Drought monitoring in unirrigated lands based on the remote sensing technique



Regional vegetation plays an important role in modeling ecosystem changes and conservation. Meteorological drought indices which are directly obtained from such meteorological data as precipitation can't be useful in drought monitoring if those data are absence. Therefore, remote sensing techniques may provide an efficient technique for drought monitoring. In this study, the values of Normalized Difference Vegetation Index (NDVI) changes was investigated in the pasture and rainfed lands of Neishabour watershed during November to May, 2001 to 2010 using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. In order to practice a more precise drought monitoring, standardized precipitation (SPI) and vegetation indexes (VCI) were also computed and drought class determined based on both values. The results showed that the highest correlation coefficients between NDVI and precipitation were obtained for 6-months time step; however the highest increment in correlation coefficients were as transition from one to two month(s) precipitation (increment from 0.068 to 0.547). The lowest and highest mentioned correlation coefficients during 2001 to 2010 were achieved as zero and 0.73 for pastures, zero and 0.71 for rainfeds, respectively. Comparison of SPI and VCI results as for drought classing showed that SPI index cannot exactly describe agricultural drought conditions perfectly. The lowest and highest correlation coefficients between VCI and SPI with time series of 1, 3, 6, 9 and 12 months were obtained as (0-0.23), (0.04-0.8), (0.001-0.66), (0-0.57) and (0.15-0.83), respectively. Analysis of the correlation coefficients between SPI and VCI showed that its lowest value (0.001) was happened for monthly SPI.