Jump to content


Inactive Members
  • Content Count

  • Joined

  • Last visited

Community Reputation

0 Neutral

About AGaber

  • Rank

Profile Information

  • Gender
  1. Remote Sensing in Groundwater Studies Chapter 2 Remote Sensing in Groundwater Studies Abstract Groundwater study in an area requires the idea of lithological units, structural disposition, geomorphic set-up, surface water condition, vegetation, etc. These can be well understood with the help of remote sensing (RS). It is the study of satellite images and aerial photographs. Satellite images are basically the electromagnetic (e-m) record of broad spectrum (ultraviolet, visible, infrared and microwave regions) by means of instrument such as scanners and cameras located on mobile platform such as satellite or spacecraft. The e-m radiation may come from an artificial source in the satellite or from the target itself if the target happens to be a source of e-m radiation. The radiation travels through the atmosphere being detected at the satellite recorder. The e-m spectrum in given bands can give information on the various targets on the earth. Vegetation, in general, appears green during daytime, because it reflects the green band of visible radiation preferentially, while absorbing other colour bands of the visible radiation. Before geophysical investigation, the RS data give the knowledge of the geological structures. Hence, the geophysicist can focus the survey area from a huge area which is not potential. The RS data are very accurate, fast and reliable as compared to the conventional data collection. Keywords Remote sensing Lithological unit Geomorphic set-up Vegetation Satellite image Aerial photography Electromagnetic radiation Colour band Geological structure 2.1 General Considerations Groundwater study of an area requires knowledge of the nature of lithological units occurring in the area, their structural disposition, geomorphic set-up, surface water conditions and the climate of the area. These can be studied through satellite images and aerial photographs, which provide detailed information about the large part of the surface of the earth in a very short time. Photograph interpretation studies help in the indication of groundwater potential through: © Springer Science+Business Media Singapore 2016 H.P. Patra et al., Groundwater Prospecting and Management, Springer Hydrogeology, DOI 10.1007/978-981-10-1148-1_2 Although remote sensing (RS) data do not directly detect deeper subsurface resources, it has been effectively used in groundwater exploration as RS data aid in drawing inferences on groundwater potentiality of the region indirectly. The fresh water confined to channels of streams and rivers and stored in ponds, lakes and reservoirs is normally considered to form the surface water resources. These sources of surface water are directly detected by satellite RS data as water absorbs most of the radiation in the infrared region, which helps in the delineation of even smaller water bodies. Vegetation, which is easily detected through spectral reflectance, is indicative of the water saturation and moisture of the ground. RS data provide only superficial and inadequate knowledge on the subsurface groundwater resources but help indirectly by giving certain ground information that aid in drawing inferences on groundwater potentiality of the area. Primarily, the infiltration capacity of the soil determines the groundwater potentiality. The speed of infiltration is dependent upon mainly on porosity and permeability of the soil and the velocity of the surface run-off. Infiltration reduces to a great extent for steeply sloping ground surface as the velocity of surface run-off increases sharply. Also a vegetative cover gives a higher infiltration capacity compared to barren lands. Several factors important in the storage of groundwater are: Thus, the RS data followed by ground check give an idea of the probable groundwater potential zones. This should be detailed through surface and subsurface geophysical methods suitable for groundwater exploration. The scientific approach consists of three steps: • RS-based investigations, • Conventional hydrogeological investigations and • Ground geophysical investigations. RS records electromagnetic (e-m) radiation comprising a broad spectrum of wavelengths. This method, therefore, may be called an applied geophysical method in a broad sense and an e-m method in particular. The e-m radiation may come from an artificial source in the satellite or from the target itself if the target happens to be a source of e-m radiation. The radiation travels through the atmosphere being detected at the satellite recorder. The e-m spectrum in given bands can give information on the various targets on the earth. With the advancement in technology, man is changing the face of the earth at a rapid phase. In order to channelize the development in proper direction with due consideration to environmental preservation, a planner needs to have an overall picture of the status of the region and its resources. Conventional methods of data collection are extremely time-consuming and are normally out of phase with the present-day conditions. In such circumstances, earth resource monitoring by space platforms is the only answer. The generation of accurate and reliable information at a cost-effective and short turnaround time coupled with a wide range of earth resource monitoring, prediction of crop yield, estimation of soil moisture conditions, in forestry applications such as wildlife habitat assessment and timber volume estimation. 2.1 General Considerations 9 In geological sciences, it is used in the study of morphology of the earth, and in lithological and structural mapping. Besides this, the technology is also used in locating sites suitable for major reservoirs and in targeting groundwater. 2.2 Remote Sensing RS is the non-contact recording of information about the earth surface, from the ultraviolet, visible, infrared and microwave regions of the e-m spectrum, by means of instruments such as scanners and cameras, located on mobile platforms such as aircraft or spacecraft followed by the analysis of acquired information by means of photograph interpretation techniques, image interpretation and image processing (Sabins 1987). The contact between the remote sensor and the target is through e-m energy (visible, thermal, infrared radiation), force fields (gravity, magnetic) or acoustic waves (sonar). Remote sensors measure the relative variation of these forms of energy that is either emanating from the body or being reflected from it for recognition and detailed studies. For most of the atmospheric and earth surface observations, e-m energy is considered to be the supreme medium for two reasons. Primarily, this is the only form of energy that has the ability to propagate through free space and also a medium. Further, its property to interact with the media and the target in a variety of ways ensures the sensor to capture the subtle variations that exist in the nature of the earth features. 2.3 Remote Sensing Technique Every part of the earth reflects the incident light depending on its optical characteristics. The information which characterizes objects is called “signatures”. Different objects of the earth surface return different amounts of reflected/emitted energy in different wavelengths of the e-m spectrum (Fig. 2.1a) depending on the atmospheric windows (Fig. 2.1b), and this reflectance/emittance from each object depends on the wavelength of the radiation, the molecular structure of the object and its surface conditions. Vegetation, in general, appears green during daytime, because it reflects the green band of visible radiation preferentially, while absorbing other colour bands of the visible radiation. Detection and measurement of these spectral signatures enable identification of surface objects both from airborne and spaceborne platforms. But often, similar spectral response from different surface objects creates spectral confusion leading to misinterpretation and misclassification. This can be avoided by systematic ground data verification. However, spectral variation in reflectance or emittance from objects is not the only characteristic of e-m radiation that helps in establishing their 10 2 Remote Sensing in Groundwater Studies signatures. Signatures, in fact, comprise of any set of observable characteristics which directly or indirectly lead to the identification of an object and its condition. The characteristics are spatial information, temporal (for example, seasonal) variation and polarization effect. The shape, size, texture, pattern, association, for example, are associated with special information. Earth resource satellites collect information about earth’s surface and transmit to the ground receiving stations. After carrying out initial corrections, two types of data products are generated for resource study. These