Methodology
The interfacing of GIS and SRS in this study provides a new and exciting capability to analyze the dynamics of land-use change. The unique feature of SRS compared to other tools is that it can be used to collect data for baseline inventory and future monitoring purposes. Since spatial relationships were inherent to the environmental data, GIS technology provided an effective means for intuitive access to the Site's environmental information.

The remote sensing readily was merged with other resources of geocoded information in a GIS. This permitted the overlapping of several layers of information with the remotely sensed data, and the application of a virtually unlimited numbers of forms of data analysis. The land cover data was calculated the change in urban area and forest area.

The complete methodology of the research is shown in Figure 1.


Figure 1. Flow chart showing complete research method
Data Processing
The two-mutidate satellite images were used to show the trend of urbanization. One of the images was of year 1992, and the other one was of year 2000. Image to image registration was followed. The same procedures (Georefrencing, Enhancement, Spatial Filtering) in ER Mapper were applied on multi-date satellite images. To improve the visual interpretability of an image by increasing the apparent distinction between the features in the scene, image enhancement was carried out. The most common “contrast stretching” was applied. Spatial filtering is a local operation in that fixed values in an original image was modified on the basis of the gray levels of neighboring pixels. High Pass Filter was used to sharpen the edges and its main emphasis was brightened the small areas.

The method that was used for change detection was Multi Date Band Insertion. For this purpose, placed the multi date images into single track in such a way that both the images were overlay each other. In order to monitor urban growth and considering the process of converting vacant natural landscape into an extensive living community, the technique of classification was used. The objective of the classification was to replace visual analysis of the image data with quantitative techniques for automating the identification of features in a scene.

The first step to get the information and the urban or forest area of the satellite image was the development of the vector layer. The vector layer was developed through Digitization in AutoCAD Map 2000i and the ‘Heads up Digitization’ method was followed.

In Arc View 3.1, the remote sensing data was merged with other resources of geocoded information to show the trend of urbanization in terms of spatial analysis and thematic maps. The script was run to calculate the urban area of 1992, 2000 and the forest area of 1976, 1992, and 2000. For the development of urban rural fringe, buffers were generated.

The theme of year 1976, 1992 was added and then the theme of year 2000 was added. Both the themes remained active and overlay the urban area of both the years for visual analysis. Similarly, the themes of forest area remained active and overlay each other, which showed clear deforestation phenomena. Bahria Town Housing Scheme was selected to observe the land use of a planner. The digitized map was exported in the *.shp format to ArcView. This map was categorized to observe the land use in ArcView. After preparing the maps in Arc View, layouts were prepared for the final output of the research.

Results
A satellite image is a digital picture of the earth. The satellite image used in this study was composed of a grid of pixels. A pixel, which is the smallest unit of an image, can be considered as a square in shape. The brightness of a pixel represented the reflected energy detected by the satellite sensor over the area of land covered by the pixel. The Grayscale image of year 1992 and 2000 was used for the research purpose [Figure 2 (a) & (b)]. The images are in panchromatic mode where as multispectral band is not used in the study because the concrete is visible in panchromatic mode. The pixel values in the image are interpreted as gray shades with lower pixel values assigned darker shades of gray. There were 256 shades of gray in these images.

The Image interpretation technique in this study enabled the use of satellite images as a spatial frame of reference. Image interpretation involved general examination of images, Extraction of features from images, and evaluation. Interpretation techniques include the detection, identification, and measurement of specific features so image interpretation after edge and contrast enhancement delineated:

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