Application of remote sensing techniques in lithological and mineral exploration: discrimination of granitoids bearing iron and corundum deposits in southeastern Banyo, Adamawa region-Cameroon

Discrimination of granitoids bearing iron and corundum mineral deposits in the southeastern part of Banyo (Adamawa region of Cameroon) using Landsat 8 OLI data and provides essential information's for exploration. Field data and remote sensing traditional mapping techniques including false color composites (FCCs), principal component analysis (PCA), Crosta-PCA, band ratios (BR), constrained energy minimization (CEM), classification algorithm as maximum likelihood, and spectral angle mapper (SAM) were applied to the visible, near-infrared, and shortwave infrared bands of Landsat 8 OLI data. The results of this research demonstrated to be most effective to highlight potential locations of granitoids bearing iron and corundum deposits. In addition, iron deposit was mapped using band ratio 4/2 due to high reflectivity in the visible red region while corundum deposits are detected by applying the constrained energy minimization method to Landsat-8 OLI band ratio 6/7 highlighting bright tones. The results of remote sensing data were validated through fieldwork and laboratory analysis (thin section analysis). Furthermore, lineaments analysis revealed the prevalence of three major structural trends affecting the southeastern part of Banyo ( N-S, E-W, and NE-SW to ENE-WSW) paired with mapped lithologies allows the potential emplacement of mineral deposits. The novelty of the present study relies in the integration of Landsat 8 OLI and field data enabling the identification of highly prospective lithological units, mineral deposits, defining exploration targets, and the minimisation of time consuming field reconnaissance in the study area.

Automated summary

By using Landsat 8 OLI data, field data, and remote sensing traditional mapping techniques, it is possible to discriminate granitoids bearing iron and corundum mineral deposits in the southeastern part of Banyo, Cameroon. The application of classification algorithms such as maximum likelihood and spectral angle mapper to Landsat 8 OLI data in the visible, near-infrared, and shortwave infrared bands proved to be effective in identifying potential locations of these deposits. Additionally, the integration of Landsat 8 OLI and field data allows for the identification of prospective lithological units, mineral deposits, and exploration targets, reducing the need for time-consuming field reconnaissance.