Satellite-Enhanced Groundwater Prospect Assessment in a Crystalline Basement Terrain Using Landsat Imageries and DEM Data Around Ilorin Metropolis and Adjacent Areas, Southwest Nigeria

Satellite-Enhanced Groundwater Prospect Assessment in a Crystalline Basement Terrain Using Landsat Imageries and DEM Data Around Ilorin Metropolis and Adjacent Areas, Southwest Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Satellite-Enhanced Groundwater Prospect Assessment in a Crystalline Basement Terrain Using Landsat Imageries and DEM Data Around Ilorin Metropolis and Adjacent Areas, Southwest Nigeria Ajibola Ayoola Michael, Olorunfemi Martins Olusola, Osotuyi Abayomi Gaius, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4674737/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract An assessment of the Crystalline Basement Complex (CBC) terrain of the province around Ilorin, Southwestern Nigeria, was carried out to evaluate its groundwater potential. The study aimed at investigating the hydrogeomorphological and geological/hydrogeological characteristics of the study area. This was with a view to classifying the study area into different groundwater potential zones, in order to delineate and recommend prospective areas for subsequent detailed geophysical study and drilling campaign. Drainage map, topographic map, and geological maps of the study area were acquired and integrated with Satellite imageries comprising Landsat Enhanced Thematic Mapper Plus (ETM+) 2000 and ASTER GDEM covering the area, and processed using the ArcGIS 10.4 software. The hydro-geomorphological map and hydrogeologic lineament density maps were generated from the processed remotely sensed data. Results from the processed Digital Elevation Model (DEM) showed five distinct hydrogeomophic units which include: Dambos (valleys) (156–270 m a.s.l), the Pediplain (270–305 m a.s.l), lower Pediment (305–330 m a.s.l), upper Pediment (330–355 m a.s.l) and the residual hills (355–390 m a.s.l). The hydrogeologic lineament trends show N-S, NNE-SSW, NE-SW and NW-SE trends. The hydro-significant lineament density map reveal five (5) lineament cluster zones in the range of 0.00–1.25, 1.25–2.50, 2.50–3.75, 3.75–5.00 and 5.00–5.60 km per km 2 . Cross examination of the hydro-geomorphological map and lineament density map, in a Geographical Information System (GIS) environment, enabled the characterization of the study area into five different classes of very low, low, moderate, high and very high groundwater potential zones. It is concluded that groundwater potential of the area around Ilorin was generally of very low to low rating. However, there are few areas with moderate groundwater potential. Hydrology Geographic Information Systems Geophysics Geology Groundwater potential zones Crystalline Basement terrains Remote Sensing (RS) Geographical Information System (GIS) DEM Landsat Imageries Lineaments Nigeria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1.0 INTRODUCTION Ilorin southwest, Nigeria has continued to suffer from shortage of portable water. This is because the municipal water capacity of the city increasingly falls short of the geometrical population growth of the Metropolis. For example, in 2007, the manucipal water supply of 12 million could only cater for about 6% of the city domestic need which stood as at then at 190 million (for 1 million population – Kwara State Diary, 2007 ). This is based on the fact that the average amount of water used domestically each day by every person is about 190 litres (Hamill and Bell, 1986 ). Today, the percentage has dwindled to 4.5% because municipal water supply has not increased and population has grown to about 1.4 million (requiring about 266 million litres excluding industrial and agricultural water needs). The seasonal nature of Asa River further exacerbates the problem. Therefore, residents most of whom are within the low to middle class have over the years taken solace in drilling for groundwater but with low success rate. This is due to the crystalline nature of the geology of the area, confining groundwater to fractures, joint, faults and weathered overburden; thus necessitating thorough geophysical investigation prior drilling. This is because crystalline rocks have poor porosities and permeability and the few existing secondary ones are constrained to few localities with fractures, fissures, joints, faults, etc., altogether called lineaments. Geophysical survey is useful in locating such potential areas. However, it is not economical and too expensive for the most of the poor residents of the city to embark on numerous geophysical surveys until a positive location is delineated. Hence there is need to carry out large scale preliminary investigation as a reconnaissance for geophysical survey in order to suggest promising localities for groundwater development. Advances in space technology have made the measurement of earth properties and related quantities associated with the subsurface earth phenomena around morphological units, tectonic plate boundaries geologic boundaries, faults, fissures, and shear zones possible without necessary physical contact with them (Huang et al., 2008 ; Zoran et al., 2014 ; Lei et al., 2018 ; Osotuyi et al., 2021 ; Osotuyi et al., 2022 ). Teme and Oni ( 1991 ) used remote sensing techniques to detect groundwater flow in fractured media in a hard rock terrain in Nigeria. They were able to use aerial photography and radar imagery to adequately locate and delineate the extent and frequency of these fracture systems thus making it possible for the siting of productive boreholes at appropriately predetermined localities within the basement areas. Edet et al. ( 1994 ) utilized the lineament analysis for groundwater exploration in the Precambrian Oban Massif and Obudu Plateau in the southeastern part of Nigeria. Gradual progressive changes involving the increase and attenuation of the thermal energy have been, before and post-seismic events have been reported using satellite Thermal Infrared (TIR) around the Federal Capital Territory in Nigeria (Osotuyi et al., 2021 ; Osotuyi et al., 2022 ). Edet et al. ( 1994 ) employed an analysis of lineaments extracted from aerial photography for groundwater exploration in the Precambrian Oban Massif and Obudu Plateau, Southeastern Nigeria. They concluded that the Plateau is characterised by high lineament density while the Oban Massif shows low lineament density. Sander and Chesley (1996) utilised Landsat-TM, SPOT, and infrared photography to study a groundwater project in Ghana. These data were interpreted for linear vegetation, drainage, and bedrock features that would indicate underlying transmissive fracture zones, in order to develop optimal strategies for future well siting. Solomon (2003) adopted an integrated approach with remote sensing, Geographic Information Systems (GIS), and traditional fieldwork techniques to assess the groundwater potential in the central highlands of Eritrea. Digitally enhanced colour composites and panchromatic images of Landsat TM and SPOT were interpreted to produce thematic maps such as lithology and lineaments. The potential of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for lithological and lineament mapping was also evaluated. Sahu and Sahoo (2006) utilised remote sensing techniques for delineating groundwater in the drought-prone Bonai area of India, preparing hydro-geomorphological and lineament maps by digital image processing across different hydro-geomorphologic units to understand the aquifer system in the area. Mogaji et al. (2011) conducted GIS analysis in the hard-rock terrain areas of Ondo State, Southwestern Nigeria, to determine the impact of rock types on the variability of aquifer characteristics and their influence on the hydrogeologic systems. Bera and Bandyopadhyay (2012) utilised an integrated remote sensing and GIS methodology to develop and test the evaluation of groundwater resources of a watershed in West Bengal and the adjoining Jharkhand State. They utilised IRS 1D LISS-III satellite data with other datasets, existing maps, and field observation data to extract information on the hydro-geomorphic features. Akinluyi ( 2013 ) adopted an integration of geological, geophysical, and remotely sensed data in regional groundwater evaluation in the basement complex of Ondo State, Southwestern Nigeria. He utilised topographic maps, Landsat Mapper ETM+, and SPOT-XS ASTER DEM to prepare thematic layers including hydro-lineament density, hydro-lineament intersection density, and hydrogeomorphology. Bayowa ( 2013 ) conducted a regional groundwater potential study within the basement complex terrain of Ekiti State, Nigeria, considering geologic, geomorphological units, lineament, and lineament intersections in a typical basement complex terrain, using satellite imagery such as Digital Elevation Model (DEM), Landsat Thematic Mapper, and Vertical Electrical Sounding (VES) to determine the hydrogeomorphic conditions and hydrogeologic lineament density distribution. Previous works have established that groundwater potential areas can classified by identifying fractured zones of higher permeability in crystalline rocks, thus making it possible to generate a regional potential map (Masoud & Koike 2006 ) by utilizing satellite imageries to extract hydrogeologic lineaments and hydrogeomophlogical data for regional groundwater potential classification. This study therefore intends to carry out a detailed remotely-sensed reconnaissance assessment of groundwater potentials using satellite technology involving Landsat Enhanced Thematic Mapper (ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (herein after referred to as ASTER GDEM) in and around Ilorin Metropolis, Asa local government, and their adjacent areas in Southwestern Nigeria. From these sets of data, results involving hydrogeomophlogical classification, hydrogeological lineament density, and regional groundwater potential map, are intended to be commissioned and commended to environment planners, hydrologists, geologists, geophysicists, engineers, climate scientists, water management experts, policy makers, and other interest groups. This is with the purpose of making informed scientific and policy decisions for well-informed and precision-guided groundwater campaigns in the densely populated and built-up areas like Ilorin City. Our study conclude that lineament density could provide some streamlined guidance for detailed exploration of groundwater potential and occurrence in Crystaline Basement terrains. 2.0 STUDY BACKGROUND 2.1 Location, Population, Vegetation, and Climate The study area cut across four local government council areas including Ilorin, the capital city of Kwara State, Nigeria. The area constituting the study location lies within Longitudes 4° 28’E and 4° 34.7’ E and Latitudes 8° 25.5’N and 8° 32.5’N, covering about 246 km 2 (Fig. 1 ). Ilorin is one of the fastest growing urban centers in Nigeria with a rapid increase in the population the city since its emergence as the state capital in 1976. The 1991 census puts the population of the city at about 572,172 (Ifabiyi, 2011 ). The study area is in the savannah region of Nigeria located within the tropical climate belt of West Africa (Osotuyi et al., 2023 ) with humidity ranging between 60 and 89% and mean annual temperature falling between 27 and 37°C. The weather condition in the region is of two broad types i.e. rainy season and dry season (Osotuyi et al., 2021 ). The rainy season commences around March and ends in October, while the dry season begins in November and ends in March with mean annual rainfall between 75 to 112 cm. The maximum rainfall are recorded in June or September after the usual break that occurs from July to August. The average wind speed is about 7km/h (4 mph or 4 knots). 