are: (i) visual imagery hard copy and (ii) computer compatible tapes (CCTs). These data are processed and interpreted for the identification and classification of different objects of the earth. Each satellite system is composed of a scanner with sensors. The sensors are made up of detectors. The scanner is the entire data acquisition system, such as the Landsat Thematic Mapper scanner or the SPOT panchromatic scanner (Lillesand and Kiefer 1987). In a satellite system, the total width of the area on the ground covered by the scanner called the “swath width”, or width of the total field of view (FOV). FOV Fig. 2.1 Spectral characteristics of energy sources, atmospheric effects and sensing system (after Lillesand and Kiefer 1987). a Energy sources. b Atmospheric transmittance. c Common remote sensing system 2.3 Remote Sensing Technique 11 differs from IFOV (instantaneous field of view); in that, the IFOV is a measure of the FOV of each detector. The FOV is a measure of the FOV of all the detectors combined together. A sensor of a satellite is a device that gathers energy, converts it to a signal and presents it in a form suitable for obtaining information about the environment. A detector is the device in a sensor system that records e-m radiation. For example, in sensor system on the Landsat Thematic Mapper scanner there are 16 detectors for each wavelength band. The common RS systems given in Fig. 2.1c have several characteristics: (i) They have circular orbits that go from north to south and south to north; (ii) They have Sun-synchronous orbits, meaning that they rotate around the Earth at the same rate as the Earth rotates on its axis, so data are always collected at the same local time of day over the same region; (iii) They record e-m radiation in one or more bands; and (iv) Their scanner produces nadir (the area on the ground directly beneath the scanner detectors) views. 2.4 Satellite Image RS image is available in the form of hard copy paper prints visual imagery, in different scales, which are generated from the digital data collected by the sensors. In satellite, the sensor measures radiations reflected/emitted from the earth’s surface. The altitude of the satellite and the size of the detector element define the spatial resolution or pixel (picture element) size. The value at each pixel on a satellite image represents the total amount of radiation reaching the sensor from the ground. Two-dimensional array of pixel values constitute a digital image of the scene. Each pixel value represents average radiance over a defined smaller area within a scene. The size of the pixel area affects the representation of the details within the scene. As the pixel area is reduced, more and more scene detail is preserved in the digital representation and this governs the spatial resolution. The continuous radiance of the scene is, therefore, quantized into discrete grey levels in the digital image. The data are thus routinely recorded in digital form by space sensors, which are transmitted to the ground stations. These data are reprocessed by the computer to generate image for interpretation. The data can be displayed in suitable scales by appropriate computer processing. 2.4.1 Data There are many data acquisition options available. These options range from photography to aerial sensors using film, to sophisticated satellite scanners. 12 2 Remote Sensing in Groundwater Studies A satellite system with detectors which produce digital data may be preferable for the reasons: (i) the digital data can be easily processed and analysed by a computer, (ii) the satellite is in orbit around the Earth, so the same area can be covered on a regular basis for change detection, (iii) once the satellite is launched, the cost of data acquisition is less than that for the aircraft data, (iv) satellites have stable geometry, meaning that there is less chance for distortion or skew in the final image. A wide variety of RS data are acquired from different types of satellites, viz. Landsat, SPOT, IRS-IB, IRS-1C, IRS-1D, NOAA, LISS-IV Pan merged data through Cartosat-2 and Resourcesat-2. Landsat has a 15-m panchromatic sensor and a 30 m enhance. Thematic Mapper sensor is with 7 bands. SPOT has a 10-m panchromatic sensor and a 20-m multispectral sensor with 3 bands. IRS-IB has a 36-m multispectral sensor with 4 bands. LISS-IV sensor on-board satellite has the spatial resolution of 5.8 m as that of IRS 1D PAN, but it has enhanced spectral resolution. LISS-IV sensor consists of three spectral bands in the green, red and near-infrared regions of the e-m field. It can be tilted up to ±26° in the across-track direction, thereby providing a revisit period of 5 days and 70 km 70 km stereo pairs. This opens a new field of microlevel applications. Remotely sensed raw data are not projected onto a plane. Therefore, rectification is necessary to project the data conforming to a map projection system before processing. 2.4.2 Resolution Resolution is a broad term commonly used to describe the number of pixel we can display on a display device or the area on the ground that a pixel represents in an image file. Four distinct types of resolutions are associated with RS data discussed below. Spectral resolution: Spectral resolution refers to the specific wavelength intervals in the e-m spectrum that a sensor can record (Simonett 1983). For example, Band 1 of Landsat Thematic Mapper sensor records energy between 0.45 and 0.53 μm in the visible part of the spectrum. Spatial resolution: Spatial resolution is a measure of the smallest object that can be resolved by the sensor, or the area on the ground represented by each pixel (Simonett 1983). The finer the resolution, the lower, the number. For instance, a spatial resolution of 36 m is coarser than a spatial resolution of 20 m. Radiometric resolution: Radiometric resolution refers to the dynamic range, or the numbers of possible data file values in each band. This is referred to by the number of bits the recorded energy is divided into. For instance, in 8-bit data, the values range from 0 to 255 for each pixel, but in 7-bit data, the values for each pixel range from only 0 to 128. Temporal resolution: Temporal resolution refers to how often a sensor obtains imagery of a particular area. For example, the IRS-1B satellite can view the same 2.4 Satellite Image 13 area of the globe once every 22 days. Temporal resolution is an important factor to be considered when performing change detection studies. 2.5 Image Processing The image processing is the manipulation of digital image data, including (but not limited to) rectification, enhancement and classification operations. The purpose of image processing is to generate thematic maps from satellite images. There is a large number of image processing softwares available from different vendors, namely ERDAS, IDRISI for both commercial and educational purposes either on personal or mainframe computers with array/vector processing capabilities (Fig. 2.2). 2.5.1 Image Interpretation Image interpretation is a complex process of physical and psychological activities occurring in a sequence of time. The sequence begins with the detection and identification of objects and later by their measurements. Images are then considered in terms of information and final deductions to be confirmed by ground checks to avoid misclassification. different grey tones. These grey tones often fail to provide the interpreter a clear perception of objects, whereas true-colour or false-colour imagery increases interpretability by providing a subtle tonal contrast between them. Tonal contrast can be enhanced or reduced optically or by enhancement techniques on computers. (ii) Texture: It is defined as a repetition of basic pattern. Texture in the image is due to tonal repetitions in a group of objects that are often too small to be discernible. It creates a visual impression of surface roughness or smoothness of objects and is a useful photo-element in image interpretation. (iii) Pattern: It refers to the spatial arrangements of surface features which are characteristics of both natural and man-made objects. Similar features under similar environmental conditions reflect similar patterns of recurrence. More often, patterns also reflect association, e.g., intensity of drainage pattern shows its relation with rock types, soil texture, rainfall, run-off, etc. (iv) Size: It refers to the spatial dimension of objects on ground. Size of an object is a function of scale of the image or photograph and is also measurable. There are different objects with varying sizes and shapes. (v) Shape: It refers to physical form of an object and is also a function of scale of the image or photograph. Size and shape are interrelated. In the image, shape refers to plan or top view of the object as seen by the satellite. Shape can be irregular, regular and uniform. (vi) Shadow: These are cast due to Sun’s illumination angle, size and shape of the object or sensor viewing angle. The shape and profile of shadow help in identifying different surface objects, e.g. nature of hill slopes, apparent relief. (vii) Location: The geographic site and location of the object often provide clues for identifying objects and understanding their genesis. (viii) Association: It refers to situation of the object with respect to other neighbouring and surface features. 