Mapping and discrimination of the mineralization potential in the Bonako area (Central Cameroon Domain): Insights from Landsat 9 OLI data, GIS fuzzy modeling techniques and field observations

Abstract

The Bonako area is situated in the Central Cameroon Domain of the Central African Fold Belt. In this study, the discrimination of lithological units with hydrothermally altered deposits is investigated by combining Landsat 9 OLI data, fieldwork descriptions, GIS fuzzy modeling techniques, and remote sensing approaches including false color composite (FCC), de-correlation stretch (DS), standard principal component analysis (PCA) and minimum noise fraction (MNF). In addition, image processing methods such as band ratios (BR) and selective principal component analysis (Crosta-PCA) were applied to target and delineate hydrothermally altered and corresponding minerals and the spectral angle mapper (SAM) classification algorithm was used to classify the discriminated lithological units within the study area. The evaluation of the fuzzy membership of each alteration-derived mineral from Landsat 9 OLI and ASTER data indicates that the highest favourability index varies from 0.8 to 1 indicating a rating index related to iron mineralization. The integration of selected remote sensing methods allowed the identification of gabbro, granites, gneiss, and mylonites with iron-oxides, hydroxyl/clay, and ferrous occurrences as potential mineralization in the Bonako area. The analysis of lineaments illustrated two main structural trends (N-S and NE-SW) and an accessory one (E-W) in the study area. Merging these with the identified formations highlighted the formations with mineral deposits. Subsequently, the lithological maps displaying alteration minerals and lineaments were validated by fieldwork investigations and microscopic data. Quantitatively, the overall accuracy of the SAM method is 100 %, which also validates the effectiveness of the classification of lithologies using Landsat 9 OLI data. This research predicts how the integration and processing of Landsat 9 OLI, Fuzzy, ASTER data, and field investigations can simplify the identification of rock units with potentially mineralized zone. It also suggests that such a combined method is useful in defining targeted mineralized areas during exploration research.