Abstract
This study investigated the multispectral remote sensing techniques including ASTER, Landsat 8 OLI, and Sentinel 2A data in order to distinguish different lithological units in the Alagbayan area of Dornogobi province, Mongolia. Therefore, Principal component analysis (PCA), Band ratio (BR), and Support Vector Machine (SVM), which are widely used image enhancement methods, have been applied to the satellite images for lithological mapping. The result of supervised classification shows that Landsat data gives a better classification with an overall accuracy of 93.43% and a kappa coefficient of 0.92 when the former geologic map and thin section analysis were chosen as a reference for training samples. Moreover, band ratios of ((band 7 + band 9)/band 8) obtained from ASTER corresponds well with carbonate rocks. According to PCs, PC4, PC3 and PC2 in the RGB of Landsat, PC3, PC2, PC6 for ASTER data are chosen as a good indicator for different lithological units where Silurian, Carboniferous, Jurassic, and Cretaceous formations are easily distinguished. In terms of Landsat images, the most efficient BR was a ratio where BRs of 5/4 for alluvium, 4/7 for schist and 7/6 to discriminate granite. In addition, as a result of BR as well as PCA, Precambrian Khutag-Uul metamorphic complex and Norovzeeg formation can be identified but granite-gneiss and schist have not given satisfactory results.
Highlights
In recent years, multispectral remote sensing data has been widely used in geological research such as lithological mapping, mineral alteration mapping as well as structural geology (Kumar et al, 2015; Pour et al, 2019; Adiri et al; 2016; Bentahar et al, 2020)
In the present study, the lithological discrimination of the Alagbayan area which is located in Mandakh soum, Dornogobi province, has been achieved using Landsat 8 OLI, ASTER and Sentinel 2A
Principal component analysis (PCA) transformation was carried out with images of ASTER and Landsat whereas Sentinel with these two data was used for Support Vector Machine (SVM) supervised classification
Summary
Multispectral remote sensing data has been widely used in geological research such as lithological mapping, mineral alteration mapping as well as structural geology (Kumar et al, 2015; Pour et al, 2019; Adiri et al; 2016; Bentahar et al, 2020). The approach of this technique is based on the characteristics of the physical and chemical properties of the different types of rocks. In detail, these rocks reflect the electromagnetic energy in three areas including visible (400-700 nm), near-infrared (700-1300 nm), and short wave infrared (1300-2500 nm) (Hauff, 2008), which could allow the identification of the spectral absorption features of the mineral composition of the rock (Bachri et al, 2019). Only a few studies have been mapped previously using remote sensing techniques within the territory of Mongolia (Stolz 2008; Son et al, 2012; Munkhsuren et al, 2019; Son et al, 2019; Son et al, 2021).
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