2.2 Regional Geology, Local Geology, and Hydrogeology The study area falls within the Basement Complex area of southwestern part of Nigeria which are Precambrian to Lower Paleozoic in age (Rahaman, 1976; Falebita et al., 2018 ). The Precambrian Crystalline Basement Complex (CBC) rocks in Ilorin and adjacent areas consist of gneisses and migmatites; metasediments (that is, schists, quartzites and metavolcanics), few Pan-African (older) granites and late-stage minor pegmatitic and aplitic intrusives (Oluyide et al., 1998 ). According to Olasehinde et al. ( 1998 ), Ilorin is situated on the undifferentiated Precambrian Basement Complex rocks of granitic and metamorphic origin. The rock units form part of the regional southwestern highlands of Nigeria running NW-SE parallel to the River Niger (Offodile, 1987 ; Olasehinde et al., 1998 ; Salako et al., 2019 ; Osotuyi et al., 2021 ; Ojo et al., 2023 ). The profile of the basement terrain comprises the topsoil, lateritic layer (occurring as un-indurated or hard indurated duricrust), weathered, slightly-weathered and fresh (fractured or unfractured) crystalline basement rocks. The oldest rocks in the area comprise gneiss complex whose principal member is biotite-hornblende gneiss with intercalated amphibolites, underlying large part of the study area (Olasehinde et al., 1998 ). Other rock types are the migmatites, older granite consisting mainly porphiritic granite, gneiss and granite-gneiss and quartz-schist. However, the study area comprises maily migmatites, granites and some quartzites such (Fig. 2 ). Major factors influencing the hydrogeology of an area include rock types, geologic structures, and climate of the area (Ademilua, 1997 ). This is because the geological formations and the associated structures underlying the area determine the types of aquifer to be encountered and the means of recharging them. Also, the climate determines the amount and rate of groundwater recharge of the aquifer and the watershed (Maliu, 1987 ; Lewis, 1987 ). Thus, the hydrogeology of an area is considered under two parts: (i) surface water and (ii) groundwater. Most crystalline rocks of Nigeria are situated in areas of high relief, as a result, run-off is high and infiltration rates are very low. The basement rocks are either devoid or of low primary porosity and permeability. Most often, the occurrence of groundwater in this type of terrain is localized (Olorunfemi and Fasuyi, 1993 ; Salako et al., 2019 ; Oni et al., 2020 ). In a typical basement complex environment as the study area, Olorunfemi and Fasuyi ( 1993 ) identified five (5) aquifer systems which are: (i) weathered aquifer, (ii) weathered/fractured (unconfined) aquifer, (iii) weathered/fractured (confined) aquifer, (iv) weathered/fractured (unconfined)/fractured (confined) aquifer, and (v) fractured (confined) aquifers. 2.3 Physiography and Drainage The landscape ranges in elevation in the western part from 225 m to 360 m above sea level and in the eastern part from 220 m to 365 m (Fig. 3 a). Sobi hill is the dominant landform. It is an inselberg, and it is the highest point in the city (394 m above sea level). The drainage system in Ilorin is dendritic in nature and is dominated by Asa River, which flows from north to south and divides the city into two parts, the western and eastern parts (Fig. 3 b). The western part represents the indigenous area. The eastern part coincides with the modern layout. Other rivers that drain the city are: Agba, Alalubosa, Oyun, Osere, Aluko (Fig. 3 b). 3.0 DATA AND METHODOLGY The methodology adopted in this study is described in the workflow shown in Fig. 4 . In this work, our interest is constrained to application of remote sensing and Geographic Information Systems (GIS) using geomorphological terrain data and lineaments extraction for groundwater investigation. Two major types of satellite imageries commonly used in hydrogeology were employed in this research (Bayowa, 2013 and Akinluyi 2013 ), viz: Landsat images and Digital Elevation models. The materials acquired include topographic and administrative maps of Kwara State; satellite imageries including Landsat Enhanced Thematic Mapper Plus (ETM+) of year 2000, with Instantaneous Field of View (IFOV) sensor having 30 m spatial resolution of path 190, row 54; and ASTER GDEM Digital Elevation Model (DEM) (Figure 5 ) of ASTGTM_N08E004 and ASTGTM_N08E005 covering the area around the four Local Government Areas (LGAs). The topographic and administrative map of the entire region were procured from the Kwara State office of the Surveyor General. The satellite imageries were downloaded from the NASA online data repository ( http://asterweb.jpl.nasa.gov/gdem.asp ). To analyse the data using ArcGIS geospatial analysis interface, we created a GIS database for the groundwater investigation project in the ArcCatalog. Shapefile delimiting the boundaries of the study area was created using Arcmap 10.0 before being stored in the GIS Database. Data including geological map, topographic map, and the administrative map demarcating the boundaries of LGAs under consideration were geo-referenced, digitized (vectorised), and stored in the created database. Satellite Imageries (Landsat - 8 bands comprising visible and near infrared bands and GDEM) covering the study area were sourced and imported as raster into ArcGIS (Figure S1), clipped to the geometry of the study area, and thereafter stored into the database. The satellite imageries were subjected to various image preprocessing for geometric effect removal and error correction due atmospheric interference. These preprocessing and processing are covered under two broad categories including image enhancement and image transformations which are hereafter succinctly discussed. 3.1 Image Enhancement This was embarked upon to improve the resolution of the imagery to assist in visual interpretation and analysis. Of the several types that are available, Edge Enhancement Filter (EEF) and Raster Pan-Sharpening (RPS) were carried out. The EEF, also known as ‘sharpening’ filters, was utilized for lineament detection (Meijerink et al., 2007 ). Subsequent stages of image enhancement involved the use of Pan Sharpening filters and an Autorectification algorithm (for elevation data like DEMs), which are applied to transformed images based on our study objective, although the latter showed no significant impact on the DEM’s resolution and was thus not displayed. Figure 6 demonstrates the impact of edge enhancement, achieved through the use of a high pass filter, on the Near Infrared (NIR) and Thermal Infrared (TIR) bands of Landsat ETM + and on the ASTER GDEM of the study area. These processes play a crucial role in the extraction of lineaments which is one of the focus of this study. The second preprocessing stage of image enhancement involves the Raster Pan-Sharpening. The Pan Sharpened Raster merges high-resolution pan chromatic raster data with a lower-resolution multiband dataset, resulting in an RGB raster band combination that matches the resolution of the panchromatic raster. The ESRI Model was used for pan sharpening in this study due to its superior image contrast for the study area. As shown in Figure S2 (e), the pan sharpened image of Band 752 enhances the visibility of surficial elements, making linear features more distinct and drainage channels, which are toned blue, easier to identify. 3.2 Image Transformations Unlike image enhancement operations which are normally applied only to a single channel of data at a time, image transformations usually involve combined processing of data for multiple spectral bands. Target identification is better enhanced by increasing the contrast of the features present on the imagery. Landsat ETM + image transformations employed in this study including Colour composite bands combination, principal components analysis (PCA), Normalised Difference Vegetation Index (NDVI), together with Landsat and DEM Image Fusion and DEM Hill Shade are highlighted below. 3.2.1 Band Combination The three primary colours, Red, Green, and Blue (RGB), represent the full spectrum of colours perceivable by humans. These colours can be assigned to selected bands or transformed images, creating a ‘false colour’ composite, such as the commonly used composite band 432 (Figure S3), generated by assigning Red to the Near Infrared (NIR) band, Green to the red spectral band, and Blue to the green spectral band. This band is particularly useful for land use analysis, especially in dry regions where some spectral bands can be highly correlated; thus, it’s beneficial to use three least correlated spectral bands for a colour composite, a selection made in this study through visual inspection. 3.2.2 Principal Component (PC) Transform The PC transform is an image enhancement technique used to highlight non-hydrogeologically significant structures for isolation and lineament structure enhancement. Figures 7 (a-b) show the combination of PC 1–6 of composite band 123457 and PC1, PC2, and PC3 of Band 123457, which are crucial for extracting lineaments due to their display of general linear features. The PCs of Band 752 (Figs. 7 (c-d)) and Band 541 (Figs. 7 (e-f)) are also significant, with the former better displaying non-hydrogeologically significant lineaments like roads and airport runways, and the latter showcasing hydrogeologically significant lineaments, many of which control drainage. 3.2.3 Normalized Difference Vegetation Index (NDVI) The NDVI is a metric for quantifying green vegetation, useful in differentiating between forested and deforested regions. Its calculation hinges on the relative reflectance of near-infrared and red light. The NDVI is the ratio of the difference between the reflectance in the red and near infrared bands divided by the total reflectance in these two bands. It is computed by using the equation: NDVI = (band 4 (NIR) – band 3 (Red))/ (band 4 (NIR) + band 3 (Red)). The NDVI image was created using the Raster Calculator and Spatial Analyst toolbar in ArcMap 10.0, with the resulting map added to the workspace as “Initial NDVI” (Fig. 8 (a)). The map was then classified using both unsupervised (Fig. 8 (b)) and supervised methods (Fig. 8 (c)), with the latter requiring training for land cover recognition and the application of the Reclassify tool. The Direct Arc Toolbox method was also used to create signature files with predefined classes, revealing that most drainage channels are associated with vegetation and that vegetated lineaments are hydrogeologically significant (Figs. 8 (d) and 8(e)). Once the NDVI analysis is completed, the quality of information extracted from the study area is well-enhanced. 3.2.4 Image Fusion The Fig. 9 illustrates that the wider arms of the drainage channels, depicted in lemon green, are primarily located on two elevated upper pediments, one in the west and the other in the southeast. The lower courses of the channels traverse the pediplains and valleys, represented in green. The built-up area, including the city center, is shown in purple, with two reservoirs (Asa and Agba Dams) appearing in a very thick, dark red. The fused DEM suggests that surface runoff flows from the western and southeastern uplands through the pediplain into the valleys and dambos, potentially indicating the direction of groundwater flow (Fig. 9 ). 