Identification of pegmatites zones in Muiane and Naipa (Mozambique) from Sentinel-2 images, using band combinations, band ratios, PCA and supervised classification

Abstract

Remote sensing has been widely used in Geological Sciences for different applications, such as to identify geological and mineralogical objects and surface alteration changes. This study aimed to analyze the Sentinel-2 potential to detect pegmatite bodies and associated alteration zones in Muiane and Naipa in Mozambique. Different remote sensing techniques were applied to a Sentinel-2 image: RGB combinations, band ratios, principal component analysis (PCA), and supervised image classification algorithms such as the Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). MLC was used as a benchmark classifier to evaluate the performance of SVM because MLC is the predominant algorithm employed in remote sensing classification studies. For that, several statistical metrics based on the confusion matrices were computed, namely accuracy, Kappa index, precision, recall, and f-score, among others. This study allows identifying the location of pegmatites by direct identification and segregating between hydrothermally altered zones and non-altered areas through remote sensing data/techniques, supported by field data. The field campaigns allowed for validating the results obtained and verifying the pegmatites identified using Sentinel-2 data that were not previously mapped. Moreover, reflectance spectroscopy studies in the laboratory were conducted on the samples collected in the field campaigns allow to validate the adequacy of the methodology proposed in this study. The results show that the precise identification of pegmatite targets requires a high spatial resolution such as Sentinel-2 images. Thus, with the integration of high spatial and spectral resolution data, a potential level of precision and accuracy can be achieved in the study areas.