A GIS and remote sensing based evaluation of

Arab J Geosci DOI 10.1007/s12517-011-0512-3

ORIGINAL PAPER

A GIS and remote sensing based evaluation of groundwater potential zones in a hard rock terrain of Vaigai sub-basin, India Prabu Pothiraj & Baskaran Rajagopalan

Received: 13 October 2011 / Accepted: 13 December 2011 # Saudi Society for Geosciences 2012

Abstract In this paper, remote sensing, geographic information systems (GIS) and fieldwork techniques were combined to study the groundwater conditions in Vaigai basin, Tamilnadu. Several digital image processing techniques, including standard color composites, intensity–hue–saturation transformation and decorrelation stretch were applied to map rock types. Remote sensing data were interpreted to produce lithological and lineament maps such as geology, geomorphology, soil hydrological group, land use/land cover and drainage map were prepared and analyzed using GIS Arc Map GIS Raster Calculator module as geomorphology× 12+drainage×9+lineament×5+geology×8+land use×2+ relief×4. The final cumulative map generated by applying the above equation had weight values ranging from 0.315 to 4.515. The overall results demonstrate that the use of remote sensing and GIS provide potentially powerful tools to study groundwater resources and design a suitable exploration plan, the thematic maps for the study area. Keywords Groundwater potential . Spatial analyst . Raster calculator . Remote sensing . GIS . Vaigai sub-basin

Introduction Geospatial technology is a rapid and cost-effective tool in producing valuable data on geology, geomorphology, lineaments slope, etc. that helps in deciphering groundwater potential zone. Groundwater is a dynamic and replenishable natural resource but in hard rock terrain availability of P. Pothiraj (*) : B. Rajagopalan Department of Industries and Earth Sciences, Tamil University, Thanjavur, Tamil Nadu 613010, India e-mail: [email protected]

groundwater is of limited extent and its occurrence is essentially confined to fractured and weathered zones (Saraf and Choudhary 1998). Exploration and utilization of groundwater especially in hard rock terrains, requires thorough understanding of geology, geomorphology and lineaments of an area, which are directly or indirectly controlled the terrain characteristics (Ravindran and Jeyram 1997; Pradeep 1998; Kumar et al. 1999). A systematic integration of these data with follow-up of hydrogeological investigation provides rapid and cost-effective delineation of groundwater potential zones. Although it has been possible to integrate these data visually and delineate groundwater potential zones, however, it becomes time consuming, difficult, and introduces manual error. In recent years, digital technique is used to integrate various data to delineate not only groundwater potential zone but also solve other problems related to groundwater. These various data are prepared in the form of a thematic map using geographical information system (GIS) software tool. These thematic maps are then integrated using “Spatial Analyst” tool. The “Spatial Analyst” tool with mathematical and Boolean operators is then used to develop a model depending on the objective of problem at hand, such as delineation of groundwater potential zones. Analysis of remotely sensed data for drainage, geology, geomorphological, and lineament characteristics of the terrain in an integrated way facilitates effective evaluation of groundwater potential zones. Such attempts have been made in the generation of thematic maps for the delineation of groundwater potential zones in different regions. In recent years, many workers such as Chatterjee and Bhattacharya (1995), Teeuw (1995), Shahid et al. (2000), Goyal et al. (1999), and Saraf and Choudhary (1998) have used the approach of remote sensing and GIS for groundwater exploration and identification of

Arab J Geosci

artificial recharge sites. Imran et al. (2011) and Jaiswal et al. (2003) have used the GIS technique for generation of groundwater prospect zones and towards rural development. Krishnamurthy et al. (1996), Murthy (2000), Obi et al. (2000), and Pratap et al. (2000) have used GIS to delineate groundwater potential zone. Srinivasa and Jugran (2003) have applied GIS for processing and interpretation of groundwater quality data. GIS has also been considered for multicriteria analysis in resource evaluation. Mohammed et al. (2003) have carried out hydrogeomorphological mapping using remote sensing techniques for water resource management around paleochannels. GIS has been applied to groundwater potential modeling by Rokade et al. (2007). In the present study, a similar attempt has been made for qualitative evaluation of groundwater potential zones in the Vaigai sub-basin of Theni district. Analysis of remotely sensed data along with Survey of India topographical maps and collateral information with necessary ground checks are helpful in generating the baseline information for groundwater targeting.

