The Geographic Information System (GIS) is a computer-assisted system for acquisition, storage, analysis and display of geographic data. GIS allows for creating, maintaining and querying electronic databases of information normally displayed on maps. These databases are spatially oriented, the fundamental integrating element being their position on the earth's surface. This system consists of a set of computerised tools and procedures that can be used to effectively encode, store, retrieve, overlay, correlate, manipulate, analyse, query, and display land-related information. They also facilitate the selection and transfer of data to application specific analytical models capable of assessing the impact of alternatives on the chosen environment. The underlying foundation of sound GIS is an effective digital map database, tied to an accurate horizontal control survey framework.

The spatial data generally is in the form of maps, which could be showing topography, geology, soil types, forest and vegetation, land use, water resource availability etc., stored as layers in a digital form. Integrating many layers of data in a computer can easily generate new thematic maps. Thus, a GIS has a database of multiple information layers that can be manipulated to evaluate relationships among the selected elements. GIS can create maps, integrate information, visualise scenarios, solve complicated problems, present powerful ideas, and develop effective solutions.

GIS works with two fundamentally different types of geographic models, the "vector" model and the "raster" model. Raster organises spatial features in regular spaced grid of pixels, while the vector data structure organises spatial feature by the set of vectors, which are specified by starting point co-ordinates. A single x, y co-ordinates, can describe the location of a point feature, such as a location of boreholes. Point features are represented as vectors without length and direction. Linear features such as roads and rivers can be stored as point co-ordinates. Polygon features, such as land parcels and river catchments, can be stored as a closed loop of co-ordinates. Compared to a line designated in a raster format, a vector line is 1-d and has no width associated with it. The vector model is extremely useful for describing discrete features, but less useful for describing continuously varying features such as soil top. 

Advantages of vector type data

The smallest feature in a raster data structure is represented by a single pixel. The raster model has evolved to model continuous features. A raster image comprises a collection of grid cells rather like a scanned map or a picture. Both the vector and raster models for storing geographic data have unique advantage and disadvantages.

Advantages of raster data type

GIS has been touted as a great boon to engineering, science, planning, and decision-making in every field. Some of the noteworthy applications of GIS are:

Remote sensing refers to obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation (Lillesand and Kiefer, 1996). The eyes are an excellent example of a remote sensing device. With this, it is possible to gather information about the surroundings or even reading the text as in this case. However, this simple definition of remote sensing is more commonly associated with the gauging of interactions between the earth surface and the electromagnetic energy. These days the data gathering of the earth surface is enabled with various sensors that are able to efficiently absorb reflected energy from the earth's surface. The satellites with onboard sensors, play an important role in data capture.

Remote sensing systems are a very important source of information for GIS, as they provide access to spatio-temporal information on surface processes on scales ranging from regional to global. A wide range of environmental parameters can be measured including land use, vegetation types, surface temperatures, soil types, precipitation, phytoplankton, turbidity, surface elevation and geology. Remote sensing and GIS aid immensely in urban sprawl studies.

 In the case of a combined application, an efficient, even though more complex, approach is the integration of remote sensing data processing, GIS analyses, database manipulation and models into a single analysis system (Michael and Gabriela, 1996). Such an integrated analysis, monitoring and forecasting system based on GIS and database management system technologies requires the analyst to understand not only the problem but also the available technologies yet without being a computer expert. 

The integration of GIS and remote sensing with the aid of models and additional database management systems (DBMS) is the technically most advanced and applicable approach today.  

The remote sensing applications are growing very rapidly with the availability of high-resolution data from the state of the art satellites like IRS-1C/1D/P4 and Landsat. The advancement in computer hardware and software in the area of remote sensing also enhances remote sensing applications. IRS-1C/1D/P4 provides data with 5.8 m resolution in panchromatic mode giving more information of the ground area covered. The remote sensing satellites with high-resolution sensors and wide coverage capabilities will provide the data with better resolution, coverage and revisit (once in 24 days for IRS 1C) to meet the growing applications needs. Many applications like crop acreage and yield estimation, drought monitoring and assessment, flood mapping, wasteland mapping, mineral prospects, forest resource survey etc., have become an integral part of the resources management system. These resource management systems need the data to be transferred in real time or near real time for processing.

The land use classification is primarily to understand the spatial distribution of various land features and plan accordingly for optimum utilisation of the land with least effects on the associated systems. The pattern and extent of land use is influenced mainly by two factors - physical and anthropogenic. Physical factors include topography, climate and soils, which set the broad limits upon the capabilities of the land, and the anthropogenic factors are, density, occupation of the people, socio-economic institutions, the technological level, and infrastructure facilities. GIS and remote sensing collectively help in understanding and undertaking these applications effectively.  

