Integration of ASTER and landsat TM remote sensing data for chromite prospecting and lithological mapping in Neyriz ophiolite zone, south Iran

Abstract

Chromite deposits in Iran are located in the ophiolite complexes, which have mostly podiform types and irregular in their settings. Exploration for podiform chromite deposits associated with ophiolite complexes has been a challenge for the prospectors due to tectonic disturbance and their distribution patterns. Most of Iranian ophiolitic zones are located in mountainous and inaccessible regions. Remote sensing approach could be applicable tool for choromite prospecting in Iranian ophiolitic zones with intensely rugged topography, where systematic sampling and conventional geological mapping are limited. In this study, Landsat Thematic Mapper (TM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data were used for chromite prospecting and lithological mapping in the Neyriz ophiolitic zone in the south of Iran. Image transformation techniques, namely decorrelation stretch, band ratio and principal component analysis (PCA) were applied to Landsat TM and ASTER data sets for lithological mapping at regional scale. The RGB decorrelated image of Landsat TM spectral bands 7, 5, and 4, and the principal components PC1, PC2 and PC3 image of ASTER SWIR spectral bands efficiently showed the occurrence of major lithological units in the study area at regional scale. The band ratios of 5/3, 5/1, 7/5 applied on ASTER VNIR-SWIR bands were very useful for discriminating most of rock units in the study area and delineation of the transition zone and mantle harzburgite in the Neyriz ophiolitic complex. Spectral Angle Mapper (SAM) technique was implemented to ASTER VNIR-SWIR spectral bands for detecting minerals of rock units and especially delineation of the transition zone and mantle harzburgite as potential zones with high chromite mineralization in the Neyriz ophiolitic complex. The integration of information extracted from the image processing algorithms used in this study mapped most of lithological units of the Neyriz ophiolitic complex and identified potential areas of high chromite mineralization (transition zone and mantle harzburgite) for chromite prospecting targets in the future. Furthermore, image processing results were verified by comprehensive fieldwork and laboratory analysis in the study area. Accordingly, result of this investigation indicate that the integration of information extracted from the image processing algorithms using Landsat TM and ASTER data sets could be broadly applicable tool for chromite prospecting and lithological mapping in mountainous and inaccessible regions such Iranian ophiolitic zones.

Spectral Signature Characterization and Remote Mapping of Oman Exotic Limestones for Industrial Rock Resource Assessment

“…The pixels with a measurement greater than the specified maximum divergence threshold are not classified. The SID mapping is applied by studying the absorption characters of endmembers and ground truth Regions of Interest (ROIs) to compare the results of SAM and MLC methods [12,13]. The contingency matrixes and classification accuracies of all algorithms are provided and discussed in Section 4.…”

“…(3) principal components analysis [11,12]; (4) spectral angle mapper [13] and (5) decorrelation stretch [14][15][16]. In this study, we focus on comparing spectral bands 8 of ASTER and 7 of ETM+ (Enhanced Thematic Mapper Plus) to distinguish exotic limestone blocks for industrial resource development in the mountain region of the Sultanate of Oman.…”

Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration

“…They used band ratios and principal components analysis to separate mafic and ultramafic rocks. In the Neyriz ophiolite, Iran, Eslami et al [62] also applied ASTER and Landsat data for lithologic mapping, using similar techniques. In North Africa, Adiri et al [63] combined Landsat and ASTER data to map sedimentary rocks in Morocco.…”

Determining Earthquake Susceptible Areas Southeast of Yogyakarta, Indonesia—Outcrop Analysis from Structure from Motion (SfM) and Geographic Information System (GIS)

“…The Landsat 8 with the RGB composite of 567 (equal with RGB 457 in Landsat 7 ETM+) was used to distinguish the surface geological units. The RGB 567 is the best composite and efficiently composite to display the major lithological units including lineament at the regional scale [33][34][35][36]. Similar to the Landsat 7 ETM+, band 5 (near-infrared: 0.851-0.879 µm) is a useful tool for displaying water or land surface because most of the radiation in this wavelength range is absorbed by water.…”