Fig. 1 Location of the study area

Study area The Vaigai sub-basin extends over approximately 849 km2 and lies between 09° 30′ 00″ and 10° 00′ 00″N latitudes and 77° 15′ 10″and 77° 45 00′E longitudes in the western part of Tamilnadu, India (Fig. 1). It originates at the altitude of 1,661 m in the Western Ghats of Tamilnadu in Theni district. The basin is generally hot and dry except during winter season. The maximum and minimum temperature for the basin is 40.7°C and 16.0°C. The area receives an average annual rainfall of about 384 mm. The surface runoff goes to stream as instant flow. Rainfall is the direct recharge source and the irrigation return flow is the indirect source of groundwater in the Vaigai sub-basin. The study area depends mainly on the northeast monsoon rains which are brought by the troughs of low pressure established in the South Bay of Bengal. Most of the farmers depend on the groundwater for their irrigational needs. There are few tanks across these drainages; however, most of these remain dry. In order to manage and develop sustainable scheme, it is vital to delineate the groundwater potential zones.

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Materials and methods Remote sensing The Indian Remote sensing Satellite (IRS) ID, linear image self scanning (LISS) III of geocoded false color composites, generated from the bands 2, 3, and 4 on 1:50,000 scale was used for the present study. The Survey of India toposheet maps 58 G/5, 58 G/6, 58 G/9, and 58 G/10 on a scale of 1:50,000 equal to the corresponding imagery were used for the preparation thematic maps. The imagery was visually interpreted to delineate geomorphologic units and land use/ land cover with the help of standard characteristic image interpretation elements like tone, texture, shape, size, pattern, and association (Table 1). Geology Various digital image processing techniques, including standard color composites, intensity–hue–saturation (IHS) transformation and decorrelation stretch (DS) were applied to map rock types. The statistical technique adopted by Sheffield (1985) was employed to select the most effective three-band color composite image. The band combination 1, 4, and 5 is the best triplet and was used to create color composites with Landsat TM bands 5, 4, and 1

in red, green, and blue, respectively. IHS transformation and DS were also applied to the selected band combination in order to enhance the difference between rock types. Better contrast was obtained due to color enhancement and this facilitated visual discrimination of various rock types. Eleven lithologic units were mapped and could be distinguished by distinct colors in the processed images. These are: pink migmatite, mofic granulite, garnet-biotite-sillimanite-gneiss, charnockite, calc-granulite/limestone, alluvium, hornblend biotite gneiss, calcareous sand and clay, pyroxene granulite, quartz, grey granitic gneiss shown in (Fig. 2). The areal extent of various geologic units is shown in Table 2. Geomorphology Topographic model parameters were calculated from the digital elevation model and used for geomorphologic analysis. The topographic model parameters slope, longitudinal curvature, cross-sectional curvature, plan convexity, and minimum curvature were calculated using a moving window of 5×5 raster elements. They are used as input bands for landform feature classifications. Different combinations of two and three input bands are used to characterize different types of features. The classification results were visually evaluated and reclassified into 15 landform features to generate the geomorphologic map shown in (Fig. 3). These are: ridge type

Table 1 Image characteristics of geomorphologic units and land use/land cover Particulars

Image characteristics (photo elements) Tone

texture

Size

Fine to coarse Medium Fine to coarse

Widespread Irregular Contiguous Small Irregular Scattered small Regular Contiguous

Agricultural land Hill and stream course Stream course

Brown to dark red Brown to dark red

coarse coarse

large small

varying Isolated Irregular Isolated

plain land plain land

Light to darkish grey Light to medium red Light yellow Pinkish Light to red with white patches Light grey to greenish white Light to medium pinkish grey Medium to dark green Greenish red Light to dark blue Light to dark blue

coarse Fine to medium fine Fine to medium Fine to medium Fine to medium Smooth Smooth Smooth Fine Smooth

varying varying varying varying varying

Irregular Irregular Irregular Irregular Irregular Irregular Irregular Irregular Irregular Definite varying

Geomorphologic units BPM Light to dark red BPS Light to dark green VF Light green RH SH Land use/land cover Built-up land Crop land Fallow land Plantation Deciduous forest Degraded forest Land with scrub Land without scrub Barren rocky land Stream Tank

varying varying varying varying varying

Shape

Pattern

Association

Irregular Agricultural land Scattered to contiguous Non- irrigated land Contiguous Non- irrigated land Contiguous Uplands Contiguous forest plantation Contiguous scattered trees Scattered Hill and stream course Scattered Hill and stream course Scattered Hill and stream course Linear Irrigated land Scattered Irrigated land