The digital image processing is largely concerned with four basic operations: image restoration, image enhancement, image classification, and image transformation (Eastman, 1999). The image restoration is concerned with the correction and calibration of images in order to achieve as faithful representation of the earth surface as possible. Image enhancement is predominantly concerned with the modification of images to optimise their appearance to the visual system. Image classification refers to the computer-assisted interpretation of images that is vital to GIS. The image transformation refers to the derivation of new imagery as a result of some mathematical treatment of the raw image bands.

The operation of image restoration is to correct the distorted image data to create a more faithful representation of the original scene. This normally involves the initial processing of raw image data to correct for geometric distortions, to calibrate the data radiometrically, and to eliminate the noise present in the data. Image rectification and restoration are also termed as pre-processing operations.

Enhancement is concerned with the modification of images to make them more suited to the capabilities of human vision. Regardless of the extent of digital intervention, visual analysis invariably plays a very strong role in all aspects of remote sensing. Enhancement of the imagery can be done by the histogram equalisation method or linear saturation method before analysis.

Digital image classification is the process of assigning a pixel (or groups of pixels) of remote sensing image to a land cover or land use class. The objective of image classification is to classify each pixel into one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques are categorised based on the training process - supervised and unsupervised classification.

Supervised classification has three distinct stages namely training, allocation and testing. Training is the identification of a sample of pixels of known class membership gathered from reference data such as ground truth, existing maps and aerial photographs. In the second stage, the training pixels are used to derive various statistics for each land cover class and so are correspondingly assigned as signature. In the third stage, the pixels are allocated to the same class with which they show greatest similarity based on the signature files.

Unsupervised classification techniques share a common intent to uncover the major land cover classes that exist in the image, without prior knowledge of what they might be. Such procedures often come under cluster analysis, since they search for clusters of pixels with similar reflectance values. Unlike the supervised classification, only major land classes are separated as clusters, while smaller classes may be ignored. The decision for the number of clusters can be based on the histogram analysis of the reflectance values. The most prominent number of classes as seen in the histogram can be considered as the number of clusters.

The Indian Remote Sensing (IRS) satellite's Linear Imaging Self Scanning Sensor (LISS) imagery contains four bands. National Remote Sensing Agency (NRSA) distributes the satellite data for India. This will have image in Band Interleaved by Line (BIL) format i.e., this file contains first line from first band, first line from second band, first line from third band and first line from fourth band in one interleaved line and in second interleaved line it contains second line from first band, second line from second band, second line from third band, second line from fourth band and so on. Band extraction is implemented to separate these bands.

The 23.5 m ground resolution IRS-LISS3 multispectral image has the following bands

Green                         0.52 - 0.59 micrometer

Red                             0.62 - 0.68 micrometer

Near-Infrared               0.77 - 0.86 micrometer

Short-wave Infrared    1.55 - 1.7 micrometer

Imagery obtained from the satellites will have geometric errors due to the nature of motion of satellite and high altitude of sensing platform. Prominent Ground Control Points (GCPs) from toposheets (which is always correct) are taken to rectify geometric errors. This procedure is also called as geo-correction / geo-rectification.

Image processing, neural network and other techniques are used to analyse the satellite imagery. The decision rule based on geometric shapes, sizes, and patterns present in the data is termed as Spatial Pattern Recognition. Similarly, visual interpretation is done on satellite imagery by considering the elements of image interpretation such as, shape, size, tone, texture, pattern and size for pattern recognition. The pattern recognition of urban sprawl is identified after classification of the remote sensed images for the built-up area and is then further analysed. 

In recent years, considerable interest has been focused on the use of GIS as a decision support system. For some, this role consists of simply informing the decision making process. However, it is more likely in the realm of resource allocation that the greatest contribution can be made with the aid of GIS and remote sensing. The use of GIS as a direct extension of the human decision-making process, most particularly in the context of resource allocation decisions, is indeed a great challenge and an important milestone. With the incorporation of many software tools to GIS for multi-criteria and multi-objective decision-making, an area that can broadly be termed decision strategy analysis there seems to be no bounds for the application of GIS. Closely associated with the decision strategy analysis is the uncertainty management. Uncertainty is not considered as a problem with data, but it is an inherent characteristic of the decision making process. With the increasing pressures on the resource allocation process, the need to recognise uncertainty as a fact of the decision making process has to be understood and carefully assessed. Uncertainty management thus lies at the very heart of effective decision-making and constitutes a very special role for the software systems that support GIS (Eastman, 1999).

 The decision support is based on a choice between alternatives arising under a given set of criterion for a given objective. A criterion is some basis for a decision that can be measured and evaluated. Criterion can be of two kinds: factors and constraints, and this can pertain either to attributes of the individual or to an entire decision set. In this case the objective being to urbanise; constraints include the already existing built-up area, road-rail network, water bodies, etc., where there is no scope for further sprawl; and factors include the components of population growth rate, population density and proximity to the highway and cities. The decision support system evaluates these sets of data using multi-criteria evaluation. This predicts the possibilities of sprawl in the subsequent years using the current and historical data giving the output images for the objective mentioned.