Textural and Rule-based Lithological Classification of Remote Sensing Data, and Geological Mapping in Southwestern Prieska Sub-basin, Transvaal Supergroup, South Africa

Although remote sensing has been widely used in geological investigations, the lithological classification of the area interested, based on medium-spatial and spectral resolution satellite data, is often not successful because of the complicated geological situation and other factors like inadequate methodology applied and wrong geological models. The study area of the present thesis is located in southwest of the Prieska sub-basin, Transvaal Supergroup, South Africa. This area includes mainly Neoarchean and Proterozoic sedimentary rocks partly uncomfortably covered by uppermost Paleozoic and lower Mesozoic rocks and Tertiary to recent soils and sands. The Precambrian rocks include various formations of volcanic and intrusive rocks, quartzites, shales, platform carbonates and Banded Iron Formations (BIF). The younger rocks and soils include dikes and shales, glacial sedimentary rocks, coarser siliciclastic rocks, calcretes, aeolian and fluvial sands, etc. Prospect activity for mineral deposits necessitates the detailed geological map (1:100000) of the area. In this research, a new rule-based classification system (RBS) was put forward, integrating spectral characteristics, textural features and ancillary data, such as general geological map (1:250000) and elevation data, in order to improve the lithological classification accuracy and the subsequent mapping accuracy in the study area. The proposed technique was mainly based on Landsat TM data and ASTER data with medium resolution. As ancillary data sets, topographic maps and general geological map were also available. Software like ERDAS©, Matlab©, and ArcGIS© supported the procedures of classification and mapping. The newly developed classification technique was performed by three steps. Firstly, the geographic and atmospheric correction was performed on the original TM and ASTER data, following the principal component analysis (PCA) and band ratioing, to enhance the images and to obtain data sets like principal components (PCs) and ratio bands. Traditional maximum-likelihood supervised classification (MLC) was performed individually on enhanced multispectral image and principal components image (PCs-image). For TM data, the classification accuracy based on PCs-image was higher than that based on multispectral image. For ASTER data, the classification accuracy of PCs- image was close to but lower, than that of multispectral image. As one of the encountered Banded Iron Formations, the Griquatown Banded Iron Formation (G-BIF) was recognized well in TM-principal components image (PCs-image). In the second step, textural features of different lithological types based on TM data were analyzed. Grey level co-occurrence matrix (GLCM) based textural features were computed individually from band 5 and the first principal component (PC1) of TM data. Geostatistics-based textural features were computed individually from the 6 TM multispectral bands and 3 principal components (PC1, PC2 and PC3). These two kinds of textural features were individually stacked as extra layers together with the original 6 multispectral bands and the 6 principal components to form several new data sets. Ratio bands were also individually stacked as extra layers with 6 multispectral bands and 6 principal components, to form other new data sets. In the same way new data sets were formed based on ASTER data. Then, all of the new data sets were individually classified using a maximum likelihood supervised classification (MLC), to produce several classified thematic images. The classification accuracy based on the new data sets are higher than that solely based on the spectral characteristics of original TM and ASTER data. It should be noticed that for one specific rock type, the class value in all classified images should correspond to its identified (ID) value in digital geological map. The third step was to perform the rule-based system (RBS) classification. In the first part of the RBS, two classified images were analyzed and compared. The analysis was based on the classification results in the first step, and the elevation data detracted from the topographic map. In comparison, the pixels with high possibility of being classified correctly (consistent pixels) and the pixels with high possibility of being misclassified (inconsistent pixels) were separately marked. In the second part of the RBS, the class values of consistent pixels were kept unchanged, and the class values of inconsistent pixels were replaced by their values in digital geological map (1:250000). Compared to the results solely based on spectral characteristics of TM data (54.3%) and ASTER data (66.41%), the new RBS classification improved the accuracy (83.2%) significantly. Based on the classification results, the detailed lithological map (1:100000) of the study area was edited. Photo-lineaments were interpreted from multi data source (MDS), including enhanced satellite images, slope images, shaded relief images and drainage maps. The interpreted lineaments were compared to those, digitized from general geological map and followed by a simple lineament analysis compared to published literatures. The results show the individual merits of lineament detection from MDS and general geological map. A final lineament map (1:100000) was obtained by integrating all the information. Ground check field work was carried out in 2009, to verify the classification and mapping, and the results were subsequently incorporated into the mapping and the classification procedures. Finally, a GIS-based detailed geological map (1:100000) of the study area was obtained, compiling the newly gained information from the performed classification and lineament analysis, from the field work and from published and available unpublished detailed geological maps. The here developed methods are proposed to be used for generation of new, detailed geological maps or updates of existent general geological maps by implementing the latest satellite images and all available ancillary data sets. Although final ground check field work is irreplaceable by remote sensing, the here presented research demonstrates the great potential and future prospects in lithological classification and geological mapping, for mineral exploration