Evaluation of SEBS Algorithm for Estimation of Daily Evapotranspiration Using Landsat-8 Dataset in a Semi-Arid Region of Central Iran

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.

Lithological Mapping from OLI and ASTER Multispectral Data Using Matched Filtering and Spectral Analogues Techniques in the Pasab-e-Bala Area, Central Iran

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.

Lithological Analysis of Nain Ophiolitic Zone Using ASTER Data

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.

Exploring Porphyry Copper Deposits in the Central Iran Using Remote Sensing Techniques

This study used the ability of remote sensing technology to identify alteration zones in porphyry copper mining and Iron oxides of area in south Nain district in Iran by using Lands at-8 data source. The band ratio of 3/2 derived from image spectra was used to indicate the distribution of iron oxides and 6/3 for identifying gossan. Hydrothermal alteration mineral zones associated with porphyry copper mineralization identified and discriminated based on two algorithms of target detection, MTTCIMF and OSP. Those techniques identified porphyry copper mineralization in study area and six points were diagnosed as the best location for ore exploration. For more accurate study and recognition between mineralization and tectonic structure of district, the lineament map of area was produced by applying Gaussian high-pass filter on IRS data. The Spatial distribution of hydrothermal alteration zones has been verified by inspection in field works and Fuzzy logic. Results showed that image processing techniques have a great ability to obtain comprehensive information for reconnaissance stage of porphyry copper exploration in the case study and assist researcher to explore porphyry copper and iron oxides regions before time-consuming and costly ground investigation.

Alteration Mapping in Central Urumieh-Dokhtar Magmatic Assemblage of Iran Using ASTER and OLI Data

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.

Advanced Investigation of Remote Sensing to Geological Mapping of Zefreh Region in Central Iran

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.