3.2.5 Hill Shade This is the transformation of a Digital Elevation Model (DEM) into a hill-shaded relief imagery using the Spatial Analyst (Surface) tool in ArcGIS, with specific sun angle and azimuth parameters (Figure S4). The algorithm for hill shade uses the sun illumination angle to highlight both natural and manmade features. However, the lineaments extracted from this imagery may not be hydrogeologically significant and should be compared with those from other imageries to ensure accuracy in the extraction procedure 3.3 Generation of Hydrogeomorphological Thematic Map The Digital Elevation Model (ASTER GDEM, Fig. 5 ) covering the entire area were acquired, analysed, classified and reclassified, and used to produce the Hydrogeomorphological Units (HGUs) information on the basement terrain of the study area. Using the reclassify spatial analysis tool in ArcMap (ArcGIS 10), the generated maps are then classified into five hydrogeomorphological units or zones namely: dambos (valley), pediplain, lower pediment, upper pediment, and the residual (hill). and saved into the geo-database as classified hydrogeomorphological map. 3.4 Generation of Lineament Density Thematic Map The satellite imageries EMT + NIR Band (Figure S1) and ASTER GDEM (Fig. 5 ) were subjected to various stages of image processing analyses in order to delineate lineaments. The eight Landsat EMT + Bands collected in the visible, Near Infrared electromagnetic spectrum, and the panchromatic intensity were subjected to image processing. These processes include Band Combination, Pan sharpening, Normalized Difference Vegetation Index (NDVI), and Principal Component Analysis (PCA) to enhance the linear features for lineament extraction (Figs. 6 – 8 ). Both the Landsat EMT + NIR Band and ASTER GDEM acquired over the study area were also edge enhanced in ArcGIS for the same purpose (Fig. 6 ). The Digital Elevation Model (ASTER GDEM) was resampled to the resolution of the Landsat panchromatic image and fused together along with Landsat EMT + NIR Band in order to delineate linear features (Figs. 7 and 8 ). The Digital Elevation Model (ASTER GDEM) was also used to produce the hill shade for the study area (Figure S4) in an effort to enhancing and delineating more subtle lineaments, with extraction done to exclude artifact, following Bayowa ( 2013 ) procedures. Each processing operation was overlain on Digital Elevation Model (DEM) in order to validate the origin of the lineaments. The extracted lineaments were classified by drawing their rose diagrams that will indicate the orientation and frequency of occurrence of the extracted lineaments. The extracted lineaments were then digitized and presented as a lineament density Map. All delineated lineament from the various imageries were then extracted and saved in to the created geo-database as general linear features. The hydrogeologically significant lineaments were afterwards extracted from the general lineaments using the information from thermal band, NDVI (Fig. 8 ), Google Earth, and ground truthing. All the extracted features were saved into the geo-database as the hydrogeologically significant lineaments. The maps previously generated were then used to produce the lineament density map in ArcGIS. The lineament density map was classified into five hydrogeological units or zones (as above) and saved into the geo-database. 4.0 RESULT AND DISCUSSION 4.1 Geomorphological Characteristics The study area is broadly of a low relief terrain typified by the remains of the residual hill, presence of pediments which has been further differentiated into lower and upper pediments, pediplains and dambos within the valleys (Table 1 ; Fig. 10 ). Geomorphologically, the entire study area could be classified into three Zones: Zone A, B, and C (Fig. 10 ). Zones A and C are characterized by relics of escarpments which have been weathered and differentiated into lower and upper pediments. Zone A, in the western part, runs from south to north, while Zone C in the eastern flank of the study area, terminates beyond the middle portion of the study area. Spatially, Zone A lies predominantly within Asa (LGA), with its lower pediments creeping into Ilorin West LGA in the center, while Zone C stretches through the southern part of Ilorin East and the western portion of Ilorin South LGA. Zone B at the center, is a depression that is slightly narrow in the south, but spreads out in the north. It is sandwiched between zones A and C. Zone B is morphologically differentiated into pediplain and Dambos. Zone B lies mainly within Ilorin West LGA with its pediplain extending into Asa in the west and Ilorin East LGA in the east. The northwestern flank of Zone A shows similar morphology with zone B. Residual hills (white tone on Fig. 10 ) is majorly evident in the southeast (Zone C, Figs. 10 ) and in the north-central area (Sobi Hill in Zone B, Figs. 10 ). 4.1.1 Residual Hill and Inselberg Residual hills and inselbergs have elevation range of 355 to 390 m, and occurs within migmatites and granite gneiss-underlain areas, without any sign of their occurrence within the quartzite. This geomorphological unit is not prevalent within the study area. Remnants of the residual hill are mainly evident in the southeast (Zone C) and in the north-central (Sobi Hill in Zone B). They are ranked as the least prolific geomorphological unit in terms of groundwater potential, and it is assigned with a groundwater rating of 1 (Fig. 10 ). In general, areas with many outcrops are not considered favourable for groundwater exploration due to shallow weathering depths and low recharge of possible fractures. It is also observed to coincide with areas with lesser presence of lineaments (Fig. 11 ). Residual hills have the lowest groundwater rating among the geomorphological units. Table 1 Hydrogeomophological Units, their Elevation Ranges, Intercepted Lithologies, Groundwater Potential Rating and Hydrogeomorphological Ranking Hydrogeomophological Units Colour Code in Fig. 10 Elevation Above Sea Level (m) Groundwater Potential Rating Hydrogeomorphological Ranking Dambos (in valleys) Blue / Dark tone 156–270 Very High 5 Pediplain Yellow/Light-Brown tone to Grayish-Green 270–305 High 4 Lower Pediment Brown /Dull Red 305–330 Moderate 3 Upper Pediment Purple/ Red 330–355 Low 2 Residual Hill White / Light Red 355–390 Very Low 1 4.1.2 Pediment The pediments are the gently sloping undulating surfaces with or without a veneer of weathered/soil materials usually formed at the foot of a mountain and often dotted with rock outcrops (Bayowa, 2013 ). This geomorphological unit generally has low overburden. Thus, groundwater conditions are poor and limited to some large fractures. This might be the result of erosion under semi-arid conditions of a weathered mantle formed earlier under more humid conditions, when the regolith expanded vertically and laterally (Meijerink et al., 2007 ). The pediments in the study area show zonation (catena or topo-sequence; Meijerink et al., 2007 ) into an Upper and Lower Pediments, as is illustrated in Fig. 10 . Pediments in hard rock country often have poor groundwater storage capability, especially when the pediment consists of a true rock-cut surface (Leopold et al., 1964 ). Also, the presence of thin sheet-wash deposits can obliterates fractures; and because soil is shallow and elevation gradient is high, runoff is high and infiltration is low, resulting in low recharge. 4.1.3 Upper Pediment This geomorphological unit has an elevation range of 330–355 m and occurs mainly within migmatites and granite gneiss underlain area. Except where saturated fractures exist, the upper pediment shows poor groundwater potential because of its associated relatively high elevation gradient, high run off and low infiltration when compared to other geomorphological units. It is the second lowest ranked geomorphological unit in term of groundwater potential and it is assigned a rating of 2 within the entire hydrogeomorphological units. 4.1.4 Lower Pediment This geomorphological unit has an elevation range of 305–330 m. It also occurs within migmatites and granite gneiss underlain area. It has higher groundwater potential than the upper pediment because of its relatively lower elevation gradient. The lower pediment is the third ranked geomorphological unit in term of groundwater potential and it is assigned a rating of 3 within the entire hydrogeomorphological units. 4.1.5 Pediplain This geomorphological unit has an elevation range of 270–305 m and has imprints of all the three lithologies. It is dominant in the central part and at the two northern corners (east and west) of the study area. The planar configuration of the geomorphological unit gives room for high infiltration and profound weathering; yet, less erosion, thus, giving room for a high groundwater potential. It is assigned a rating of 4 within the entire hydrogeomorphological units. 4.1.6 Dambo This geomorphological unit has an elevation range of 156–270 m and has no significant imprint in granite gneiss underlain area. McFarlane (1989) described a Dambo as the seasonally waterlogged bottomland of the land surface, irregularly lowered by a process termed ‘etch-planation’. This involves differential leaching by infiltrated water to the extent that the saprolite collapses, leaving a thick residuum. The dambo-catenas in the crystalline regions of sub-Saharan Africa has attracted the attention of hydrogeologists and geomorphologists (Mackel, 1985; McFarlane, 1989; Wolski 1999, and other authors) probably because of the seasonal presence of groundwater at the surface. In the study area, the interfluves consist of light textured soils and saprolite that offer opportunity for recharge. This hydrogeomorphological unit is characterized by vegetation even in the dry season, no matter how little, which must have been supported by groundwater. Three occurrences of the dambos are evident within the study area with the dominant one occurring within zone B (narrow in the south and widening out towards the north). The other two are restricted to the two northern corners of study area, i.e. northwest of zone A and northeast of zone C respectively. The dambos occur mainly within migmatites and quartzite (in lower central portion of zone B) without any sign of its occurrence in the granite gneiss. Dambo is the highest ranked and most prolific geomorphological unit in term of groundwater potential and it is assigned a rating of 5 within the entire hydrogeomorphological units. 4.2 HYDROGEOLOGICAL CHARACTERISTICS 4.2.1 Hydrogeologically Significant Lineament Map Figure 11 shows the hydrogeologically significant lineament map prepared by removing all lineaments that fall on hills, ridges and those on streams and river channels which are presumed not to be structurally controlled in the study area. The Rose Diagram (Fig. 12 ) shows that there are four predominant sets of hydrogeologically significant lineaments which are closely related to tectonically developed features. The first set of the lineaments trends in the N-S, the second NNE-SSW; the third set trends NE-SW and the fourth set trends in the NW-SE direction. 