Arab J Geosci Fig. 2 Geology of the study area

Table 2 Areal extent of geology Soil no

Geology

1 2

Pink Migmatite Mofic Granulite

3 4 5 6 7 8 9 10 11

Garnet-biotite-sillimanite-gneiss Charnockite Calc-granulite/limestone Alluvium Hornblend biotite gneiss Calcareous sand and clay Pyroxene granulite Quartz Grey granitic gneiss

Area

Percent

Weightage

0.947977 0.641613

0.111658 0.075573

10 10

53.52605 335.1003 1.296876 68.6401 303.3971 73.14326 0.526489 9.617911 2.233865

6.3046 39.47 0.152753 8.084817 35.73581 8.615225 0.062013 1.132852 0.263117

10 10 40 10 10 40 10 10 10

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Fig. 3 Geomorphology of the study area

structural hills (large), moderately weathered/moderately buried Pediplain, Pediplain Canal Command, Dome type Denudation Hills (Large), sand dune, shallow buried pediment,

pediment/valley floor, moderate buried pediment, valley fill/ filled-in valley, linear ridge/dyke, dome type residual hills, alluvial fan younger, inselberg, shallow weathered/shallow

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buried pediplain, and fracture valley. Their areal extent is shown in Table 3. Lineament Lineaments are large-scale linear features which expresses itself in terms of topography which is in itself an expression of the underlying structural features. From the groundwater point of view, such features includes valleys controlled by folding, faulting and jointing, hill ranges and ridge lines, abrupt truncation of rocks, straight segments of streams and right angled offsetting of stream courses as these linear features are commonly associated with dislocation and deformation they provide the pathways for groundwater movements. Lineaments are important in rocks where secondary permeability and porosity dominate the intergranular characteristics combine in secondary openings influencing weathering, soil water, and groundwater movements. The fracture zones forms an interlaced network of high transmissivity and acts as groundwater conduits in massive rocks in interfractured areas. The lineament intersection areas are considered to be good groundwater potential zones. The areas with higher lineament density and topographically low elevated grounds are considered to be the best aquifer zones. Lineament has generally been used as an indicative tool for locating potential groundwater zones, but with the present scenario of over-exploitation of the aquifer, characterization of the lineament becomes essential to ensure the possibility of locating potential groundwater zones and managing over-exploited aquifers in hard rock areas. Lineaments are clearly discernible in all Table 3 Areal extent of geomorphology

digitally processed color composites. Most of the linear features are enhanced due to color contrast. Although major lineaments can be detected in the raw image data, most of the finer details are more clearly recognizable in the filtered image. Although lineaments have been identified throughout the area, it is the lineaments in the Pediplain or Valley fill which are considered significant from groundwater occurrence point of view. Those across the denudation hills, residual hills, in the high-drainage density, and high-slope area or in the area occupied by clay zones are of less significance as there could be high runoff along them and these may act only as conduit to transmit infiltrated rainwater. The structure map showing the lineaments of the study area is shown in (Fig. 4); while as the lineament density (in square kilometer) is shown in Fig. 5 (Table 4). Curvature The curvature values in the Vaigai sub-basin vary −18.52 to +6.76 (Fig. 6), indicating the hilly nature of the southeast region of the study area. A positive curvature indicates the surface is upwardly convex at that cell. A negative curvature indicates the surface is upwardly concave at that cell. A value of zero indicates the surface is flat. Slope The digital elevation models (DEM) was generated through ASTER 30 m DEM. Slope layer is obtained

Soil no

Geomorphology

Area

Percent

Weightage

1 2 3 4 5 6 7 8 9 10 11 12 13

Ridge type structural hills (large) Moderately weathered/moderately buried pediplain Pediplain canal command Dome type denudational hills (large) Sand dune Shallow buried pediment Pediment/valley floor Moderate buried pediment Valley fill/filled-in valley Linear ridge/dyke Dome type residual hills Alluvial fan younger Inselberg

466.57954 48.829646 1.5547358 10.867181 0.1068667 18.580152 80.327864 49.440355 6.3657541 4.1227808 6.1475912 0.7752083 0.817424

54.956365 5.7514307 0.1831255 1.2799978 0.0125874 2.1884749 9.4614681 5.8233634 0.7497944 0.4856043 0.7240979 0.0913084 0.0962808

10 40 60 10 60 40 40 40 80 10 10 10 10

14 15

Shallow weathered/shallow buried pediplain Fracture valley

150.78345 3.1879925

17.760124 0.3754997

60 80

Arab J Geosci Fig. 4 Lineament map of the study area

in ArcGIS 3D analyst package. The slope categorized (Table 5) and weightage value given to each slope category (Fig. 7).

subdendridic on gentle slopes. The drainage density (Fig. 9) has been computed using line density tool available in ArcGIS package.

