Application of Remote Sensing Technologies to Map the Structural Geology of Central Region of Kenya

Abstract

Advancements of digital image processes (DIP) and availability of multispectral and hyperspectral remote sensing data have greatly benefited mineral investigation, structure geology mapping, fault pattern, and landslide studies: site-specific landslide assessment and landslide quantification. The main objective of this research was to map the geology of the central region of Kenya using remote-sensing techniques to aid rainfall-induced landslide quantification. The study area is prone to landslides geological hazards and, therefore, it was necessary to investigate geological characteristics in terms of structural pattern, faults, and river channels in a highly rugged mountainous terrain. The methodology included application of PCA, band rationing, intensity hue saturation (IHS) transformation, ICA, false color composites (FCC), filtering applications, and thresholding, and performing knowledge-based classification on Landsat ETM + imagery. PCA factor loading facilitated the choice of bands with the most geological information for band rationing and FCC combination. Band ratios (3/2, 5/1, 5/4, and 7/3) had enhanced contrast on geological features and were the input variables in a knowledge-based geological classification. This was compared to a knowledge-based classification using PCs 2, 5, and IC1, where the band ratio classification performed better at representing geology and matched FCC [IC1, PC5, saturation band of IHS (5,7,3)]. Fault and lineament extraction was achieved by filtering and thresholding of pan-band8 and ratio 5/1 and overlaid on the geology map. However, the best visualization of lineaments and geology was in the FCC [IC1, PC5, saturation band of IHS (5,7,3)], where volcanic extrusions, igneous, sedimentary rocks (eolian and organic), and fluvial deposits were well discriminated.