4.2.2 Lineament Density Map Lineament density map is a measure of cluster of linear features in a particular area. From the hydrogeologically significant lineaments (i.e. faults fissures, joints etc. with groundwater prospect) map, the lineament density map (Fig. 13 ) was generated using the ArcGIS 10.0. Lineament density map is one of the important thematic maps prepared from lineaments, which are critically used in groundwater studies related to hard rock terrain (Subba Rao, 1992 ; Krishnamurthy et al., 1996 ). The peaks in the lineament density contour maps are the places of interest for groundwater resource development. Areas with high lineament density excluding (the residual hill environment) are good for groundwater development (Haridas et al., 1994 ). From the generated lineament density map (Fig. 13 ), the moderate to high lineament densities are dominant in the central part of the study area, while the western, southern and eastern parts show very low to low lineament densities. Table 2 shows the lineament densities, the intercepted lithologies, the groundwater potential rating and the groundwater potential ranking for each lineament density class within the study area. Table 2 Groundwater Prospect of the Study Area based on Lineament Density Lineaments DensityColour Code Lineamentabundancein thestudy area Lineaments Density Range (Km/Sq. Km) Groundwater Potential Ranking Lineament Ground-waterRanking Dark Blue Least 0.00 − 1.25 Very Low 1 Blue Less 1.25–2.50 Low 2 Light Blue Moderate 2.50–3.75 Moderate 3 Lemon Green More 3.75–5.00 High 4 Yellow Most 5.00–5.60 Very High 5 5.0 CONCLUSION AND RECOMMENDATION Geomorphological analysis of the Digital Elevation Model (DEM) of study area showed that the area comprised of the Dambos (valleys) (156–270 m a.s.l), the pediplain (270–305 m a.s.l), lower pediment (305–330 m a.s.l), upper pediment (330–355 m a.s.l) and the residual hills (355–390 m a.s.l). The geological/hydrogeological investigation by the analysis of the extracted lineaments showed four predominant sets of lineaments. The first set of the lineaments trends in the N-S, the second in the NNE-SSW direction; the third set trends NE-SW while the fourth set trends in the NW-SE direction. The hydro-significant density map generated from hydro-significant lineament map revealed five (5) lineament cluster zones in the range of 0.00–1.25, 1.25–2.50, 2.50–3.75, 3.75–5.00 and 5.00–5.60 Km/SqKm. From the generated Lineament density map, lineaments within the western central parts of the study area are characterized by moderate to very high lineament with several lineaments intersection, while the northwestern and southeastern corners show lineament densities of low to high. The moderate to high lineament densities are dominant in the central part of the study area, while the western, southern and eastern parts show very low to low lineament densities. On the basis of geology, the areas underlain by quartzite show low to moderate groundwater potential. Migmatites and Granite Gneiss show groundwater rating predominantly of very low to low. However, some localized areas with high lineament densities show moderate to high groundwater potential in migmatites; whereas, on Granite Gneiss, very low groundwater potential zone is extensive, as seen in Ilorin South LGA. By cross evaluation and integration of the geological, the hydrogeomophogical, and the lineaments density map of the study area, we concluded that the groundwater potential of the study area is generally very low to low. However, there are pockets of areas with moderate groundwater potential and very few localities with high groundwater. It is recommended that detailed pre drilled geophysical survey be carried out in order to further and better understand the hydrogeological characteristics of the study area in an effort to affirm the findings of this research. Declarations Funding The authors received no funding was received in carrying out this study. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4674737","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321771024,"identity":"571604c4-6bdc-47a0-a951-68c88e2b0095","order_by":0,"name":"Ajibola Ayoola 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13:55:43","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4674737/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4674737/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59559123,"identity":"919ab6d1-4cb6-4159-aa7f-0bdaea024946","added_by":"auto","created_at":"2024-07-03 07:57:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165109,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area covering Asa, Ilorin-West, Ilorin East, and Ilorin South in Southwest Nigeria.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/b65044f3f2979040be66bdbb.png"},{"id":59559124,"identity":"f88d692f-118b-4dcf-9b01-7f5e4985de3d","added_by":"auto","created_at":"2024-07-03 07:57:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":232372,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area showing the major geological features.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/667d207934901b19bd4128fa.png"},{"id":59559126,"identity":"b0dedead-8338-496f-b54a-a01caf541b80","added_by":"auto","created_at":"2024-07-03 07:57:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1313780,"visible":true,"origin":"","legend":"\u003cp\u003eTopographic map (a) and drainage map (b) of the study area\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/168bfcb01d9e81ab8312624b.png"},{"id":59559917,"identity":"e794bd20-7fb0-4fc4-9ab4-5a55ef74eba7","added_by":"auto","created_at":"2024-07-03 08:05:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126599,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the methods adopted in this study\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/c24b136d9ab0ab2617279659.png"},{"id":59559138,"identity":"b99dba8e-680e-428b-ae62-f746ff1940d6","added_by":"auto","created_at":"2024-07-03 07:57:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":781850,"visible":true,"origin":"","legend":"\u003cp\u003eDigital Elevation Model of the Study Area (Extracted with Global Mapper v13.00)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/74303aea08c87a53e38c1e96.png"},{"id":59559121,"identity":"175cf9d4-21b1-4689-a9bf-4f473e704565","added_by":"auto","created_at":"2024-07-03 07:57:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1708698,"visible":true,"origin":"","legend":"\u003cp\u003eEdge Enhanced High Pass Filter on Landsat Imageries and ASTER GDEM of the Study Area. (a) Near Infrared (Band 4), (b) Edge Enhanced Band 4 stretched with SD Histogram (c) Thermal Band Infrared (Band 6), (d) Edge Enhanced Band 6 stretched with SD Histogram, (e) Digital Elevation Model (DEM), and (f) Edge Enhanced DEM stretched with Histogram Specification.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/dfe31e46a43bfbb33c6cd6f5.png"},{"id":59559920,"identity":"fe807e9d-d92d-42f7-ba8a-731040469f48","added_by":"auto","created_at":"2024-07-03 08:05:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2726994,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Components of Landsat ETM+ Composites Bands of the study area (a) Combination of PC 1-6 of Composite Band 123457, (b) Combination of PC 1-3 of Composite band 123457, (c) Combination of PC 1-3 of Composite band 752, (d) Combination of PC 1-2 of Composite band 752, (e) Combination of PC 1-3 of Composite band 541, and (f) Combination of PC 1-2 of Composite band 541\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/47fb78e5c6a9556efd5d4bad.png"},{"id":59559132,"identity":"417b8650-90f7-4d96-aa3a-b31dfec7cc73","added_by":"auto","created_at":"2024-07-03 07:57:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2150722,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized Difference Vegetation Index (NDVI) Transforms where (a) Initial NDVI Transform, (b) Unsupervised NDVI Transform (2 classes), (c) Supervised NDVI (2 classes), (d) NDVI with 3 ISO Cluster Classes, and (e) NDVI with 4 ISO Cluster Classes\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/6ed921ce444dcef67015aed0.png"},{"id":59559119,"identity":"b11a08c8-e88f-4576-bfb2-f5ff3180c507","added_by":"auto","created_at":"2024-07-03 07:57:14","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1605991,"visible":true,"origin":"","legend":"\u003cp\u003eFused Image of NIR Band 4 intensity map and the DEM\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/aba9e859d12a746cd8aee678.png"},{"id":59559122,"identity":"8af9e541-b489-4a53-98b2-0e0c7f0bf62f","added_by":"auto","created_at":"2024-07-03 07:57:16","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":64147,"visible":true,"origin":"","legend":"\u003cp\u003eExtracted Hydrogeomorphological Units (HGUs) of the study area\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/a50071bba6f7b57f6b164b7e.png"},{"id":59560764,"identity":"1f73e85e-8acc-415d-a1b0-ad89213f099a","added_by":"auto","created_at":"2024-07-03 08:13:17","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":152918,"visible":true,"origin":"","legend":"\u003cp\u003eHydrogeological Significant Lineaments Trends of the Study Area\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/eba4d19df18ab09d7d84b3b4.png"},{"id":59559128,"identity":"bb2c9bcd-db7d-4c86-bc2d-097303b2e031","added_by":"auto","created_at":"2024-07-03 07:57:17","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":140042,"visible":true,"origin":"","legend":"\u003cp\u003eRose Diagram showing the Hydrogeological Significant Lineaments Trends\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/a3436c539066f8074726d3a1.png"},{"id":59559133,"identity":"173303f1-6c09-41a3-9d38-f22436f96a9b","added_by":"auto","created_at":"2024-07-03 07:57:17","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":54555,"visible":true,"origin":"","legend":"\u003cp\u003eHydrogeological Significant Lineament Density Map of the Study Area\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/011569e4cad196819a3400a7.png"},{"id":59561147,"identity":"a72f9b6d-2ae0-4e9b-95ed-0aedcfb0ad73","added_by":"auto","created_at":"2024-07-03 08:21:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13246321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/86e22a15-faa1-436d-a409-3ba4cbe3da4e.pdf"},{"id":59559139,"identity":"cd84aa37-f8c0-42a0-84c2-8776d9ddf98e","added_by":"auto","created_at":"2024-07-03 07:57:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8006831,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"PaperonGISRSforGroundwaterAssessmentinIlorinupdateduploadSupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4674737/v1/0b2a09dd09a78701852100a8.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSatellite-Enhanced Groundwater Prospect Assessment in a Crystalline Basement Terrain Using Landsat Imageries and DEM Data Around Ilorin Metropolis and Adjacent Areas, Southwest Nigeria\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.0 INTRODUCTION","content":"\u003cp\u003eIlorin southwest, Nigeria has continued to suffer from shortage of portable water. This is because the municipal water capacity of the city increasingly falls short of the geometrical population growth of the Metropolis. For example, in 2007, the manucipal water supply of 12\u0026nbsp;million could only cater for about 6% of the city domestic need which stood as at then at 190\u0026nbsp;million (for 1\u0026nbsp;million population \u0026ndash; Kwara State Diary, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This is based on the fact that the average amount of water used domestically each day by every person is about 190 litres (Hamill and Bell, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Today, the percentage has dwindled to 4.5% because municipal water supply has not increased and population has grown to about 1.4\u0026nbsp;million (requiring about 266\u0026nbsp;million litres excluding industrial and agricultural water needs). The seasonal nature of Asa River further exacerbates the problem. Therefore, residents most of whom are within the low to middle class have over the years taken solace in drilling for groundwater but with low success rate. This is due to the crystalline nature of the geology of the area, confining groundwater to fractures, joint, faults and weathered overburden; thus necessitating thorough geophysical investigation prior drilling. This is because crystalline rocks have poor porosities and permeability and the few existing secondary ones are constrained to few localities with fractures, fissures, joints, faults, etc., altogether called lineaments. Geophysical survey is useful in locating such potential areas. However, it is not economical and too expensive for the most of the poor residents of the city to embark on numerous geophysical surveys until a positive location is delineated. Hence there is need to carry out large scale preliminary investigation as a reconnaissance for geophysical survey in order to suggest promising localities for groundwater development.