Geological and Mineralogical Mapping Based on Statistical Methods of Remote Sensing Data Processing of Landsat-8: A Case Study in the Southeastern Transbaikalia, Russia

Abstract

The work considers the suitability of using multispectral satellite remote sensing data Landsat-8 for conducting regional geological and mineralogical mapping of the territory of south-eastern Transbaikalia (Russia) based on statistical methods for processing remote sensing data in conditions of medium–low-mountain relief and continental climate. The territory was chosen as the object of study due to its diverse metallogenic specialization (Au, U, Mo, Pb-Zn, Sn, W, Ta, Nb, Li, fluorite). Diversity in composition and age of ore-bearing massifs of intrusive, volcanogenic, and sedimentary rocks are also of interest. The work describes the initial data and considers the procedure for their pre-processing, including radiometric and atmospheric correction. Statistical processing algorithms to increase spectral information content of satellite data Landsat-8 were used. They include: principal component analysis, minimum noise fraction, and independent component analysis. Eigenvector matrices analyzed on the basis of statistical processing results and two-dimensional correlation graphs were built to compare thematic layers with geological material classes: oxide/hydroxide group minerals containing transition iron ions (Fe3+ and Fe3+/Fe2+); a group of clay minerals containing A1-OH and Fe, Mg-OH; and minerals containing Fe2+ and vegetation cover. Pseudo-colored RGB composites representing the distribution and multiplication of geological material classes are generated and interpreted according to the results of statistical methods. Integration of informative thematic layers using a fuzzy logic model was carried out to construct a prediction scheme for detecting hydrothermal mineralization. The received schema was compared with geological information, and positive conclusions about territory suitability for further remote mapping research of hydrothermally altered zones and hypergenesis products in order to localize areas promising for identifying hydrothermal metasomatic mineralization were made.

Multi-Sensor Satellite Remote-Sensing Data for Exploring Carbonate-Hosted Pb-Zn Mineralization: Akhlamad Area, Razavi Khorasan, North East Iran

“…VNIR spectral bands (0.45 to 1.2 µm) contain the most important information for the identification of iron oxide/hydroxide (Fe 3+ /Fe 2+ ) [61]. In this study, the 4/2 band ratio of Landsat-8, Sentinel-2, and ASTER data was used to characterize iron oxide/hydroxide.…”