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.