\u003c/p\u003e \u003cp\u003eAdvances in space technology have made the measurement of earth properties and related quantities associated with the subsurface earth phenomena around morphological units, tectonic plate boundaries geologic boundaries, faults, fissures, and shear zones possible without necessary physical contact with them (Huang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zoran et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lei et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Osotuyi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Osotuyi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Teme and Oni (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) used remote sensing techniques to detect groundwater flow in fractured media in a hard rock terrain in Nigeria. They were able to use aerial photography and radar imagery to adequately locate and delineate the extent and frequency of these fracture systems thus making it possible for the siting of productive boreholes at appropriately predetermined localities within the basement areas. Edet et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) utilized the lineament analysis for groundwater exploration in the Precambrian Oban Massif and Obudu Plateau in the southeastern part of Nigeria. Gradual progressive changes involving the increase and attenuation of the thermal energy have been, before and post-seismic events have been reported using satellite Thermal Infrared (TIR) around the Federal Capital Territory in Nigeria (Osotuyi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Osotuyi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Edet et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) employed an analysis of lineaments extracted from aerial photography for groundwater exploration in the Precambrian Oban Massif and Obudu Plateau, Southeastern Nigeria. They concluded that the Plateau is characterised by high lineament density while the Oban Massif shows low lineament density. Sander and Chesley (1996) utilised Landsat-TM, SPOT, and infrared photography to study a groundwater project in Ghana. These data were interpreted for linear vegetation, drainage, and bedrock features that would indicate underlying transmissive fracture zones, in order to develop optimal strategies for future well siting. Solomon (2003) adopted an integrated approach with remote sensing, Geographic Information Systems (GIS), and traditional fieldwork techniques to assess the groundwater potential in the central highlands of Eritrea. Digitally enhanced colour composites and panchromatic images of Landsat TM and SPOT were interpreted to produce thematic maps such as lithology and lineaments. The potential of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for lithological and lineament mapping was also evaluated. Sahu and Sahoo (2006) utilised remote sensing techniques for delineating groundwater in the drought-prone Bonai area of India, preparing hydro-geomorphological and lineament maps by digital image processing across different hydro-geomorphologic units to understand the aquifer system in the area. Mogaji et al. (2011) conducted GIS analysis in the hard-rock terrain areas of Ondo State, Southwestern Nigeria, to determine the impact of rock types on the variability of aquifer characteristics and their influence on the hydrogeologic systems. Bera and Bandyopadhyay (2012) utilised an integrated remote sensing and GIS methodology to develop and test the evaluation of groundwater resources of a watershed in West Bengal and the adjoining Jharkhand State. They utilised IRS 1D LISS-III satellite data with other datasets, existing maps, and field observation data to extract information on the hydro-geomorphic features. Akinluyi (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) adopted an integration of geological, geophysical, and remotely sensed data in regional groundwater evaluation in the basement complex of Ondo State, Southwestern Nigeria. He utilised topographic maps, Landsat Mapper ETM+, and SPOT-XS ASTER DEM to prepare thematic layers including hydro-lineament density, hydro-lineament intersection density, and hydrogeomorphology. Bayowa (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) conducted a regional groundwater potential study within the basement complex terrain of Ekiti State, Nigeria, considering geologic, geomorphological units, lineament, and lineament intersections in a typical basement complex terrain, using satellite imagery such as Digital Elevation Model (DEM), Landsat Thematic Mapper, and Vertical Electrical Sounding (VES) to determine the hydrogeomorphic conditions and hydrogeologic lineament density distribution. Previous works have established that groundwater potential areas can classified by identifying fractured zones of higher permeability in crystalline rocks, thus making it possible to generate a regional potential map (Masoud \u0026amp; Koike \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) by utilizing satellite imageries to extract hydrogeologic lineaments and hydrogeomophlogical data for regional groundwater potential classification.\u003c/p\u003e \u003cp\u003eThis study therefore intends to carry out a detailed remotely-sensed reconnaissance assessment of groundwater potentials using satellite technology involving Landsat Enhanced Thematic Mapper (ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (herein after referred to as ASTER GDEM) in and around Ilorin Metropolis, Asa local government, and their adjacent areas in Southwestern Nigeria. From these sets of data, results involving hydrogeomophlogical classification, hydrogeological lineament density, and regional groundwater potential map, are intended to be commissioned and commended to environment planners, hydrologists, geologists, geophysicists, engineers, climate scientists, water management experts, policy makers, and other interest groups. This is with the purpose of making informed scientific and policy decisions for well-informed and precision-guided groundwater campaigns in the densely populated and built-up areas like Ilorin City. Our study conclude that lineament density could provide some streamlined guidance for detailed exploration of groundwater potential and occurrence in Crystaline Basement terrains.\u003c/p\u003e"},{"header":"2.0 STUDY BACKGROUND","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Location, Population, Vegetation, and Climate\u003c/h2\u003e \u003cp\u003eThe study area cut across four local government council areas including Ilorin, the capital city of Kwara State, Nigeria. The area constituting the study location lies within Longitudes 4\u0026deg; 28\u0026rsquo;E and 4\u0026deg; 34.7\u0026rsquo; E and Latitudes 8\u0026deg; 25.5\u0026rsquo;N and 8\u0026deg; 32.5\u0026rsquo;N, covering about 246 km\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Ilorin is one of the fastest growing urban centers in Nigeria with a rapid increase in the population the city since its emergence as the state capital in 1976. The 1991 census puts the population of the city at about 572,172 (Ifabiyi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The study area is in the savannah region of Nigeria located within the tropical climate belt of West Africa (Osotuyi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) with humidity ranging between 60 and 89% and mean annual temperature falling between 27 and 37\u0026deg;C. The weather condition in the region is of two broad types i.e. rainy season and dry season (Osotuyi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The rainy season commences around March and ends in October, while the dry season begins in November and ends in March with mean annual rainfall between 75 to 112 cm. The maximum rainfall are recorded in June or September after the usual break that occurs from July to August. The average wind speed is about 7km/h (4 mph or 4 knots).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Regional Geology, Local Geology, and Hydrogeology\u003c/h2\u003e \u003cp\u003eThe study area falls within the Basement Complex area of southwestern part of Nigeria which are Precambrian to Lower Paleozoic in age (Rahaman, 1976; Falebita et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Precambrian Crystalline Basement Complex (CBC) rocks in Ilorin and adjacent areas consist of gneisses and migmatites; metasediments (that is, schists, quartzites and metavolcanics), few Pan-African (older) granites and late-stage minor pegmatitic and aplitic intrusives (Oluyide et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). According to Olasehinde et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), Ilorin is situated on the undifferentiated Precambrian Basement Complex rocks of granitic and metamorphic origin. The rock units form part of the regional southwestern highlands of Nigeria running NW-SE parallel to the River Niger (Offodile, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Olasehinde et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Salako et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Osotuyi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ojo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The profile of the basement terrain comprises the topsoil, lateritic layer (occurring as un-indurated or hard indurated duricrust), weathered, slightly-weathered and fresh (fractured or unfractured) crystalline basement rocks. The oldest rocks in the area comprise gneiss complex whose principal member is biotite-hornblende gneiss with intercalated amphibolites, underlying large part of the study area (Olasehinde et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Other rock types are the migmatites, older granite consisting mainly porphiritic granite, gneiss and granite-gneiss and quartz-schist. However, the study area comprises maily migmatites, granites and some quartzites such (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMajor factors influencing the hydrogeology of an area include rock types, geologic structures, and climate of the area (Ademilua, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This is because the geological formations and the associated structures underlying the area determine the types of aquifer to be encountered and the means of recharging them. Also, the climate determines the amount and rate of groundwater recharge of the aquifer and the watershed (Maliu, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Lewis, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Thus, the hydrogeology of an area is considered under two parts: (i) surface water and (ii) groundwater. Most crystalline rocks of Nigeria are situated in areas of high relief, as a result, run-off is high and infiltration rates are very low. The basement rocks are either devoid or of low primary porosity and permeability. Most often, the occurrence of groundwater in this type of terrain is localized (Olorunfemi and Fasuyi, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Salako et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Oni et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In a typical basement complex environment as the study area, Olorunfemi and Fasuyi (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) identified