Drainage density

Hydrologic soils

Microlevel drainage pattern has been traced out from the digitally processed image of the study area (Fig. 8). The drainage pattern is subparallel at higher slopes and

Soil characteristics invariably control penetration of surface water into an aquifer system and they are directly related to rates of infiltration, percolation and permeability, 13 different

Arab J Geosci Fig. 5 Lineament density of the study area

categories of soils were classified based on USDA soil taxonomy (Fig. 10; Table 6). Table 4 Lineament density

Land use/land cover

Lineament density

Area

Percent

6

645.6712 173.2376 23.93108 7.731352

76.05079 20.4049 2.818737 0.910642

Weightage 10 20 30 40

The identified land use/land cover features from the IRS imagery of the study area (Table 7) are medium dense forest, dense forest and plantation shrubs, groundnut, cholam and floriculture, reserved forest, barren land and rocky outcrop, paddy, and sugarcane (Fig. 11).

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Fig. 6 Profile curvature of the study area

GIS database development The features of the study area such as topography, soil type, land use pattern and drainage pattern, geology, lineament,

profile curvature and geomorphology were obtained and thematic maps were prepared and generated the attributes for each thematic map were generated. All the thematic maps were superimposed by weighted overlay method using

Arab J Geosci Table 5 Drainage density Drainage density

Area

Percent

6

123.9246 538.5428 185.5191 0.714704

14.59653671 63.43260831 21.85148859 0.084181882

Fig. 7 Slope map of the study area

Weightage 40 30 20 10

spatial analysis tool. The modeling involves delineation of zones of varying groundwater potential based on integration of eight thematic maps in a raster based GIS. The eight parameters considered are: 1. 2. 3. 4. 5.

Geology, Geomorphology, Lineament density, Profile curvature. Land use.

Arab J Geosci Fig. 8 Drainage of the study area

6. Soils. 7. Slope 8. Drainage density Every class in the thematic layers was placed into one of the following categories viz.: (1) good, (2) moderate, (3) moderate to poor, and (4) poor, depending on their level of groundwater potential. Considering their behavior with respect to groundwater control, the different classes were

given suitable values, according to their importance relative to other classes in the same thematic layer. The values assigned to different classes in all thematic layers are given in Tables 5a–c. The values assigned to the geology layer take into account the hydrogeological significance of the rock types. In general, lineaments act as conduits for groundwater flow, and hence are hydrogeologically significant. The values given for lineaments were based primarily on the

Arab J Geosci Fig. 9 Drainage density of the study area

relation of well yields to proximity of lineaments. Accordingly, four classes were defined based on distance from lineaments with decreasing values as the distance from lineaments increase. It is assumed that the intensity of groundwater potential decreases with increasing distance away from the lineaments. This implies that the best chances for groundwater targeting are close to lineaments. Moreover, areas showing negative values for curvature were assigned good potential where as positive values’ areas were considered poor for groundwater potential.

So far geomorphology is concerned, valley fills has been considered as good for groundwater potential, where as inselberg, residual hills, denudation hills were considered as poor. After assigning values for each class in each layer, these four layers were added and the sums were grouped into groundwater potential zones. The highest value that the sum can attain is 460 (80+80+60+80+80+80) and the lowest value is 60 (10+10+10+10+10+10). The minimum of 100 was set as the class interval and all areas with a sum not larger than 100, were considered to be zones of poor

Arab J Geosci Fig. 10 Soils of the study area

groundwater potential. Groundwater potential map was prepared (Fig. 12) by overlaying all the thematic maps.

3. Data integration. Spatial database building

Results and discussion The integration of various thematic maps describing favorable groundwater zones into a single groundwater potential map has been carried out through the application of GIS. It required mainly three steps. 1. Spatial database building; 2. Spatial data analysis and

The tools provided in Arc Catalog have been used to create the scheme for feature data sets, tables, geometric networks, and other items inside the database. The secant method of geodatabase building has been followed. Maps such as geomorphologic map, geological map, and drainage and lineament map have been digitized. After digitization, these maps have been processed for editing of error, dangles, pseudonodes, etc. Attributes to

Arab J Geosci Table 6 Areal extent of Soils Code Description 14 33 44 62 79 80 90 106 110 119 133 142 145