2.5.2 Image Enhancement Improvement in the quality of an image in the context of a particular application is called image enhancement. Contrast stretching, band combination, data compression, edge enhancement and filtering, and colour display are some of the well-established techniques in image enhancement. Contrast enhancement: In order to accommodate the tonal variations of a variety of environments spread over the earth, satellite sensors are designed to record a wide range of radiation intensity within every band width. Due to this, while scanning a particular scene, signals are recorded only within a small portion of this wider scale. This imagery when viewed in its raw state will exhibit a low contrast, often leading to difficulties in feature recognition. Therefore, to increase the 2.5 Image Processing 15 contrast, the recorded digital numbers are rescaled to a new longer scale following certain statistical criteria (Fig. 2.3). There are different methods of contrast enhancement, such as linear stretch, histogram equalization, binary stretch, logarithmic stretch, and Gaussian stretch. Band combination: Band addition, subtraction and rationing are some of the common band combinations. Different bands of the same image is subtracted or rationed to suppress the details common to the two images and enhance details that are different. Band combination is also performed on geometrically registered multitemporal scenes to monitor the changes in the environment, such as the effect of floods, extension of forest fire, urban sprawl and in agriculture. Principal component analysis (PCA): A major problem frequently encountered in the analysis of multispectral data is extensive interband correlation. High correlation indicates high degree of redundancy among the data, i.e., each band data convey essentially similar information. PCA is a technique designed to remove or reduce such redundancy in multidimensional data. The PCA compresses the whole of information contained in the original multiband data into fewer channels or components with zero correlation and are often more interpretable (Drury 1987). Fig. 2.3 Principle of contrast stretch enhancement. a Histogram. b No stretch. c Linear stretch. d Histogram stretch. e Spatial stretch 16 2 Remote Sensing in Groundwater Studies PCA is the most widely used and popular technique among the digital enhancement methods (Radhakrishnan et al. 1992). Edge enhancement and filtering: The edge enhancement is an operation which helps the analyst in achieving edge-highlighted image (Radhakrishnan et al. 1992). According to Jensen (1986), the edge enhancement operation delineates the edges and thereby makes the shapes and details comprising the image more conspicuous and perhaps easier to analyse. Edge enhancements are the techniques for enhancing sharp changes in the grey levels, such as lineaments, roads, canals, field boundaries and contacts of two land use classes. In geology, they are advantageous for enhancing joints, faults, lineaments and fractures. Edge enhancement is achieved by a process called “spatial filtering”. Spatial filters emphasize or de-emphasize image data of various “spatial frequencies”. Spatial frequencies refer to the roughness of the tonal variations occurring in an image. Image areas of high spatial frequency are tonally rough. That is, the grey levels in these areas change abruptly over a relatively small number of pixel (e.g. across roads, linear features). Smooth image areas are those of low spatial frequency, where grey levels vary only gradually over a relatively large number of pixel transformation of an image, where the transformation depends not only on the grey levels of the pixel concerned, but also on the grey levels of the neighbouring pixels. Spatial filtering is a context-dependent operation that alters the grey level of pixel according to its relationship with the grey level of a pixel in the immediate vicinity (Schowengerdt 1983). Usually, different combinations of low-pass filtering and high-pass filtering are used in image processing by convolution using convolution windows. The window is moved over the input image processing by convolution using convolution windows. The window is moved over the input image from pixel to pixel, performing a discrete mathematical function transforming the original pixel values to new ones. The windows or filters may be rectangular or square. Each location in the box filter is given a certain weight. Many types and sizes of filters can be designed by changing the window size and varying the weights. The edges may be enhanced either by directional or non-directional edge enhancement techniques. Various edge-enhancing operators have been reported to detect linear patterns from images (Wang and Newkirk 1998; Holyer and Peckinpaugh 1989). 2.5.3 Image Classification and Generation of Thematic Maps Multispectral classification is the process of sorting pixels into a finite number of individual classes, or categories of data, based on their data file values. If a pixel satisfies a certain set of criteria, the pixel is assigned to the class that corresponds to the criterion. The classification process breaks down into two parts—training and classifying. First, the computer system must be trained to recognize patterns in the data. 2.5 Image Processing 17 Training is the process of defining the criteria by which these patterns are recognized (Hord 1982). The result of training is a set of signatures, which are statistical criteria for a set of proposed classes. Training can be performed with either a supervised or an unsupervised method. Pixels of an image area are then sorted into classes based on the signatures, by the use of a classification decision rule. The decision rule is a mathematical algorithm that uses particular statistics such as maximum likelihood and Bayesian methods to sort the pixels. Supervised classification: This type of classification is more closely controlled by the user. In this process, user selects the pixels that represent the pattern with the help of other sources, such as aerial photographs, ground truth data or maps. Knowledge of the data, and of the classes desired, is required before classification. By identifying patterns, user can train the computer system to identify pixel with similar characteristics. If the classification is accurate, each resulting class represents an area of interest within the data that corresponds to the pattern user originally identified. Unsupervised classification: This type of classification is more computerautomated. It allows the user to specify some parameters which the computer uses to uncover statistical pattern that are inherent in the data. These patterns do not necessarily correspond to directly meaningful characteristics of the scene, such as contiguous, easily recognized areas of a particular soil type or land use. They are simple groups of pixels with similar spectral characteristics. In some cases, it may be more important to identify groups of pixels with similar spectral characteristics than it is to sort pixel into recognizable categories. Unsupervised classification is dependent upon the data itself for the definition of classes. The method is usually used when less is known about the data before classification. It is the responsibility of the user, after classification, to attach meanings to resulting classes to generate thematic maps. Unsupervised classification is only useful if the classes can be appropriately interpreted. 