Geological studies indicate that the southeastern Sanandaj-Sirjan Zone, located in the southeastern Zagros Orogenic Belt, is subdivided transversally into the EsfahanSirjan Block with typical Central Iranian stratigraphic features and the ShahrekordDehsard Terrane consisting of Paleozoic and Lower Mesozoic metamorphic rocks. The Main Deep Fault (Abadeh Fault) is a major lithospheric fault separating the two parts. The purpose of this paper is to clarify the role of the southeastern Sanandaj-Sirjan Zone in the tectonic evolution of the southeastern Zagros Orogenic Belt on the basis of geological evidence. The new model implies that Neo-Tethys 1 came into being when the Central Iran Microcontinent split from the northeastern margin of Gondwana during the Late Carboniferous to Early Permian. During the Late Triassic a new spreading ridge, Neo-Tethys 2, was created to separate the Shahrekord-Dehsard Terrane from Afro-Arabian Plate. The Zagros sedimentary basin was formed on a continental passive margin, southwest of Neo-Tethys 2. The two ophiolitic belts of Naien-Shahrebabak-Baft and Neyriz were developed to the northeast of Neo-Tethys 1 and southwest of Neo-Tethys 2 respectively, related to the sinking of the lithosphere of the Neo-Tethys 1 in the Late Cretaceous. It can be concluded that deposition of the Paleocene conglomerate on the Central Iran Microcontinent and Pliocene conglomerate in the Zagros Sedimentary Basin is directly linked to the uplift generated by collision.
Using satellite data for geological mapping beside saving time and reducing coast leads to increased accuracy. In this study, the result of remote sensing techniques has been compared for manifesting geological units. The study area is limited to 1:25,000 rectangle of Pasab-e-Bala which is located in the northeast of Isfahan and West of Qom-Zefreh fault. This region mainly consists of Devonian and Quaternary sedimentary units. In this study, ASTER and OLI satellite data has been corrected atmospherically and radiometrically. Spectral Analogues method and OLI band combination (652) in RGB image were powerful in distinguishing various rock units. Finally, a new geologic map of the Pasab-e-Bala area was created by integrating the results of remote sensing, previous geological maps and field inspection. It is concluded that the workflow of Landsat 8 image processing, interpretation and ground inspection have a great potential to identify geological formations. According to field data originality, accuracy of the produced map was evaluated through calculating kappa index and overall accuracy and a thematic accuracy of 86% was achieved for geological formations.
Evapotranspiration is one the most important parameters in the hydrological cycle and plays a significant role in energy balance of the earth's surface. Traditional field-based measurements approaches for calculation of daily evapotranspiration are valid only for local scales. Using advanced remote sensing technology, the spatial distribution of evapotranspiration may now be quantified more accurately. At the present study, daily evapotranspiration is estimated using Landsat 8 datasets based on the Surface Energy Balance System (SEBS) algorithm over the Zayanderud Dam area in central Iran. For this purpose, three Landsat 8 datasets in the years 2013, 2014 and 2015 covering the study area were atmospherically corrected using the FLAASH approach. The biophysical parameters of the earth's surface for SEBS algorithm, such as normalized difference vegetation index (NDVI), Leaf area index (LAI), fractional vegetation cover (FC) were extracted from the visible and near infrared bands and land surface temperature was computed from thermal bands the Landsat 8 datasets. The spatial distribution of daily ET was provided separately for each year. In addition to the SEBS algorithm, the Penman-Monteith method was applied to estimate the daily ET from meteorological datasets which was obtained from two synoptic stations within the study area. Finally, the simulated daily ET values from both SEBS and Penman-Monteith method were compared to observed values obtained from a lysimeter within the study area. Although the estimated results from both SEBS and Penman-Monteith show a strong correlation with the observed values, the derived ET maps and following analysis demonstrated SEBS has higher accuracy and strength in estimation of daily ET in Zayanderud Dam region.
