Episodes 2023; 46(3): 407-419
Published online September 1, 2023
Copyright © International Union of Geological Sciences.
Abraham Thomas1*, Tshilidzi Radzuma2, Ndivhuwo Cecilia Mukosi2
1Wilson Geoservices (Pty) Ltd., 204 Brenda Road, Murrayfield, Pretoria 0184, South Africa
2Council for Geoscience, Limpopo Regional Office: 30A Schoeman Street, Polokwane, 0699, South Africa
Correspondence to:*E-mail: firstname.lastname@example.org
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This paper presents the usefulness of remote sensing data processing and analysis of Sentinel-2 imagery acquired during the winter period of year 2018 to aid the geological mapping of six 1:50,000 scale map sheets of Giyani Greenstone Belt area of South Africa. Images of different band ratios and band combinations were experimented using ENVI and SNAP to identify suitable band/band ratio combinations to produce false colour composites (FCCs) that enabled the discrimination of surface features (land/water/vegetation) and the geology of the area through identification of lithological units, structural features and lineaments present. FCCs made in RGB using band ratios and combinations of B12/B11-B11-Slope in degrees, B12/B11-B11-B2, B12-B8A-B11/B3, B12-B8A-B5, B4/B3-B11-B12, B11-B12-B8A, B12-B11-B4, B7-B4-B2 and shaded relief made from 30m digital elevation model (RGB: 240, 0, 120 degree azimuth) are found to be very useful in discriminating lithological units, structural features, lineaments and protruded outcrops including dykes that are hardly not visible in a true colour image of the same resolution. FCCs made using RGB: B4/B3-B11/B8-B11/B12 and RGB: B11/B12-B11/B8-B4/B2 could show indications of existence of some alteration and iron oxides. The datasets of selected FCCs produced from Sentinel-2 and exported as GeoTiff format for displaying in a GIS are found to be very useful to mapping geologists for their geological mapping purpose. The quality of the information extractable from the described analysis depends on the inherent quality and characteristics (band resolutions) of the input datasets.