2.5.4 Case Study The study area is Midnapur District, West Bengal (location map in Fig. 2.4, reproduced from Nath et al.2000) covers 631 km2. It is a typical soft rock area having hydrogeological conditions favourable for shallow groundwater reserve. For the groundwater investigations, thematic maps of surficial geology and drainage pattern are prepared from IRS-1B LISS-II data. The data have spatial and radiometric resolutions of 36 m and 7 bits, respectively. In the first step, raw image data are projected onto a plane using Everest projection method and taken into Universal Transverse Mercator (UTM) coordinate system for further processing. Enhancement of the image is accomplished by using PCA on bands 1, 2 and 3 for the generation of geological map of the area. PCA 1, 2 and 3 are assigned the colours red, green and blue, respectively, for generating a false-colour composite (FCC) of the image. Three distinct features are identified in the enhanced image 18 2 Remote Sensing in Groundwater Studies from their tones and textures. From ground check-up, these features are found to be laterite, older alluvium and newer alluvium. The spectral signature of these features are used to classify (supervised) the image and generate the thematic map of the geology of the area, as shown in Fig. 2.4a. For the preparation of thematic map of drainage pattern of the study area, a standard FCC (using bands 1, 2 and 3) is generated. The water bodies are identified by their blue tone and fine texture. The fresh water that is confined to channels of streams and rivers and accumulated in ponds and lakes are normally considered as surface water resources. The infiltration capacity of the earth surface, coupled with evapotranspiration of the region, controls the volume of water in a channel. Further, the channel size and its gradient also control the water holding capacity. In order to harvest this resource, barrages or dams are constructed across the rivers and artificial lakes or reservoirs are formed from where the water supply is regulated through a network of canals. Several criteria are taken into consideration while selecting suitable sites for dam construction. The first and most important factor in dam designing depends on normal aerial coverage and lower evaporation loss. Further, they are beneficial as the area of submergence is nominal and also the cost of dam construction works out to be far cheaper. The nature of the valley bottom material and the catchment area is another important criteria. Impervious and consolidated basement crystalline rocks are supposed to be excellent valley bottom material as the loss of water due to percolation is minimized. Further, catchment area having good vegetative cover and soils resistant to erosive forces are considered good as the rate of siltation of the reservoir will be low and also the loss of water due to infiltration will be less. The ability of recording the earth-related information in narrow wavelength bands coupled with synoptic coverage makes the satellite data, a potential tool in the hands of the interpreter in deriving the required information for locating hydel projects. A careful observation of the satellite imagery helps in determining the width of the valley, size of the upstream catchment and in evaluating the total area of submergence and loss of vegetative and forest cover due to submergence, etc. Further, by coupling the satellite data with other field inputs it is possible to extract information regarding the nature of valley floor material, lineaments criss-crossing the area, erodability of the catchment material, area occupied by the artificial reservoir and finally, in estimating the total area to be irrigated by the hydel project. 2.6.2 Groundwater Exploration Although remotely sensed data often provide only a superficial and inadequate knowledge on the subsurface resources, it is time and again used, quite effectively, in the exploration of groundwater. This is because, the satellite imageries help in 20 2 Remote Sensing in Groundwater Studies acquiring certain ground information that aid in drawing inferences on groundwater potential of a region. This section examines the basic requirements of groundwater accumulation and the hydrogeomorphological features that aid in the exploration for groundwater resources using satellite RS data. The water to be stored beneath the surface of the earth as groundwater requires an intricate balance between many factors. Primarily, the infiltration capacity of the soil determines the groundwater potential. The speed of infiltration is dependent upon, mainly, the porosity and permeability of the soil and the velocity of the surface run-off. If the surface run-off is extremely high due to steeply sloping nature of the ground then, even if the soil is porous and permeable, the soil infiltration capacity is reduced to a great extent. Another point to be noted here is the part played by vegetation. It has been found that the surfaces covered with abundant vegetative cover have better infiltration capacity than barren lands. The thickness of the permeable layer is another very important factor that determines the storage of groundwater. While making inferences about the groundwater potential of an area using the satellite data, an interpreter concentrates mainly on the geomorphic units that he can observe on the imagery. Based on the nature of origin, the geomorphic units can be grouped into five main classes, namely (i) fluvial, (ii) denudational, (iii) structural, (iv) aeolian and (v) marine. Geomorphic Units of Fluvial Origin Alluvial plains, flood plains, alluvial fans, deltaic plains, river cut-offs, bajada, wadi (dry river bed) are some of the geomorphic features that have come into existence due to fluvial process. All these features can be mapped easily from the satellite imagery. All these features occupy the valley portion and are composed of loose deposits of permeable material like sand or clay. The groundwater prospects range from excellent to moderately good. Geomorphic Units of Denudational Origin The surface of the earth is constantly being acted upon by different kinds of exogenetic forces, such as wind, water and ice. These forces tend to bring about both physical and chemical weathering of the bed rocks and also transport and deposit the weathered debris in certain locations on the earth. The complete phenomenon is referred to as denudational process, and this gives rise to many characteristic landforms. The earth’s crust is made up of several kinds of material and when the exogenetic process operates in a region certain resistant rocks are left out without much damage. These rocks will be appearing as hills of bare rock dotting a plain land. They are called denudational hills and inselbergs. They can be observed in satellite imagery as bare rock outcrops and they are normally devoid of any water, as they are totally made up of compact crystalline rock. 2.6 Applications 21 The gently inclined surfaces that are formed in front of major slopes during the process of erosion are the pediments. They form the zone of recession due to valley formation. Sometimes by the process of valley formation and widening, a few adjoining pediments will merge to give rise to a wider plain—the pediplain. Usually, they are found to be either poor or at the most moderately good as far as their groundwater prospects are concerned. Under humid tropical conditions, the crystalline rocks are subjected to intense chemical weathering, giving rise to lateritic uplands. Although the laterites are soft, permeable and porous, their location at the ridge tops prevents them from holding sufficient water resources, against the gravitation force, to be considered as good groundwater prospecting zones. On the standard FCC, they appear as bright light brown spots or lines. The eroded material that has been deposited in the valleys gives rise to piedmont and talus cones. Piedmonts are the alluvial fans that are found at the foot of the hills or mountain chains. Normally, they are composed of fine silts and clay and are considered to be very good zones of groundwater accumulation. These are readily visible in standard FCC as triangular zones of high reflectance at the foot of hills. Talus cones are heaps of cobble and boulder, found at the foot of hills. They are the result of glacial action, and due to the absence of finer sediments, they are not suitable for holding any moisture or groundwater. Geomorphic Units of Structural Origin Due to the tectonic readjustments that are taking place within the crust of the earth, many features are formed. The most important features of this category are structural hills and valleys, fractured plateau, mesa and butte. Mesa and butte are the resistant rock outcrops of volcanic origin, consolidated in nature and do not qualify as rich groundwater potential zones. The structural ridges are also considered to be poor groundwater zones. On the contrary, if these features contain sets of fractures or faults then they qualify as good sources of groundwater, this is because the numerous fractures and faults act as conduits for groundwater movement. While faulting, due to the abrasion of the opposite blocks a zone of breccia is formed and this zone is considered to be extremely rich in groundwater. In a standard FCC, the joints and structural hills and valleys can be readily mapped. The lineaments and faults are also observed on the imagery as faint lines of straight or curved nature. Sometimes, they are also located by the abrupt tonal contrast in the imagery. In cases where the lineaments are not readily visible, then image enhancement techniques like edge enhancement, using high-pass filters, have yielded good results. In fact it is easy to map the lineaments of fairly large dimensions from the digitally enhanced satellite imagery rather than by following the conventional geophysical methods. Once the lineaments are traced from the satellite imagery, then the conventional methods are adopted to fix their location with accuracy on the ground. 