A new approach for hydrothermal alteration mapping by selecting and interpreting principal components in Landsat ETM+ images

Document Type : Research Article

Authors

• Mohammad Kashkoei Jahroomi
• Afshin Qishlaqi

Shahrood University of Technology

10.22067/econg.v8i1.31997

Abstract

Introduction
In remote sensing studies, especially those in which multi-spectral image data are used, (i.e., Landsat-7 Enhanced Thematic Mapper), various statistical methods are often applied for image enhancement and feature extraction (Reddy, 2008). Principal component analysis is a multivariate statistical technique which is frequently used in multidimensional data analysis. This method attempts to extract and place the spectral information into a smaller set of new components that are more interpretable. However, the results obtained from this method are not so straightforward and require somewhat sophisticated techniques to interpret (Drury, 2001). In this paper we present a new approach for mapping of hydrothermal alteration by analyzing and selecting the principal components extracted through processing of Landsat ETM+ images.
The study area is located in a mountainous region of southern Kerman. Geologically, it lies in the volcanic belt of central Iran adjacent to the Gogher-Baft ophiolite zone. The region is highly altered with sericitic, propyliticand argillic alterationwell developed, and argillic alteration is limited (Jafari, 2009; Masumi and Ranjbar, 2011).
Multispectral data of Landsat ETM+ was acquired (path 181, row 34) in this study. In these images the color composites of Band 7, Band 4 and Band 1 in RGB indicate the lithology outcropping in the study area. The principal component analysis (PCA) ofimage data is often implemented computationally using three steps: (1) Calculation of the variance, covariance matrix or correlation matrix of the satellite sensor data. (2) Computation of the eigenvalues and eigenvectors of the variance-covariance matrix or correlation matrix, and (3) Linear transformation of the image data using the coefficients of the eigenvector matrix.

Results
By applying PCA to the spectral data, according to the eigenvectors obtained, 6 principal components were extracted from the data set. In the PCA matrix, theeigen vector differences between the means of the level of significance between two bands (or spectral significance of the PC). The higher value is regarded as the Target Value of the bands which show a lower correlation. The components having maximum spectral significance of PCs, in bands 1 and 3, 5 and 7 and 5 and 3, were selected for enhancement of iron oxides, clay minerals and carbonate minerals, respectively. In each PC matrix, the sum of the significances is regarded as the spectral weight of that PC.
The spectral weight of the extracted PCs, was found to be as follows:
PC5> PC4>PC2>PC3>PC7>PC1
The inverse PC4 and –PC3 provide valuable information on vegetation mapping. In order to map the alteration zones and igneous rocks outcropped in the study area, the color composites of the PC5, -PC4 and average of each PC are included in RGB, respectively. The spectral proportion of each PC pertaining to each mineral was calculated as spectral significance in the two bands (e.g. Bands 5 and 7 for clay minerals and Bands 3 and 1 for Fe oxide minerals) divided by spectral weight of that PC. Based on the obtained results, the selectivity of the extracted components for enhancement of clay minerals and Fe oxide minerals was calculated and images of these minerals were produced using the following expressions:
Fe oxide minerals:
Clay minerals:
For carbonate minerals, the proportion of each PC is calculated in terms of the eigenvectors of bands 5 and 3. The selectivity of the PCs used in enhancing of spectral data of carbonate minerals is as follows: PC5>PC2>PC1>PC3>PC4>PC7
In the remotely sensed image, PC5 with high spectral weight was selected as the informative PC for clay minerals, iron oxides and carbonate minerals. This is becausepropylitic alteration and the formation of carbonate minerals can be easily enhanced in the processed images. Eventually, overlapping of the processed images provides patterns of hydrothermal alterationwhich indicate the areas to be prospected. In order to validate the obtained results of the image processing with geological evidence,petrographic studies of rock samples collected from major outcrops in the study area were made. It was found that quartz, calcite, epidote, sericite and chlorite are the main constituents of sericiticand propylitic alteration assemblages in the study area. The minerals are virtually enhanced in Landsat ETM+ using the proposed methods and confirm the results obtained from multispectral data analysis.

Conclusion
This study provides a new and improved approach to obtain the most meaningful spectral data for oxides, carbonates and clay minerals in multispectral images. As these minerals are typically found in hydrothermal alteration, the method presented in this article can be used for enhancement of such mineral spectral data, which can be very helpful in prospecting and exploration for hidden mineral deposits.

Acknowledgments
The first author wishes to sincerely thank Z.Gholami (PhD student, Shiraz University) for her assistance during this study. Also, we would like to thank Dr.Ranjabar (ShahidBahonar University of Kerman) and Dr. H.Tangestani (Shiraz University) for their constructive comments. Financial support for this study was provided by the ZABPAK Company.

References
Drury, S.A., 2001. Image interpretation in geology. Routledge, London, 304 pp.
Jafari, H., 2009. Evaluate the economic potential of copper in Hararan (Kerman province) using by lithogeochemical methods. Journal of Land and Resources, 1(2): 25-31.
Masumi, F. and Ranjbar, H., 2011. Hydrothermal alteration mapping using image sensors ASTER and ETM+ in the northern half of the Geological Map 1:100,000 Baft. Journal of Earth Sciences, 20(79): 121-128.
Reddy, M.A., 2008. Textbook of Remote Sensing and Geographical Information Systems. Atlantic Publishers and Distributors, Haydarabad, 476 pp.

Keywords

• Principal component analysis
• LandSat ETM+
• hydrothermal alteration
• mapping
• Baft
• Kerman

20.1001.1.20087306.1395.8.1.11.0

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