Satellite images are widely used to map geological and environmental features at different map scales. The ability of visible to near-infrared (VNIR) scanner systems to map gossans, rich in iron and associated with weathered sulfide occurrences, as well as to characterize regoliths, is perhaps one of the most important current applications of this technology. Initial results of this study show that advanced space-borne thermal emission and reflection (ASTER), VNIR, and short-wave infrared radiometer scanner systems can be used successfully to map iron ores. By applying internal average relative reflectance, false color composite, minimum noise fraction transform, and mathematical evaluation method (MEM) techniques, iron contaminations were successfully detected in the Chadormalu iron mine area of central Iran. An attempt was also made to discriminate between the geogenic and anthropogenic iron contaminations in the vicinity of the Chadormalu iron deposit. This research compares ASTER and Landsat 8 data images and the MEM with the band ratio method in a full scope view scale and demonstrates ASTER image data capability in detecting iron contaminations in the Chadormalu area. This indicates that ASTER bands 3, 2, and 1 have a higher spatial (15 m) resolution compared with sensors used in previous works. In addition, the capability of the MEM in detecting Fe-contaminants, unlike the color judgments of the band ratio method, can discriminate between iron pollution in an alluvial plain and the Fe-contents of the host and country rocks in the study area. This study proved that Landsat 8 data illustrate exaggeration both in the MEM and band ratio final results (outputs) and cannot display iron contamination in detail.
The study area is about 2650 sq. kms that is located in the southern part of Central Iranian volcanic-sedimentary belt. This belt runs parallel with the Zagros mountain ranges. The main aim of this study is to differentiate between the altered rocks with the carbonates and calcareous shales. Both ASTER and ETM+ data are used here for hydrothermal alteration mapping. This study compares these two data for hydrothermal alteration mapping. Different image processing techniques such as band ratio, principal component analysis and band combinations are used here aiming at enhancing the altered areas. The band ratio has shown that band7/band9 and b4/b5 ratios of ASTER data are better than band5/band7 ratio of ETM+ data for mapping the clay minerals. Principal component analysis has shown that PC4 of ASTER data is better than PC5 of ETM+ data for enhancing phyllic and argillic alterations. Color combination of PC4 (Red), PC5 (green), and PC6 (Blue) for the ASTER data is enhancing the altered areas and, at the same time, suppressing the effects of carbonates, flysch and calcareous sediments. ASTER data was used for image classification using spectral angle mapper algorithm. Through this analysis it was found that clay minerals such as kaolinite and montmorilonite can be differentiated. Comparison of the above image processing techniques have shown that except the enhancement of the iron oxide bearing rocks, the ASTER data is more useful for alteration mapping than ETM+ data.
The area under study is located in the southern part of the Central Iranian Volcanic Sedimentary belt and covers an area of about 488 sq. kms. Sar Cheshmeh and Darrehzar areas with known mineralization and alteration are chosen as control areas. Airborne geophysical data -- radiometry and megnetometry -- and ETM+ data has been integrated and analyzed using fuzzy classification. This type of classification is suggested for remote sensing data, but it can also be used for classification of combined airborne geophysical and satellite data. After defining the training areas (Sar Cheshmeh and Darrehzar areas), the entire region is classified into altered and unaltered aras. This analysis is found useful for exploration of porphyry type deposits in the Central Iranian Volcanic Belt, where most parts of this belt is surveyed by airborne geophysics.
Many of the known porphyry copper deposits are situated in the Central Iranian volcanic Belt. The area under study is located in the southern part of this belt and covers an area of about 2600 sq. kms. ETM+ images have been used for alteration mapping. Crosta method was found useful for enhancing the areas with hydroxyl and iron oxide minerals. However field checking has shown that this method is not able to enhance all the areas with hydrothermal alteration. In order to recognize such areas combination of remote sensing and geophysical data can be helpful. The Sar Cheshmeh mine is chosen as a control area. The data has been divided into two sets namely, explanatory and target variables. The target variables are rock geochemistry (Cu and Mo) and alteration data. The explanatory data are Landsat and airborne geophysical data (K and Th counts, and total magnetics). The data analysis is performed in two steps, (1) integration and analysis of target variables over the control area by principal component analysis method, and (2) transformation of explanatory variables by using the eigenvector loadings of the first step. This technique is found to be useful for the delineation of hydrothermaly altered areas with enough confidence.
Tertiary porphyry deposits in Iran are an important source of Cu, Mo and locally Au. Most of the known porphyry Cu deposits are located in the Central Iranian Volcanic Belt(CIVB). Pariz area is located within this belt and is chosen as a test area to evaluate remote sensing data at one such area, and use of these data in exploration of the other parts of CIVB. Eight known mineralization sites were chosen in the area, which are mainly porphyry type. SPOT images in XS mode are used to study geology as well as hydrothermal alteration in the Pariz area, where soil and vegetation cover is substantially poor. Different approaches such as band ratioing, principal component analysis, I-S-H decorrelation processing, digital filtering and hybrid composite were used to enhance the diagnostic features associated with the lithologies as well as the hydrothermal alteration. Color combinations of the principal components, I-S-H transformation and 2/1 ratio have proved to be the best image enhancement techniques for geological studies in such areas. Lineament analysis has shown that the areas with known ore occurrence and the hydrothermally altered areas are closely associated with the higher photolineament factor values. Comparison of geophysical and remote sensing data has shown that there is a good correlation between the airborne geophysical and remote sensing data in the area.
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