Syst Res, 7:13. https://doi.org/10.1186/s40068-018-0115-z
Gu, Y., Brown, J.F., Verdin, J.P., & Wardlow, B. (2007). A five-years analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters 34:L06407, https://doi.org/10.1029/2006GL029127
Hadebe, S.T., Modi, A.T., & Mabhaudhi, T. (2017). Drought tolerance and water use of cereal crops: a focus on Sorghum as a food security crop in Sub-Saharan Africa. J. Agron.Crop. Sci., 203 (3), 177–191. https://doi.org/10.1111/jac.12191
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, 87:85-98. http://dx.doi.org/10.1016/S0034-4257(03)00174-3
Kogan, F.N. (1990). Remote sensing of weather impacts on vegetation in non-homogeneous areas. Int. J. Remote Sensing, 11(80):1405-1419. https://doi.org/10.1080/01431169008955102
Kogan, F.N. (1995). Application of vegetation index and brightness temperature for drought detection. Advance in Space Research, 15(11): 91-100. https://doi.org/10.1016/0273-1177(95)00079-T
Kogan, F.N. (1997). Global drought watch from space. Bulletin of American Meteorological Society, 78: 621– 636. https://doi.org/10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2
Kogan, F.N. (2001). Operational Space Technology for Global Vegetation Assessment. Bull. Amer. Meteor. Soc., 82(9): 1949-1964. http://dx.doi.org/10.1175/1520-0477(2001)082<1949:OSTFGV>2.3.CO;2
Kogan, F.N. (2002). World Droughts in the New Millennium from AVHRR-based Vegetation Health Indices. Eos Transactions Amer Geophy Union, 83(48): 562-563. https://doi.org/10.1029/2002EO000382
Kogan, F.N., Gitelson, A., Edige, Z., Spivak, l., & Lebed, L. (2003). AVHRR-Based Spectral Vegetation Index for Quantitative Assessment of Vegetation State and Productivity: Calibration and Validation. Photogrammetric Engineering & Remote Sensing, 69(8) pp 899-906. https://doi.org/10.14358/PERS.69.8.899
Kogan, F.N., Wei, B.G., Zhiyuan, Yang. P., & Xianfeng, J. (2005). Modeling corn production in China using AVHRR-based vegetation health indices. International Journal of Remote Sensing, 26(11): 2325–2336. https://doi.org/10.1080/01431160500034235
Kogan, F.N., Salazar, L., & Roytman, L. (2012). “Forecasting Crop Production Using Satellite Based Vegetation Health Indices in Kansas, United States.” International Journal of Remote Sensing, 3:2798–2814. https://doi.org/10.1080/01431161.2011.621464.
Lesk, C., Rowhani, P., & Ramankutty, N. (2016). Influence of extreme weather disasters on global crop production. Nature, 529, 84–87. http://dx.doi.org/10.1038/nature16467
Mishra, A.K., & Singh, V.P. (2010). A review of drought concepts. J Hydrol, 391, 202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012
Mishra, A.K., Ines, A.V.M., Das, N.N., Khedun, C.P., Singh, V.P., Sivakumar, B., & Hansen, J.W. (2015). Anatomy of a local-scale drought: Application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study. Journal of Hydrology, 526:15-29. https://doi.org/10.1016/j.jhydrol.2014.10.038
NOAA STAR (2016). Global vegetation health products.
Quiring, S.M., & Ganesh, S. (2010). Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas. Agric For Meteorol, 150 (3): 330–339. http://dx.doi.org/10.1016/j.agrformet.2009.11.015
Rhee, J., Im, J., & Carbone, G.J. (2010). Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment, 114:2875-87. https://doi.org/10.1016/j.rse.2010.07.005
Singh, R.P., Roy, S., & Kogan, F.N. (2003). Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. Int J Remote Sens, 24: 4393–4402. https://doi.org/10.1080/0143116031000084323
Zarei, M., Tazeh, M., Moosavi, V., & Kalantari, S. (2021). Evaluating the changes in Gavkhuni Wetland using MODIS satellite images in 2000-2016. Journal of Nature and Spatial Sciences (JONASS), 1(1), 27-41.
Zargar, A., Sadiq, R., Naser, B., & Khan, F.I. (2011). A review of drought indices. Environ Rev, 19: 333–349. https://doi.org/10.1139/a11-013