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The aim of the study is to employ remotely sensed data and evaluate the principal components (PC) of physiographic variables like topography, drainage, slope and wetness. These variables may wield physiographic influence on groundwater. The software ENVI 5.0 and GLOBAL MAPPER 15 were used to obtain these information from Landsat ETM+ and SRTM-DEM, while ARCMAP 10.3 was used to perform the extraction of slope data. Principal component analysis (PCA) revealed that there was very strong negative loading of Eigen Values for the variables; topography, wetness, and drainage density within PC1, PC2, and PC4, respectively. However, slope showed a very strong positive loading of Eigen Values in PC3. PC3 multiplied by −1 was then performed in order to realise a more ideal relationship between these physiographic variables and the availability of groundwater. It showed weak positive Eigen Values of +0.031 and +0.012 for wetness and slope respectively, although their influence on groundwater occurrence is not very strong. Three classes namely; low, moderate, and high groundwater potentials were well-defined based on these physiographic variables using multi-fractal analysis (MFA). The regions delineated are 26%, 65%, and 9% respectively, in terms of percentage areal coverage. Therefore, variables other than the physiographic influences exert stronger influences on the spatial distribution of groundwater in the study area. |