five (5) aquifer systems which are: (i) weathered aquifer, (ii) weathered/fractured (unconfined) aquifer, (iii) weathered/fractured (confined) aquifer, (iv) weathered/fractured (unconfined)/fractured (confined) aquifer, and (v) fractured (confined) aquifers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Physiography and Drainage\u003c/h2\u003e \u003cp\u003eThe landscape ranges in elevation in the western part from 225 m to 360 m above sea level and in the eastern part from 220 m to 365 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Sobi hill is the dominant landform. It is an inselberg, and it is the highest point in the city (394 m above sea level). The drainage system in Ilorin is dendritic in nature and is dominated by Asa River, which flows from north to south and divides the city into two parts, the western and eastern parts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The western part represents the indigenous area. The eastern part coincides with the modern layout. Other rivers that drain the city are: Agba, Alalubosa, Oyun, Osere, Aluko (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 DATA AND METHODOLGY","content":"\u003cp\u003eThe methodology adopted in this study is described in the workflow shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In this work, our interest is constrained to application of remote sensing and Geographic Information Systems (GIS) using geomorphological terrain data and lineaments extraction for groundwater investigation. Two major types of satellite imageries commonly used in hydrogeology were employed in this research (Bayowa, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e and Akinluyi \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), viz: Landsat images and Digital Elevation models. The materials acquired include topographic and administrative maps of Kwara State; satellite imageries including Landsat Enhanced Thematic Mapper Plus (ETM+) of year 2000, with Instantaneous Field of View (IFOV) sensor having 30 m spatial resolution of path 190, row 54; and ASTER GDEM Digital Elevation Model (DEM) (Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) of ASTGTM_N08E004 and ASTGTM_N08E005 covering the area around the four Local Government Areas (LGAs). The topographic and administrative map of the entire region were procured from the Kwara State office of the Surveyor General. The satellite imageries were downloaded from the NASA online data repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://asterweb.jpl.nasa.gov/gdem.asp\u003c/span\u003e\u003cspan address=\"http://asterweb.jpl.nasa.gov/gdem.asp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To analyse the data using ArcGIS geospatial analysis interface, we created a GIS database for the groundwater investigation project in the ArcCatalog. Shapefile delimiting the boundaries of the study area was created using Arcmap 10.0 before being stored in the GIS Database. Data including geological map, topographic map, and the administrative map demarcating the boundaries of LGAs under consideration were geo-referenced, digitized (vectorised), and stored in the created database. Satellite Imageries (Landsat - 8 bands comprising visible and near infrared bands and GDEM) covering the study area were sourced and imported as raster into ArcGIS (Figure S1), clipped to the geometry of the study area, and thereafter stored into the database. The satellite imageries were subjected to various image preprocessing for geometric effect removal and error correction due atmospheric interference. These preprocessing and processing are covered under two broad categories including image enhancement and image transformations which are hereafter succinctly discussed.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Image Enhancement\u003c/h2\u003e \u003cp\u003eThis was embarked upon to improve the resolution of the imagery to assist in visual interpretation and analysis. Of the several types that are available, Edge Enhancement Filter (EEF) and Raster Pan-Sharpening (RPS) were carried out. The EEF, also known as \u0026lsquo;sharpening\u0026rsquo; filters, was utilized for lineament detection (Meijerink et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Subsequent stages of image enhancement involved the use of Pan Sharpening filters and an Autorectification algorithm (for elevation data like DEMs), which are applied to transformed images based on our study objective, although the latter showed no significant impact on the DEM\u0026rsquo;s resolution and was thus not displayed. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrates the impact of edge enhancement, achieved through the use of a high pass filter, on the Near Infrared (NIR) and Thermal Infrared (TIR) bands of Landsat ETM\u0026thinsp;+\u0026thinsp;and on the ASTER GDEM of the study area. These processes play a crucial role in the extraction of lineaments which is one of the focus of this study. The second preprocessing stage of image enhancement involves the Raster Pan-Sharpening. The Pan Sharpened Raster merges high-resolution pan chromatic raster data with a lower-resolution multiband dataset, resulting in an RGB raster band combination that matches the resolution of the panchromatic raster. The ESRI Model was used for pan sharpening in this study due to its superior image contrast for the study area. As shown in Figure S2 (e), the pan sharpened image of Band 752 enhances the visibility of surficial elements, making linear features more distinct and drainage channels, which are toned blue, easier to identify.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Image Transformations\u003c/h2\u003e \u003cp\u003eUnlike image enhancement operations which are normally applied only to a single channel of data at a time, image transformations usually involve combined processing of data for multiple spectral bands. Target identification is better enhanced by increasing the contrast of the features present on the imagery. Landsat ETM\u0026thinsp;+\u0026thinsp;image transformations employed in this study including Colour composite bands combination, principal components analysis (PCA), Normalised Difference Vegetation Index (NDVI), together with Landsat and DEM Image Fusion and DEM Hill Shade are highlighted below.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Band Combination\u003c/h2\u003e \u003cp\u003eThe three primary colours, Red, Green, and Blue (RGB), represent the full spectrum of colours perceivable by humans. These colours can be assigned to selected bands or transformed images, creating a \u0026lsquo;false colour\u0026rsquo; composite, such as the commonly used composite band 432 (Figure S3), generated by assigning Red to the Near Infrared (NIR) band, Green to the red spectral band, and Blue to the green spectral band. This band is particularly useful for land use analysis, especially in dry regions where some spectral bands can be highly correlated; thus, it\u0026rsquo;s beneficial to use three least correlated spectral bands for a colour composite, a selection made in this study through visual inspection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Principal Component (PC) Transform\u003c/h2\u003e \u003cp\u003eThe PC transform is an image enhancement technique used to highlight non-hydrogeologically significant structures for isolation and lineament structure enhancement. Figures\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(a-b) show the combination of PC 1\u0026ndash;6 of composite band 123457 and PC1, PC2, and PC3 of Band 123457, which are crucial for extracting lineaments due to their display of general linear features. The PCs of Band 752 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(c-d)) and Band 541 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(e-f)) are also significant, with the former better displaying non-hydrogeologically significant lineaments like roads and airport runways, and the latter showcasing hydrogeologically significant lineaments, many of which control drainage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Normalized Difference Vegetation Index (NDVI)\u003c/h2\u003e \u003cp\u003eThe NDVI is a metric for quantifying green vegetation, useful in differentiating between forested and deforested regions. Its calculation hinges on the relative reflectance of near-infrared and red light. The NDVI is the ratio of the difference between the reflectance in the red and near infrared bands divided by the total reflectance in these two bands. It is computed by using the equation:\u003c/p\u003e \u003cp\u003eNDVI = (band 4 (NIR) \u0026ndash; band 3 (Red))/ (band 4 (NIR)\u0026thinsp;+\u0026thinsp;band 3 (Red)).\u003c/p\u003e \u003cp\u003eThe NDVI image was created using the Raster Calculator and Spatial Analyst toolbar in ArcMap 10.0, with the resulting map added to the workspace as \u0026ldquo;Initial NDVI\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(a)). The map was then classified using both unsupervised (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(b)) and supervised methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(c)), with the latter requiring training for land cover recognition and the application of the Reclassify tool. The Direct Arc Toolbox method was also used to create signature files with predefined classes, revealing that most drainage channels are associated with vegetation and that vegetated lineaments are hydrogeologically significant (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e(d) and 8(e)). Once the NDVI analysis is completed, the quality of information extracted from the study area is well-enhanced.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Image Fusion\u003c/h2\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates that the wider arms of the drainage channels, depicted in lemon green, are primarily located on two elevated upper pediments, one in the west and the other in the southeast. The lower courses of the channels traverse the pediplains and valleys, represented in green. The built-up area, including the city center, is shown in purple, with two reservoirs (Asa and Agba Dams) appearing in a very thick, dark red. The fused DEM suggests that surface runoff flows from the western and southeastern uplands through the pediplain into the valleys and dambos, potentially indicating the direction of groundwater flow (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5 Hill Shade\u003c/h2\u003e \u003cp\u003eThis is the transformation of a Digital Elevation Model (DEM) into a hill-shaded relief imagery using the Spatial Analyst (Surface) tool in ArcGIS, with specific sun angle and azimuth parameters (Figure S4). The algorithm for hill shade uses the sun illumination angle to highlight both natural and manmade features. However, the lineaments extracted from this imagery may not be hydrogeologically significant and should be compared with those from other imageries to ensure accuracy in the extraction procedure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Generation of Hydrogeomorphological Thematic Map\u003c/h2\u003e \u003cp\u003eThe Digital Elevation Model (ASTER GDEM, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) covering the entire area were acquired, analysed, classified and reclassified, and used to produce the Hydrogeomorphological Units (HGUs) information on the basement terrain of the study area. Using the reclassify spatial analysis tool in ArcMap (ArcGIS 10), the generated maps are then classified into five hydrogeomorphological units or zones namely: dambos (valley), pediplain, lower pediment, upper pediment, and the residual (hill). and saved into the geo-database as classified hydrogeomorphological map.