22 2 Remote Sensing in Groundwater Studies Geomorphic Units of Aeolian Origin Most of the features that form the desert topography fall into this category. All except the playa (dry lake) qualify as zones of very low potentiality for groundwater prospecting. Geomorphic Units of Marine Origin Coastal plains, salt flats, mud flats, beach/sand bars and lagoons are the features formed due to marine processes. Most of the features can be mapped easily from the satellite imageries using their characteristic spectral signatures and spatial associations. Except a few broad coastal plains, all the other units are rich in saline water and, therefore, are not of much use, as far as their resource potential is considered. 2.6.3 Monitoring of Freshwater Submarine Springs Gardino and Tonelli (1983) used RS to detect freshwater submarine springs in the coastal areas of Italy, Sicily and Sardinia. About seven hundred such springs have been studied along a coastline of 1500 km. Isotherm maps were prepared for all the coastal inflows. Ground checks were carried out to distinguish spring flows from pollution seepage discharges. Isotherm maps have also been used to compute the likely yield on the basis of thermal balance between water from the spring and the sea. Aerial thermal surveys have been found to be quite useful in locating rapidly and cheaply the regional carbonate aquifers discharging reasonably good amount of water into the sea. 2.6.4 Water Table Depths for Aquifers in Deserts The possibility of estimating shallow groundwater table depths through remotely sensed thermal infrared data was studied by Menenti (1983). In arid zones, both surface and groundwater reservoirs lose water through evaporation. This aspect of the problem, particularly the evaporation rate, was analysed cheaply in the Fezzan region of Libya (Menenti 1983). Evaporation loss through the playas (dry lakes) was estimated by combining ground experiments with remotely sensed data. An infrared line scraping (IRLS) airborne survey with adequate agri-meteorological support led to the assessment of shallow water table depth and evaporation rate in the deserts. 2.6 Applications 23 2.6.5 Lineaments from IRS LiSS H Satellite Data Prasad and Sivaraj (2000) used IRS LiSS-II satellite data and aerial photographs to locate structurally controlled weaker zones, i.e., lineaments suitable for groundwater accumulation in the Nileshwar river basin areas of the state of Kerala (India). Geology of the Area Nileshwar river flows over a length of 46 km and joins the Laccadive Sea through Karingotte river. This river drains an area of 190 km2 and is bounded within latitudes 12° 13′ and 12° 23′N and longitudes 75° 05′ and 75° 17′E. The basin is covered by hard rocks with only fringes of alluvium along the coast. Basin areas are characterized by charnockites of Precambrian age, laterites of Pliestocene age and the alluvium (Prasad and Sivaraj 2000). The lineaments obtained from aerial photographs and IRS LISS satellite data are the surface manifestations of the linear feature like faults joints and fractures. The nature of two sets lineaments with general trend along NW-SE and NE-SW are shown in Fig. 2.6. Observation The wells existing near the lineaments are found to have a perennial shallow groundwater source. Groundwater potentiality of high order is indicated where lineament runs along/across the alluvial zones with several lineaments intersecting each other. However, it has been suggested that further field investigations involving drilling and yield test be carried out for detailing and confirmation (Prasad and Sivaraj 2000). The availability of spaceborne data gave an opportunity to several geologists to apply it in regional-scale mapping of structural features, thereby leading to the discovery of several hitherto unknown lineaments, faults and folds in areas considered to be reasonably well mapped. When this information was correlated with known mineral deposits of the area, a clear relation between the lineaments and the zone of mineralization and groundwater accumulation could be established. This experiment convinced the geological community, beyond doubt, the efficiency of the spaceborne data in providing the information that is so vital in updating the existing mineralogical maps. After the initial enthusiasm, geologists slowly started exploring the potential of space derived data in tackling other geological problems. But, geological mapping with spaceborne data is not as easy as forest or land use mapping as most of the geological features of interest are not readily visible on the initial imagery and requires the application of a series of interactive image enhancement techniques. 2.7.1 Geomorphology Geomorphology is one of the principal subdisciplines of geology and deals with the study of the surface configuration of the earth. Here, the landforms are described, classified and an attempt is made to explain the origin and development of the present-day landforms and their relationship to tectonics. Earlier, the geomorphologist had heavily depended on air photographs for acquiring the information on earth feature, but now with the availability of the various kinds of satellite imageries such as visible, near-infrared, thermal infrared and radar imageries at regular intervals the geomorphologist is better equipped for mapping and monitoring the various changes that are taking place with regard to the size and shape of the landforms, slope of the terrain, river courses and the drainage networks. All these data have provided an insight into the geomorphologist in understanding of the processes that are shaping the earth and also in predicting the future trend of the changes. 2.7.2 Geological Mapping Geological mapping comprises of recording information on the extent and distribution of different rock types and their structural deformation, through satellite imageries and subsequent ground checks. 26 2 Remote Sensing in Groundwater Studies Lithological Mapping The diversity in the spectral reflectance properties of the various geological materials serves as basis for lithological discrimination using satellite data. For example, most of the acidic or felsic rocks (rocks composed of light minerals) show reflectance values between 25 and 50 % in the wavelength region of 0.5–1.1 m and in the same region most of the mafic rocks (rocks composed of heavy minerals) have reflectance <25 %. However, in practice it is not very easy to discriminate rock types, due to the soil and vegetation cover. Recently, it has been found that the thermal infrared imagery is best suited for the discrimination of consolidated rock outcrops from the surrounding as their radiation emissions are much higher than the unconsolidated soil. Further, the differences in the spatial domain such as spread of the outcrop, drainage pattern of the region, drainage density and associated vegetation also help in inferring the nature of the rock type. In case of sedimentary terrain, the discontinuities existing between beds of differing characters can be distinguished, again by their differing spectral signature such as variation in colour, texture, tone and the nature of vegetation, by mapping the ridge and furrow system of semi-parallel nature formed due to the differential erosion of the beds in a sedimentary basin the nature and orientation of the beds can be deciphered. Drainage patterns, such as trellis, rectangular, parallel, also furnish information on the attitude of beds and lineaments. Structural Mapping Structural study involves the mapping of linear features representing major faults or joints (lineaments), detection of unconformities, mapping of bedding planes and folds of regional extent. Satellite imageries provide the most useful single tool for initiating a regional analysis of large-scale tectonism and structural deformation as they provide synoptic views of large areas at a constant low-azimuth Sun angle, thereby creating an apparent relief and accentuating minor variation in the morphology. Lineaments, by definition, are two-dimensional geomorphological features, presumably reflecting the subsurface tectonic phenomena, of mappable dimension possessing rectilinear or lightly curvilinear characteristics. They are considered to be weak tracts representing, zones of mineral enrichment or groundwater potential zones. In an imagery, they are recognized by abrupt changes in the tonal contrast from the adjacent features or as lines of pixel with distinctive tones. However, due to certain limitations in the sensor resolution they may not be readily recognizable and in such instances the data have to be further enhanced by applying high-pass digital filter. As discussed earlier, this technique involves the transformation of the raw image to display the quantum of changes that are existing between the adjacent pixels of the imagery rather than their absolute digital values, thereby enhancing the zones of maximum variation—the lineaments. 