Abstract. Recent developments in the image processing approaches and the availability of multi and/or hyper spectral remote sensing data with high spectral, spatial and temporal resolutions have made remote sensing technique of great interest in investigations of geological sciences. One of the biggest advantage of the application of remote sensing in geology is recognizing the type of unknown rocks and minerals. In this study, an investigation on spectral features of carbonate rocks (i.e. calcite, dolomite, and dolomitized calcite) were done in terms of main absorptions, the reasons of those absorptions and comparison of these absorption with Johns Hopkins University (JHU) spectral library and laboratory spectra of Analytical Spectral Devices (ASD) instrument. For this purpose, we used the VNIR and SWIR bands of ASTER and OLI datasets. Finally, we applied the Spectral Analyst Algorithm in order to comparison between the obtained spectra from ASTER dataset and carbonate spectra of JHU spectral library.

In geological remote sensing approaches for estimating evapotranspiration, METRIC method is among the most modern and precise approaches that many positive experiments have been reported regarding its applicability. In this study, the value of actual evapotranspiration (ET) occurred in Marvdasht farmlands, Fars province, Iran, was calculated instantaneously (hourly) and daily (24 hours), at 24 April 2017 using METRIC method and Lansat-8 data. In order to accuracy assessment of the results, estimated values were compared to the values calculated with Penman Monteith method in places that covers by alfafa crop. Our observations showed that ET values obtained via METRIC model on average, have 0.51mm difference in estimations, comparing to the Penman Manteith method.

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One of the serious dangers which threatens human communities especially those who are living in mountainous areas is the occurrence of landslides. Therefore, determination of the areas with potential for landslide events is very important for avoiding establishment of residential areas or industrial facilities. The aim of this study is to provide a landslide potential map in Sheshpeer sub-catchment, Iran, using an integration of remote sensing (RS) techniques and geographical information systems (GIS). Compared with the traditional approaches, these techniques are very fast, inexpensive and trustworthy in landslide mapping. For this purpose, we collected and produced seven data layers using GIS and RS, and then Analytic Hierarchy Process (AHP) method was applied for data analysis. Our results showed that among the twelve pervious landslide events in this area, nine of them are located in the regions with very high potentiality and the others are in highly potential regions for landslides occurrence.

Hitherto there have been many studies comparing the usefulness of OLI and ETM+ sensors for linear feature extraction. However, not too much attention has been paid to the differences in the bandwidth of the two sensors. In this study, the suitability of Landsat ETM+ and OLI sensors for automatic detection of linear features by LINE algorithm was compared. In this study, eight regions in northern, central and southern parts of Iran were selected based on the diversity of lithology, the pristine status, and lack of human activities for the comparison of the two datasets. Results revealed that LINE algorithm performed better on the images with higher standard deviation. The ETM+ datasets are more suitable for linear feature extraction because ETM+ panchromatic band and first principal component analysis image (PC1 image) of ETM+ datasets have higher standard deviation compared to OLI datasets.