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Generation of Lineament Density Thematic Map\u003c/h2\u003e \u003cp\u003eThe satellite imageries EMT\u0026thinsp;+\u0026thinsp;NIR Band (Figure S1) and ASTER GDEM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) were subjected to various stages of image processing analyses in order to delineate lineaments. The eight Landsat EMT\u0026thinsp;+\u0026thinsp;Bands collected in the visible, Near Infrared electromagnetic spectrum, and the panchromatic intensity were subjected to image processing. These processes include Band Combination, Pan sharpening, Normalized Difference Vegetation Index (NDVI), and Principal Component Analysis (PCA) to enhance the linear features for lineament extraction (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Both the Landsat EMT\u0026thinsp;+\u0026thinsp;NIR Band and\u003c/p\u003e\u003cp\u003eASTER GDEM acquired over the study area were also edge enhanced in ArcGIS for the same purpose (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The Digital Elevation Model (ASTER GDEM) was resampled to the resolution of the Landsat panchromatic image and fused together along with Landsat EMT\u0026thinsp;+\u0026thinsp;NIR Band in order to delineate linear features (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The Digital Elevation Model (ASTER GDEM) was also used to produce the hill shade for the study area (Figure S4) in an effort to enhancing and delineating more subtle lineaments, with extraction done to exclude artifact, following Bayowa (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) procedures. Each processing operation was overlain on Digital Elevation Model (DEM) in order to validate the origin of the lineaments. The extracted lineaments were classified by drawing their rose diagrams that will indicate the orientation and frequency of occurrence of the extracted lineaments. The extracted lineaments were then digitized and presented as a lineament density Map. All delineated lineament from the various imageries were then extracted and saved in to the created geo-database as general linear features. The hydrogeologically significant lineaments were afterwards extracted from the general lineaments using the information from thermal band, NDVI (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), Google Earth, and ground truthing. All the extracted features were saved into the geo-database as the hydrogeologically significant lineaments. The maps previously generated were then used to produce the lineament density map in ArcGIS. The lineament density map was classified into five hydrogeological units or zones (as above) and saved into the geo-database.\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0 RESULT AND DISCUSSION","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Geomorphological Characteristics\u003c/h2\u003e \u003cp\u003eThe study area is broadly of a low relief terrain typified by the remains of the residual hill, presence of pediments which has been further differentiated into lower and upper pediments, pediplains and dambos within the valleys (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Geomorphologically, the entire study area could be classified into three Zones: Zone A, B, and C (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Zones A and C are characterized by relics of escarpments which have been weathered and differentiated into lower and upper pediments. Zone A, in the western part, runs from south to north, while Zone C in the eastern flank of the study area, terminates beyond the middle portion of the study area. Spatially, Zone A lies predominantly within Asa (LGA), with its lower pediments creeping into Ilorin West LGA in the center, while Zone C stretches through the southern part of Ilorin East and the western portion of Ilorin South LGA. Zone B at the center, is a depression that is slightly narrow in the south, but spreads out in the north. It is sandwiched between zones A and C. Zone B is morphologically differentiated into pediplain and Dambos. Zone B lies mainly within Ilorin West LGA with its pediplain extending into Asa in the west and Ilorin East LGA in the east. The northwestern flank of Zone A shows similar morphology with zone B. Residual hills (white tone on Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) is majorly evident in the southeast (Zone C, Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) and in the north-central area (Sobi Hill in Zone B, Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Residual Hill and Inselberg\u003c/h2\u003e \u003cp\u003eResidual hills and inselbergs have elevation range of 355 to 390 m, and occurs within migmatites and granite gneiss-underlain areas, without any sign of their occurrence within the quartzite. This geomorphological unit is not prevalent within the study area. Remnants of the residual hill are mainly evident in the southeast (Zone C) and in the north-central (Sobi Hill in Zone B). They are ranked as the least prolific geomorphological unit in terms of groundwater potential, and it is assigned with a groundwater rating of 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In general, areas with many outcrops are not considered favourable for groundwater exploration due to shallow weathering depths and low recharge of possible fractures. It is also observed to coincide with areas with lesser presence of lineaments (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Residual hills have the lowest groundwater rating among the geomorphological units.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHydrogeomophological Units, their Elevation Ranges, Intercepted Lithologies, Groundwater Potential Rating and Hydrogeomorphological Ranking\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydrogeomophological\u003c/p\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColour Code in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevation Above Sea Level (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroundwater Potential Rating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrogeomorphological\u003c/p\u003e \u003cp\u003eRanking\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDambos\u003c/p\u003e \u003cp\u003e(in valleys)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlue / Dark tone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156\u0026ndash;270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePediplain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYellow/Light-Brown tone to\u003c/p\u003e \u003cp\u003eGrayish-Green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270\u0026ndash;305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower Pediment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrown /Dull Red\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e305\u0026ndash;330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper Pediment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurple/ Red\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330\u0026ndash;355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual Hill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite /\u003c/p\u003e \u003cp\u003eLight Red\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e355\u0026ndash;390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Pediment\u003c/h2\u003e \u003cp\u003eThe pediments are the gently sloping undulating surfaces with or without a veneer of weathered/soil materials usually formed at the foot of a mountain and often dotted with rock outcrops (Bayowa, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This geomorphological unit generally has low overburden. Thus, groundwater conditions are poor and limited to some large fractures. This might be the result of erosion under semi-arid conditions of a weathered mantle formed earlier under more humid conditions, when the regolith expanded vertically and laterally (Meijerink et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The pediments in the study area show zonation (catena or topo-sequence; Meijerink et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) into an Upper and Lower Pediments, as is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Pediments in hard rock country often have poor groundwater storage capability, especially when the pediment consists of a true rock-cut surface (Leopold et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1964\u003c/span\u003e). Also, the presence of thin sheet-wash deposits can obliterates fractures; and because soil is shallow and elevation gradient is high, runoff is high and infiltration is low, resulting in low recharge.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Upper Pediment\u003c/h2\u003e \u003cp\u003eThis geomorphological unit has an elevation range of 330\u0026ndash;355 m and occurs mainly within migmatites and granite gneiss underlain area. Except where saturated fractures exist, the upper pediment shows poor groundwater potential because of its associated relatively high elevation gradient, high run off and low infiltration when compared to other geomorphological units. It is the second lowest ranked geomorphological unit in term of groundwater potential and it is assigned a rating of 2 within the entire hydrogeomorphological units.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.1.4 Lower Pediment\u003c/h2\u003e \u003cp\u003eThis geomorphological unit has an elevation range of 305\u0026ndash;330 m. It also occurs within migmatites and granite gneiss underlain area. It has higher groundwater potential than the upper pediment because of its relatively lower elevation gradient. The lower pediment is the third ranked geomorphological unit in term of groundwater potential and it is assigned a rating of 3 within the entire hydrogeomorphological units.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.1.5 Pediplain\u003c/h2\u003e \u003cp\u003eThis geomorphological unit has an elevation range of 270\u0026ndash;305 m and has imprints of all the three lithologies. It is dominant in the central part and at the two northern corners (east and west) of the study area. The planar configuration of the geomorphological unit gives room for high infiltration and profound weathering; yet, less erosion, thus, giving room for a high groundwater potential. It is assigned a rating of 4 within the entire hydrogeomorphological units.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.1.6 Dambo\u003c/h2\u003e \u003cp\u003eThis geomorphological unit has an elevation range of 156\u0026ndash;270 m and has no significant imprint in granite gneiss underlain area. McFarlane (1989) described a Dambo as the seasonally waterlogged bottomland of the land surface, irregularly lowered by a process termed \u0026lsquo;etch-planation\u0026rsquo;. This involves differential leaching by infiltrated water to the extent that the saprolite collapses, leaving a thick residuum. The dambo-catenas in the crystalline regions of sub-Saharan Africa has attracted the attention of hydrogeologists and geomorphologists (Mackel, 1985; McFarlane, 1989; Wolski 1999, and other authors) probably because of the seasonal presence of groundwater at the surface. In the study area, the interfluves consist of light textured soils and saprolite that offer opportunity for recharge. This hydrogeomorphological unit is characterized by vegetation even in the dry season, no matter how little, which must have been supported by groundwater. Three occurrences of the dambos are evident within the study area with the dominant one occurring within zone B (narrow in the south and widening out towards the north). The other two are restricted to the two northern corners of study area, i.e. northwest of zone A and northeast of zone C respectively. The dambos occur mainly within migmatites and quartzite (in lower central portion of zone B) without any sign of its occurrence in the granite gneiss. Dambo is the highest ranked and most prolific geomorphological unit in term of groundwater potential and it is assigned a rating of 5 within the entire hydrogeomorphological units.