2.7 Other Geological Applications 27 2.8 Geographic Information System (GIS) Geographic information system (GIS) is a tool to efficiently capture, store, update, manipulate, analyse and display all forms of geographically referenced data. It stores data about the world as a collection of thematic layers, a pictorial representation of which is given in Fig. 2.7, to be linked together in spatial domain using geographic reference system. This simple but extremely powerful and versatile concept has proven invaluable in solving many real-world problems from tracking delivery vehicles, to recording details of planning applications and managing natural resources. The use of GIS in groundwater investigations is growing tremendously. Nowadays, it is used for groundwater potential (Chi and Lee 1994; Krishnamurthy et al. 1996) and vulnerability assessment (Rundquist et al. 1991; Laurent et al. 1998), groundwater modelling (Chieh et al. 1993; Watkins et al. 1996) and management (Hendrix and Buckley 1989). In regional scale, it requires to handle large volume of georeferenced (spatial) and attribute (aspatial) data. Using GIS, one can play around with the data and generate themes as required for specific applications. GIS is an organized collection of computer hardware, software, geographic data and personnel designed to turn the geographic data into information to meet the users’ needs. For example, if the hydrogeological properties of aquifers, their locations and lateral extent of an area are given as input, GIS can manipulate these data into information such as the groundwater potential zones, vulnerability to pollution. GIS is the only system that can produce this type of spatial information not possible by any other means. Fig. 2.7 The real-world geographics represented as a number of layers or them 28 2 Remote Sensing in Groundwater Studies 2.8.1 Basic Components of GIS From the structural point of view, GIS is very similar to conventional database management system (DBMS), except for the fact that the database of GIS is more sophisticated and has the capability to associate and manipulate enormous volume of spatially referenced interrelated data (Fig. 2.8). GIS stores spatial and aspatial data into two different databases. The geocoded spatial data defines an object that has an orientation and relationship with other objects in two- or three-dimensional space. It is also known as topological data, stored in topological database. The data that described the objects are known as attribute data stored in a relational database. GIS links the two databases by maintaining one-to-one relationship between records of object location in the topological database and records of the object attribute in relational database by using end-user defined common identification index or code. GIS uses three types of data to represent a map or any georeferenced data, namely point type, line type and area type or polygon type. It can work with both vector and raster geographic models. The vector model is generally used for describing discrete features, while the raster model is used for continuous features. GIS uses both operational and analysis tools for generating thematic maps. There are several commercial GIS packages available in the industry, namely Arcinfo, Integrated Land and Water Information System (ILWIS) and Earth Resource Data Analysis System (ERDAS) developed by various software vendors. A GIS approach comprises three distinct phases: (I) data acquisition, (2) data processing and (3) data analysis. The data acquisition phase includes establishing control of the data quality, which consists of positional accuracy, and reliability of observation. There are several ways of digitizing map data for its incorporation in a GIS. The data can be directly digitized from the map using a digitizing table or it can be digitized by tracing the outline of required classes on a transparent overlay in image processing software. The latter approach is common for hydrogeological mapping. In the present study, image processing software ERDAS is used for the processing of satellite imagery. The maps are prepared by tracing the outline of the classes of the enhanced image in GIS software Arcinfo by activating the live-link facility between ERDAS and Arcinfo. The slope is calculated from the elevation G I S Results contour given in the topographic sheet of Survey of India. The mapping of slope classes is done by classifying the slope values into different ranges and digitizing the polygons of each class using digitizing table. The net recharge of the area is calculated from the water table fluctuation data. The procedure followed to prepare different thematic maps for the development of a GIS database is shown by the flowchart of Fig. 2.9. 2.9 Application of GIS in Groundwater Groundwater exploration in any terrain is largely controlled by the prevalence and orientation of primary and secondary porosity. The exploration involves delineation and mapping of different lithological and morphological units and identifying quantitative parameters of the drainage network, soil characteristics and slope of the terrain. These parameters play major roles in evaluating hydrogeological parameters, which in turn enable in understanding the groundwater situation in an area. Studying all these parameters in an integrated way facilitates effective groundwater exploration and exploitation. In many developing countries, readily available RS data may comprise a majority of the existing information over local and regional scale. Establishing relationships between features identified in RS data, borehole records, surface geophysical data and other hydrogeological phenomena, is critical GIS database Digitization Net recharge Water table fluctuation data Slope Image interpretation Image Enhancement Photo Interpretation Satellite imagery Aerial photographs Geology Geomorphology Soil Toposheet Existing maps Remote Sensing data Fig. 2.9 Preparation of thematic maps 30 2 Remote Sensing in Groundwater Studies in any strategy aimed at maximizing water development efficiencies. These strategies are developed using a GIS as the unifying element for all collected data. Many studies have been attempted to integrate groundwater controlling parameters, such as geology, landform, soil characteristics, topographic features and quantitative morphometric characteristics. (Ross and Tara 1993; Krishnamurthy et al. 1996; Laurent et al. 1998). The topological data structure of GIS allows the hydrologist to increase the degree of spatial units into the distributed models. Spatial modelling with GIS can be used to extract relevant information, such as slope, watershed limits, and flow path. We will now deal with specific applications of GIS in the present context. 2.9.1 Groundwater in a Soft Rock Area Through GIS: A Case Study The occurrence and movement of groundwater are controlled mainly by porosity and permeability of the surface and underlying lithology. The same lithology forming different geomorphic units will have variable porosity and permeability thereby causing changes in the potential of groundwater, this is also true for same geomorphic unit with variable lithology. The surface hydrological features such as topography, drainage density, water bodies, play important role in groundwater replenishment. High relief and steep slopes impart higher run-off, while the topographic depressions help in an increased infiltration. An area of high drainage density also increases surface run-off compared to a low drainage density area. Surface water bodies such as river, ponds can act as recharge zones enhancing the groundwater potential in the vicinity. Hence, identification and quantization of these features are important in generating groundwater potential model of a particular area. GIS can be effectively used for this purpose to combine different hydrogeological themes objectively and analyse those systematically for demarcating the potential zone. In the present study, an empirical model is developed within the logical condition of GIS for the qualitative assessment of groundwater potential in a soft rock area. It is tested in Midnapur District, West Bengal, India. The results are benchmarked by correlating with the available borehole and pumping test data. Methodology The GIS used hydrogeological settings of an area as the basic mapping units. Seven themes were evaluated, viz. (i) lithology (L), (ii) geomorphology (G), (iii) soil (S), (iv) net recharge (R), (v) drainage density (D), (vi) slope (E) and (vii) surface water body (W). Each theme was assigned a value from 1 to 7 ranges on the basis of its direct control of the groundwater occurrence. Each feature of an individual theme 2.9 Application of GIS in Groundwater 31 was next ranked in