Maedeh Behifar; a.a Kakroodi; Majid Kiavarz; Farshad Amiraslani
Abstract
Drought is one of the most important natural disasters in the country, with devastating environmental and economic effects. Most drought studies have focused on drought severity and other drought characteristics have not been usually investigated. In this research, for the first time, the capability ...
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Drought is one of the most important natural disasters in the country, with devastating environmental and economic effects. Most drought studies have focused on drought severity and other drought characteristics have not been usually investigated. In this research, for the first time, the capability of meteorological drought indices and satellite data are combined and applied to study drought in inland and coastal basins. For this purpose, the SPI index was calculated by using TRMM satellite precipitation products and then, the drought characteristics such as severity, duration, magnitude, and extent were spatially studied. The results showed that the correlation coefficient between the SPI calculated from the image and the station data was 0.94. The maximum intensity of drought in the study area was -4.19 which occurred in December 2010. Furthermore, the frequency of extreme droughts in 6- and 12-months timescales was higher in the inland area compared with the coastal area. Moreover, in the six-month timescales, 60 percent of drought events had a magnitude of -18.3 or less. The results showed that it is possible to obtain the extent of drought by using satellite imagery which cannot be calculated by other methods. Besides, by using satellite images, drought characteristics could be studied spatially at the basin scale, which is not possible by traditional methods. The results showed the advantage of using satellite precipitation images in the drought study
Ali Sadeghi; Ali Darvishi Boloorani; ataolah abdolahi kakroodi; seyed kazem Alaipana; Saeid Hamzeh
Abstract
The presence of dry and green vegetation in pixels containing spectral information is essential in geological and mineralogical studies. Thus, retrieving sub-pixel information, including estimation of a mineral’s quantity in a single hyperspectral RS image pixel is very important. In this study, ...
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The presence of dry and green vegetation in pixels containing spectral information is essential in geological and mineralogical studies. Thus, retrieving sub-pixel information, including estimation of a mineral’s quantity in a single hyperspectral RS image pixel is very important. In this study, the vegetation corrected continuum depth (VCCD) method was trained and its results were validated using spectrometry, laboratory mineralogy, and Hyperion image to reduce the effect of vegetation on the estimation of minerals. The study was conducted in Oghlansar region located in northwestern Iran. SAVI and absorption depth (2102 μm) were used for the estimation of the green and dry vegetation, respectively. Meanwhile, the trained models do not have a high sensitivity to the presence of noise in the spectrum and vegetation type changes. The correction of continuum removed band depth (CRBD) analysis was possible up to 60% for maximum green vegetation cover threshold, 56-60% for dry vegetation, and 72-76% for both dry and green vegetation. Effect of noise and different vegetation types on model capability was examined and the result shows that VCCD is not highly sensitive to random noise and changes in vegetation types. After correction of the coefficients and confirmation of its efficiency, the model was used to correct CRBD and reduce the effect of vegetation on Hyperion image. In the estimation of kaolinite and muscovite, the presence of green and dry vegetation led to the underestimation of the minerals present in the study area. The results showed that VCCD was able to increase the prediction accuracy (R2) by 0.25 and 0.13 and reduce RMSE by 0.0108 and 0.125 for kaolinite and muscovite, respectively.