This study has tried to prove the ability of remote sensing techniques to extract information necessary for preparation of geological mapping of the earth's surface using multi-spectral satellite images which are rich sources of Earth's surface information. In this study, the surface geological mappings of Zefreh region have been investigated through ASTER, OLI, and IRS-PAN remote sensing data. To prepare the geological map, preprocessing steps and reducing noises from data using MNF algorithm were firstly carried out. Then a set of processing algorithms and image classification methods are included; the band rationing, color composite and pixel classification based on maximum likelihood, spectral and sub-pixel classification methods of spectral angle mapper (SAM), spectral feature fitting (SFF), linear spectral differentiation (LSU), hill-shade images and automatic lineament extraction were used. Confusion matrix was formed for all classified images through control points were randomly selected from 1:25,000 map of the region to determine the accuracy of obtained results, which indicated the maximum accuracy (up to 90%) of output images. Comparing the results obtained from these methods with the map prepared by ground operations confirmed accuracy results. Finally, the surface geology and fault map of Zafreh region was produced by combining detected geological formations and tectonic lineaments.
<p>To overcome the food and water shortages and optimize the land use, remote sensing techniques and satellite image processing have utilized our demands. However, with limitations in image processes, the use of such techniques will need further development to overcome related constraints. Shadows, occurred on the opposite side of objects, result from topography and different angles of the emitting light source is one of these limitations. Several topographic correction methods are proposed based on the properties of ground coverage. To suggest and compare methods for imagery topography, this study uses Cosine Correction, C-Correction, Statistical Empirical Correction, and finally the Minnaert Correction. The study area used to compare the introduced methods is located in North West of Isfahan (Ardestan), Iran. The current report has used OLI sensors (LANDSAT 8) combined with ASTER global digital elevation data. After implementing topographic corrections, by optimal index OIF, images are processed. Based on the unsupervised method and the study region, results based on optimal arrangement bands are introduced as a suitable classification. In conclusion, based on imagery and statistical data from the topography corrections, Minnaret shows the most exceptional topographical correction classification for the chosen studied region.</p><p>&#160;</p><p>Keywords: aster, c-correction, cosine correction, Isfahan, Landsat-8, land management, Minnaert, oli, topographic correction, unsupervised classification.</p>
The Mesozoic ophiolitic Mélange, north of Nain in the Central-East Iran Microplate (CEIM) comprises serpentinized ultramafic rocks, harzburgites, dunite, gabbro, peridotite, pelagic limestone and other carbonate rocks. The excellent and vast exposure of this desert region is well suited for geologic mapping of this rock suite using remote sensing, especially using data from the satellite-borne advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) imaging system which was designed for mapping mineral information. In this study, data processing methods like Method Minimum noise fraction (MNF), Feature Oriented Principal Components Selection (FPCS), Band Ratios (BR) and Optimum Index Factor (OIF) were used to process ASTER data to optimize the mapping of ophiolite rock types. For example, a simple color composites of OIF (Red: B3, Green: B4, and Blue: B8) and Band ratios (e.g. Red: (B2 + B4)/B3, Green: (B5 + B7)/B6, Blue: (B7 + B9)/B8) were useful for discriminating serpentinite, meta-basalt and granite rock types. It is concluded here that proposed ASTER data has the potential for mapping similar ophiolites elsewhere using the global archive of ASTER imagery.
The study area is located on the Urumieh-Dokhtar Magmatic Assemblage of Iran, in the west of Ardestan between the East longitude 52˚1' to 52˚18' and the North latitude 33˚17' to 33˚27'. Remote sensing techniques are suitable for studying the alterations occurring in the igneous terranes. The alteration zones are well illustrated by implementation of the principal component analysis and the Crosta methods and Spectral Feature Fitting on ASTER data. In order to identify the lineaments, both Landsat-8 satellite imagery and GDEM-ASTER data are used in spatial processing. Using directional filtering and automatic extraction of lineaments, a tectonic lineaments map is prepared. Then alteration maps, tectonic lineaments map and 1:100,000 geology map are used to identify areas with high potential of Cu mineralization.
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