the 1–10 scale in the ascending order of hydrogeological significance. The Groundwater Potential Index (GWPI) for an integrated layer was calculated using GIS as follows: GWPI ¼ LwLr þGwGr þSwSr þRwRr þDwDr þEwEr þWwWr ð Þ=Xw ð2:1Þ where w represents the weight of a theme and r the rank of a feature in the theme. GWPI is a dimensionless quantity that helps in indexing the probable groundwater potential zones in an area. Study Area The test site in Midnapur District, West Bengal, India (87° 10′E, 22° 15′N to 87° 22′ 30′′E, 22° 27′ 30′′N), covering an area of 631 km2 falls under Gangetic West Bengal region (Fig. 2.4) with an average annual rainfall of 152 cm and temperature of 31 °C. This forms a typical soft rock area having hydrogeological conditions favourable for shallow groundwater reserve. This was, therefore, best suited for testing the proposed GIS integration tool (Shahid et al. 2000). Preparation of Thematic Maps All the thematic maps were prepared in 1:50,000 scale with a spatial resolution of 0.1 km2 using GIS package Arcinfo. Depending on the relative importance in groundwater exploration, the themes were assigned specific weights. As used by Krishnamurthy et al. (1996), geomorphology was assigned the highest weight of 7 while surface water body the least value of 1. Thematic map preparations and ranking of various features are highlighted below. Lithology: The lithological map of the area was prepared from the standard false-colour composite (FCC) of Indian Remote Sensing (IRS-IB) Linear Image Scanner System (LISS-II) data (Fig. 2.10a). Three types of lithounits were observed in the satellite sensor image, viz. (i) laterite as light bluish tone with coarse texture, (ii) older alluvium as dark bluish tone with fine texture and (iii) newer alluvium as white and red tone with medium texture. Lithounits were ranked on the basis of their groundwater yield capacity. Geomorphology: The geomorphological map of the area was prepared from the hybrid FCC of PCA of Band 1, 2 and 3 as shown in Fig. 2.10b. The following seven geomorphological units were identified in the area: (i) older deltaic formation by reddish brown tone and fine texture, (ii) older filled valley cuts by reddish brown tone and coarse texture in the lateritic formation, (iii) younger deltaic formation by bluish tone and medium to coarse texture, 32 2 Remote Sensing in Groundwater Studies (iv) younger filled valley cuts by bluish tone and medium to coarse texture in older deltaic formation, (v) recent deltaic formation by light yellow tone and fine texture, (vi) hard crust of laterites by reddish brown tone and coarse texture and (vii) mottled clay of laterites by reddish brown tone and fine to medium texture. Depending on the hydrogeological significance, the geomorphic features were ranked. Soil: The soil map of the area as shown in Fig. 2.10c was prepared using RS data, aerial photograph and field investigation. The area is covered by five soil types, viz. (i) sandy loam, (ii) loam, (iii) silty clay, (iv) sandy clayey loam and Net recharge: The net recharge can be calculated from the annual water table fluctuation data in an area. A net recharge of 25 cm and above was ranked 10 following DRASTIC ratings of Aller et al. (1987). The present area was found to be in this class. Drainage Density: Using standard FCC, drainage map of the area was prepared for developing the thematic map of the drainage density as presented in Fig. 2.10d. The features of drainage density were ranked in the 1–10 by Krishnamurthy et al. (1966). Slope: The elevation contours in the topographic sheet No. 73(N/7) of Survey of India helped us in generating the slope thematic map (Fig. 2.10e), each feature of which was ranked following the DRASTIC ratings. Surface water body: Surface water body thematic map (Fig. 2.10f) was generated from standard FCC. Although there is no yardstick as to what extent the surface water bodies can recharge in the immediate vicinity, we had chosen two buffer zones with radii 25 and 75 m and ranked them as 6 and 3, respectively, in our present analysis. Using the above thematic maps, the GIS integration was performed. Integration and Modelling The rank of each thematic map was scaled by the weight of that theme. All the thematic maps were then registered with one another through ground control points and integrated step by step using the normalized aggregation method in GIS for Table 2.1 Assigned weightage for the layers Sl. no. Theme Attribute Rel. weightage 1 Hydrogeomorphology Valley fill 120 BPP-D 90 BPP-M 60 BPP-S/PP 30 2. Overburden thickness >25 m 140 15–25 m 105 05–15 m 70 <5 m 35 3. Lineament NW-SE orientation 20 NE-SW orientation 10 4 Slope 0–1 % 10 1–3 % 5 Abbreviation—BPP-D Buried Pediplain-Deep, BPP-M Buried Pediplain-Medium, BPP-S Buried Pediplain-Shallow and PP Pediplain 34 2 Remote Sensing in Groundwater Studies computing GWPI of each feature. The evolved thematic map of groundwater potential of the area is displayed in Fig. 2.11a. Field Verification The accuracy of the estimates from the GIS model was determined with the existing borehole and pumping test data. The locations of boreholes along with the lithosection and pumping test sites (T1-T6) are shown on the GWPI map given in Fig. 2.11b. Lithology sections obtained from the boreholes clearly show that Fig. 2.11 a Thematic map of GWPI model depicting groundwater potential, and b locations of boreholes and pumping wells in the study area with available lithosection 2.9 Application of GIS in Groundwater 35 approximately 10-m-thick shallow aquifer of coarse sand is present in the area where GWPI is greater than or equal to 8. Approximately 8-m-thick shallow sandy aquifer is occupying in the zone where GWPI is in between 6 and 8. A thin fine sand and morum sand layer can be detected in the zone where GWPI is less than 6. Concluding Remarks A model is, therefore, developed to assess the groundwater potential of a soft rock area by integrating seven hydrogeological themes through GIS. The field verification of this model undoubtedly establishes the efficacy of the GIS integration tool in demarcating the potential groundwater reserve in soft rock terrain. Hence, this method can be used routinely in the groundwater exploration in favourable geological conditions. 2.10 Integration of Multigeodata for Hard Rock Area: A Case Study One of the important aims of GIS application is to integrate the information and its analysis, which will provide useful information about spatial and non-spatial data. Arcinfo is the most complete desktop GIS. It can edit and analyse the data in order to make better decisions in faster way. Arcinfo is the de facto standard for GIS. It has the functionality of ArcEditor and ArcView and adds advanced spatial analysis, extensive data manipulation and high-end cartography tools. It can create and manage personal geodatabases, multiuser geodatabases, and feature data sets. Also it can perform advanced GIS data analysis and modelling. The multigeodata technique will provide through various spatial data of the same area in same geographic coordinate using command like “union” an integrated layer of the described area containing all useful information. “union” method in GIS is a topological overlay of two polygon coverages which preserves features that fall within the spatial extent of either input data sets, i.e. all features from both coverages are retained. The integration of different data layers involves a process called overlay. At its simplest, this could be a visual operation, but analytical operations require one or more data layers to be joined physically. The integration of multigeodata technique has been used in the Govind Sagar dam environs of Lalitpur District to have an integrated groundwater potential map. Total four themes, i.e. hydrogeomorphology, lineament, overburden thickness and slope in the area of Lalitpur District, have been integrated using “union” command. Each theme has different categories of attributes, and they have been assigned relative weightage as per their importance. The relative weightages are the assigned values in places of attributes of a theme in order to higher groundwater potential is having higher relative weightage values and less potential is having less values. The assigned 36 2 Remote Sensing in Groundwater Studies relative weightages for different themes are shown in Table 2.1. To cite an example, the maximum weight assigned for the valley fill (which is more potential) is 120, whereas the lower weight of value 30 is assigned to BPP-S (which is less potential) in hydrogeomorphology theme. On the other hand, in overburden thickness layer, a maximum weight of 140 is assigned for thickness is having >25 m. and a minimum weight of 35 for thickness is having <5 m. There have been number of polygons created in the composite map after integration and the weightage values of each polygon have been summed up. Sum of the weightages of these polygons have been reclassified into four distinct classes according to their groundwater potentiality. 