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.2 HYDROGEOLOGICAL CHARACTERISTICS\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Hydrogeologically Significant Lineament Map\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows the hydrogeologically significant lineament map prepared by removing all lineaments that fall on hills, ridges and those on streams and river channels which are presumed not to be structurally controlled in the study area. The Rose Diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e) shows that there are four predominant sets of hydrogeologically significant lineaments which are closely related to tectonically developed features. The first set of the lineaments trends in the N-S, the second NNE-SSW; the third set trends NE-SW and the fourth set trends in the NW-SE direction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Lineament Density Map\u003c/h2\u003e \u003cp\u003eLineament density map is a measure of cluster of linear features in a particular area. From the hydrogeologically significant lineaments (i.e. faults fissures, joints etc. with groundwater prospect) map, the lineament density map (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e) was generated using the ArcGIS 10.0. Lineament density map is one of the important thematic maps prepared from lineaments, which are critically used in groundwater studies related to hard rock terrain (Subba Rao, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Krishnamurthy et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The peaks in the lineament density contour maps are the places of interest for groundwater resource development. Areas with high lineament density excluding (the residual hill environment) are good for groundwater development (Haridas et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). From the generated lineament density map (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e), the moderate to high lineament densities are dominant in the central part of the study area, while the western, southern and eastern parts show very low to low lineament densities. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the lineament densities, the intercepted lithologies, the groundwater potential rating and the groundwater potential ranking for each lineament density class within the study area.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroundwater Prospect of the Study Area based on Lineament Density\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLineaments DensityColour Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLineamentabundancein thestudy area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLineaments Density\u003c/p\u003e \u003cp\u003eRange (Km/Sq. Km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroundwater Potential Ranking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLineament Ground-waterRanking\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDark Blue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 \u0026minus;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u0026ndash;2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight Blue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50\u0026ndash;3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLemon Green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.75\u0026ndash;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00\u0026ndash;5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5.0 CONCLUSION AND RECOMMENDATION","content":"\u003cp\u003eGeomorphological analysis of the Digital Elevation Model (DEM) of study area showed that the area comprised of the Dambos (valleys) (156\u0026ndash;270 m a.s.l), the pediplain (270\u0026ndash;305 m a.s.l), lower pediment (305\u0026ndash;330 m a.s.l), upper pediment (330\u0026ndash;355 m a.s.l) and the residual hills (355\u0026ndash;390 m a.s.l). The geological/hydrogeological investigation by the analysis of the extracted lineaments showed four predominant sets of lineaments. The first set of the lineaments trends in the N-S, the second in the NNE-SSW direction; the third set trends NE-SW while the fourth set trends in the NW-SE direction. The hydro-significant density map generated from hydro-significant lineament map revealed five (5) lineament cluster zones in the range of 0.00\u0026ndash;1.25, 1.25\u0026ndash;2.50, 2.50\u0026ndash;3.75, 3.75\u0026ndash;5.00 and 5.00\u0026ndash;5.60 Km/SqKm. From the generated Lineament density map, lineaments within the western central parts of the study area are characterized by moderate to very high lineament with several lineaments intersection, while the northwestern and southeastern corners show lineament densities of low to high. The moderate to high lineament densities are dominant in the central part of the study area, while the western, southern and eastern parts show very low to low lineament densities. On the basis of geology, the areas underlain by quartzite show low to moderate groundwater potential. Migmatites and Granite Gneiss show groundwater rating predominantly of very low to low. However, some localized areas with high lineament densities show moderate to high groundwater potential in migmatites; whereas, on Granite Gneiss, very low groundwater potential zone is extensive, as seen in Ilorin South LGA. By cross evaluation and integration of the geological, the hydrogeomophogical, and the lineaments density map of the study area, we concluded that the groundwater potential of the study area is generally very low to low. However, there are pockets of areas with moderate groundwater potential and very few localities with high groundwater. It is recommended that detailed pre drilled geophysical survey be carried out in order to further and better understand the hydrogeological characteristics of the study area in an effort to affirm the findings of this research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors received no funding was received in carrying out this study.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe authors appreciate the objective feedbacks and suggestions received from the anonymous reviewers which have immensely helped to improve the quality of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdemilua, O. L. (1997). A Geoelectric and Geologic Evaluation of Groundwater potential of Ekiti and Ondo States, Southwestern, Nigeria. Unpublished M.Sc.Thesis, Dept. of Geology, Obafemi Awolowo University, Ile-Ife, Nigeria. pp. 1- 67. \u003c/li\u003e\n\u003cli\u003eAjibade, A. 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Hydrogeology Journal, Vol. 15(1), pp. 71-74.\u003c/li\u003e\n\u003cli\u003eSubba Rao, N. (1992). Factors Affecting Optimum Development of Groundwater in Crystalline Terrain of the Eastern Ghats, Visakhapatnam Area, Andhra Pradesh, India, Journal of Geological Society of India, 40(5), pp. 462-467. \u003c/li\u003e\n\u003cli\u003eTeme, S. C. and Oni, S. F. (1991). Delineation of Groundwater flow in Fractured Media through Remote Sensing Techniques to detect Groundwater flow in Fractured Mediain Hard Rock Terrains, some Nigeria cases. Journal of African Earth Sciences (12)3: 461-466.\u003c/li\u003e\n\u003cli\u003eHuang, J., Mao, F., Zhou, W., Zhu, X. (2008). Satellite Thermal IR associated with Wenchuan earthquake in China using Modis Data. The 14th World Conference on Earthquake Engineering, October 12-17, 2008, Beijing, China, 6.\u003c/li\u003e\n\u003cli\u003eLei, L., Tingjun, Z., Tiejun, W., Xiaoming, Z. (2018). Evaluation of Collection-6 MODIS Land Surface Temperature Product Using Multi-Year Ground Measurements in an Arid Area of Northwest China. Remote Sensing 10(11), 1852. https://doi.org/10.3390/ rs10111852. \u003c/li\u003e\n\u003cli\u003eZoran, M. A., Savastru, R. S., Savastru, D. M. (2014). Satellite thermal infrared anomalies associated with strong earthquakes in the Vrancea area of Romania. Open Geoscience 1, 606\u0026ndash;617. https:// doi.org/10.1515/geo-2015-0046.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Groundwater potential zones, Crystalline Basement terrains, Remote Sensing (RS), Geographical Information System (GIS), DEM, Landsat Imageries, Lineaments, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-4674737/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4674737/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAn assessment of the Crystalline Basement Complex (CBC) terrain of the province around Ilorin, Southwestern Nigeria, was carried out to evaluate its groundwater potential. The study aimed at investigating the hydrogeomorphological and geological/hydrogeological characteristics of the study area. This was with a view to classifying the study area into different groundwater potential zones, in order to delineate and recommend prospective areas for subsequent detailed geophysical study and drilling campaign. Drainage map, topographic map, and geological maps of the study area were acquired and integrated with Satellite imageries comprising Landsat Enhanced Thematic Mapper Plus (ETM+) 2000 and ASTER GDEM covering the area, and processed using the ArcGIS 10.4 software. The hydro-geomorphological map and hydrogeologic lineament density maps were generated from the processed remotely sensed data. Results from the processed Digital Elevation Model (DEM) showed five distinct hydrogeomophic units which include: Dambos (valleys) (156\u0026ndash;270 m a.s.l), the Pediplain (270\u0026ndash;305 m a.s.l), lower Pediment (305\u0026ndash;330 m a.s.l), upper Pediment (330\u0026ndash;355 m a.s.l) and the residual hills (355\u0026ndash;390 m a.s.l). The hydrogeologic lineament trends show N-S, NNE-SSW, NE-SW and NW-SE trends. The hydro-significant lineament density map reveal five (5) lineament cluster zones in the range of 0.00\u0026ndash;1.25, 1.25\u0026ndash;2.50, 2.50\u0026ndash;3.75, 3.75\u0026ndash;5.00 and 5.00\u0026ndash;5.60 km per km\u003csup\u003e2\u003c/sup\u003e. Cross examination of the hydro-geomorphological map and lineament density map, in a Geographical Information System (GIS) environment, enabled the characterization of the study area into five different classes of very low, low, moderate, high and very high groundwater potential zones. It is concluded that groundwater potential of the area around Ilorin was generally of very low to low rating. However, there are few areas with moderate groundwater potential.\u003c/p\u003e","manuscriptTitle":"Satellite-Enhanced Groundwater Prospect Assessment in a Crystalline Basement Terrain Using Landsat Imageries and DEM Data Around Ilorin Metropolis and Adjacent Areas, Southwest Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-03 07:57:07","doi":"10.21203/rs.3.rs-4674737/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"43d2cf46-8b2a-4a0e-b033-c611bbcd686a","owner":[],"postedDate":"July 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34019439,"name":"Hydrology"},{"id":34019440,"name":"Geographic Information Systems"},{"id":34019441,"name":"Geophysics"},{"id":34019442,"name":"Geology"}],"tags":[],"updatedAt":"2024-07-03T07:57:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-03 07:57:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4674737","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4674737","identity":"rs-4674737","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}