Area

Percent

Moderately deep, well drained, clayey soils on undulating lands moderately eroded 28.61244 3.370135 Deep, well drained, loamy soils on gently sloping O lands, moderately eroded 153.7195 18.10595 Very deep, well drained, clayey soils on gently sloping lands moderately eroded 160.6192 18.91864 Deep, somewhat excessively drained, clayey soils on moderately steeply sloping hill ranges, 83.15354 9.794292 moderately eroded Deep, well drained, gravelly clay soils on moderately sloping, low hills, severely eroded 234.4389 27.61353 Moderately shallow, somewhat excessively drained, gravelly clay soils on undulating isolated hills/ 5.469167 0.644189 hillocks, severely eroded Deep, moderately well drained, calcareous, clayey soils on gently sloping lowlands, slightly eroded 70.21256 8.270031 Very deep, moderately well drained, loamy soils O of nearly level valleys slightly eroded 6.117374 0.720539 Deep, poorly drained, clayey soils on gently sloping lowlands, slightly eroded 51.55544 6.07249 Deep, moderately well drained, calcareous cracking clay soils on nearly level lands, slightly eroded 14.16527 1.668465 Rocky outcrops 22.59606 2.661491 Rocky outcrops; associated with; moderately shallow, well drained, clayey soils on undulating lands, 7.594463 0.894519 moderately eroded Rocky outcrops; associated with; very deep, well drained, loamy soils on moderately steeply sloping, 10.81753 1.274149 high hills and escarpments, severely eroded

these maps have been added. In any coverage, attributes need to be added to available features to distinguish them. Buffering of 100 m in lineament map has been carried out. Spatial data analysis It is an analytical technique associated with the study of locations of geographic phenomena together with their spatial dimension and their associated attributes (like table analysis, classification, polygon classification, and weight classification). The various thematic maps as described above have been converted into raster form. These were then reclassified and assigned suitable weightage following the patterns as used by Srinivasa and Jugran (2003), Krishnamurthy et al. (1996), and Saraf and Choudhary (1998).

Table 7 Areal extent of land use/land cover Land use

Area

Percent

Weightage

Medium dense forest Dense forest and plantation Shrubs Groundnut, cholam and floriculture Reserved forest

78.17482 9.207111 159.8519 18.82671 1.013876 0.11941 62.59224 7.371859

10 10 40 10

275.0845

32.39834

10

Barren land and rocky outcrop 161.4043 Paddy and sugarcane 110.9498

19.00954 13.06722

10 10

Weightage 20 40 40 20 40 20 20 40 60 40 10 10 10

Data integration Digitized vector maps pertaining to chosen parameters, viz. geomorphology, geology, and lineament, were converted to raster data using 23×23 m grid cell size. The raster maps of these parameters were assigned respective theme weight and their class weights. The individual theme weight was multiplied by its respective class weight and then all the raster thematic layers were aggregated in a linear combination equation in ArcMap GIS Raster Calculator module as given here: geomorphology
12 þ drainage
9 þ lineament
5 þ geology
8 þ land use
2 þ relief
4 The final cumulative map generated by applying the above equation had weight values ranging from 0.315 to 4.515. The cumulative map was further reclassified into four categories of groundwater prospects, viz. very good to good, good to moderate, moderate to poor, and poor to very poor. The groundwater potential map thus derived is shown in Fig. 8.

Conclusion Mapping of groundwater resources have been increasingly implemented in recent years because of increased demand for water. The data most commonly available for groundwater study are geological, geomorphologic,

Arab J Geosci Fig. 11 Land use/land cover of the study area

lineament, and land use/land cover information. In this study, we attempted to identify groundwater potential zones using remote sensing and geographic information system techniques in the Mamundiyar basin. In order to demarcate the groundwater potential zones within each thematic layer, an innovative statistical modeling was done using Arc GIS 9.2. To demarcate the groundwater availability of the Mamundiyar basin, various thematic maps such as geological map, lineament map, geomorphology map, and drainage map were prepared from remote sensing

data and topographic maps, geology maps, and geomorphology maps using Arc GIS and ERDAS software; these maps are integrated for preparing groundwater prospects map. The geomorphologic units such as valley fill and buried pediplain are prospective zones for groundwater exploration and development in the study area. Presence of lineaments in the area enhances the potential of these units. The integrated map thus deciphered could be useful for various purposes such as the development of sustainable scheme for groundwater in the area.

Arab J Geosci Fig. 12 Groundwater potential map of the study area

Acknowledgments We thank anonymous reviewers for their constructive review of the manuscript and we are very much grateful to the Editor for the editorial revision. Thanks are due to the GIS lab of Dept. of Earth Sciences, Tamil University for providing the lab facilities.

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