2.10.1 Classification Depending on the attributes of four GIS coverages and the general properties pertaining to the groundwater criteria, decision rule has been prepared. The probable groundwater potential zones have been classified from all the polygons in the composite coverage. Then, the polygons have been reclassified according to the weightage. Sum weightage 200–290 has been considered for “Class-4”, which is “excellent” groundwater potential zone, sum weightage 140–200 has been considered for “Class-3”, which is “good” groundwater potential zone, sum weightage 110–140 has been considered for “Class-2”, which is “moderate” groundwater potential zone and sum weightage up to 110 has been considered for “Class-1”, which is “poor” groundwater potential zone. Figure 2.12 shows that the probable groundwater potential zones constitute four classes. These classes can respond to certain specified management practices for the purposes of optimization of the available resources. The methodology and results clearly show the usage of GIS in exploration of groundwater. The technique also envisages the usefulness of RS information in groundwater exploration. The integrated results of more number of coverages can yield more accurate information about the groundwater in that area. Also the same technique can add the proper management of land utilization. 2.10.2 Depth to Bed Rock Contour Although some information can be obtained regarding the availability of groundwater from geoelectric depth slicing up to a certain depth, the topography of hard rock below 15 m depth is not obtainable and, hence, there is a need for depth contour map. It can give at a glance the variation of resistant substratum with depth in addition to the thickness of weathered and fractured/jointed rock at the subsurface. In general, the study area shows that depth of overburden increases from north to south. The thickness of the overburden (Fig. 2.13) varies in between 5 and 25 m in 2.10 Integration of Multigeodata for Hard Rock Area: A Case Study 37 the study area except the SE part, where the overburden exists up to 40 m. This part of the area is having moderate to good thickness of aquifer, which is the only indicative of the presence of exploitable groundwater through tube well in the area. Further, it is evident also from the boreholes drilled on the basis of geophysical recommendations. The reported well discharges in this part vary from 30 lpm (litre/minute) to 600 lpm. Fig. 2.12 Integrated map showing groundwater potential zones Govind Sagar dam environs, District Lalitpur 38 2 Remote Sensing in Groundwater Studies 2.10.3 Dar Zarrouk Parameters The product of the two parameters, longitudinal unit conductance (S), the ratio of thickness and resistivity (unit is mho), and transverse unit resistance (T) (unit is ohm-m2), are termed as Dar Zarrouk parameters. Sum of such parameters for a sequence of layers within a particular depth or total depth column of overburden gives a qualitative picture of the area regarding groundwater potentiality within that depth column. It is an established fact that a combination of high T and low S shows Fig. 2.13 Depth to basement contour map of Goving Sagar dam environs District Lalitpur 2.10 Integration of Multigeodata for Hard Rock Area: A Case Study 39 potential aquifer in hard rock area, where the general quality of groundwater is more or less uniform (Mallet 1947; Chandra and Athavale 1977). Variation of S reflects the basement topography if the overburden resistivity is not varying rapidly. In order to identify the potential aquifer at the Govind Sagar dam environs, the contours for T and S were drawn from geoelectrical data with the help of Arcinfo Tin GIS. From the careful examination of the T and S contour maps (Figs. 2.14 and 2.15), it has been observed that the area enclosed by T contours >400 and <2000 Xm2 and the same area enclosed by S contours >0.20 and <1.3 mho hold Vadose zone characteristics give the idea of rock formation within the zone of aeration. From the water table contour map (preferably premonsoon), the thickness of overburden above the zone of saturation can be determined and, hence, the transverse resistance of vadose zone (TV) can be calculated. This particular parameter (TV) can give the quantitative and qualitative picture of the area regarding relative groundwater recharge. Aller L, Bennett T, Lchr JH, Petty RJ, Hockett G (1987) DRASTIC: a standardized system for evaluating ground water pollution potential using hydrogeologic settings. NWWA/EPS scries, EPA-600/2-87-035 Chandra PC, Athavale RN (1977) Close grid resistivity surveys for demarcating the aquifer encountered in borewell at Koyyur in lower Manner basin, NGRI Hyderabad. Technical report no. GH-11-G.P.-7 16p Chi KH, Lee BJ (1994) Extracting potential groundwater area using remotely sensed data and GIS techniques. In: Proceedings of the regional seminar on integrated applications of remote sensing and GIS for land and water resources management, Bangkok (ESCAPE), pp 64–69 Chieh SH, Cromer MV, Swanson WR (1993) Computerized data processing and geographic information system applications for development of a 3-dimensional ground waler flow model. In: Hon K (ed) 20 Anniversary Water Resources Planning and Management Conference Proceedings. ASCE, New York, pp 224–227 Drury SA (1987) Image interpretation in geology. Allen and Upwin Publishing Ltd., New York, 396 p Gardino A, Tonelli AM (1983) Recent remote sensing techniques in fresh water submarine springs monitoring: qualitative and quantities approach. In: Proceeding of international symposium on methods and instrumentation for the investigation of groundwater system. Noordwijkerhout, The Netherlands, pp 301–310 Hendrix WG, Buckley DJA (1989) Geographic information system technology as a tool for ground water management. In: Proceedings of the Annual Convention of ACSM-ASPRS, Falls Church, Virginia, pp 230–239 Holyer RJ, Peckinpaugh SH (1989) Edge detection applied to satellite imagery of the oceans. IEEE Trans Geosci Remote Sens GE-27:46–56 Hord MR (1982) Digital image processing of remotely sensed data. Academic Press Inc., New York Jensen JR (1986) Introductory digital image processing. Prentice Hall, New York, 212 p Krishnamurthy J, Kumar Venkates N, Jayaraman V, Manivel M (1996) An approach to demarcate ground water potential zones through remote sensing and a Geographic Information System. Int J Remote Sens 17(10):1867–1884 Laurent R, Anker W, Graillot D (1998) Spatial modeling with geographic information system for determination of water resources vulnerability application to an area in Massif Central (France). J Am Water Resour Assoc 34(I):123–134 Lillesand TM, Kiefer RW (1987) Remote sensing and image interpretation. Wiley, New York, 721 p Mallet R (1947) The fundamental equation of electric prospecting. Geophysics 12(4):529–556 Menenti M (1983) A new geophysical approach using remote sensing techniques to study ground water tabic depth and regional evaporation from aquifers in deserts. In: Proceedings of the international symposium on methods and instrumentation for the investigation of groundwater system. Noorwijkerhout, The Netherlands, pp 311–325 Nath SK, Patra HP, Shahid Shamsuddin (2000) Geophysical prospecting for groundwater. Oxford and IBH Publishing Company, New Delhi, 256 p 44 2 Remote Sensing in Groundwater Studies Radhakrishnan K, Gceta V, Diwaker PG (1992) Digital image processing techniques—an overview. Natural Resources management—a new perspective. NNRMS, ISRO, Bangalore, pp 13–15 Ross MA, Tara PD (1993) Integrated hydrologic modeling with geographic information systems. J Water Resour Plann Manag 119(2):129–140 Rundquist DC, Peters AJ, Di L, Rodekohr DA, Ehram RL, Murray G (1991) Statewide groundwater vulnerability assessment in Nebraska using the DRASTIC/GIS model. Geocarto Int 6(2):51–57 Sabins FF Jr, (1987) Remote sensing: principles and interpretation. W.H. Freeman and Co., San Francisco, CA, 429 p Schowengerdt RA (1983) Techniques for image processing and classification in remote sensing. Academy Press, New York Shahid S, Nath SK, Roy J (2000) Groundwater potential modeling in a soft rock area using GIS. Int J Remote Sens 21:19–24 Simonett S (1983) The development and principles of remote sensing. Chapter I in manual of remote sensing, Edited by R.N. Colwell. Americal Society of Photogrammetry. Falls Church, Virginia Wang F, Newkirk R (1998) Acknowledge based system for highway network extraction. IEEE Trans Geosci Remote Sens GE-26(5):525–531 Watkins DW, Mckinncy DC, Maidment DR, Lin MD (1996) Use of geographic information system in ground water flow modeling. J Water Resour Plann Manag 122:88–96 References 45 http://www.springer.com/978-981-10-1147-4
  • Create New...

Important Information

By using this site, you agree to our Terms of Use.