This survey was performed in order to obtain accurate locational point data for each land use and land cover class included in the classification scheme as well as for fda approved the creation of training sites and for signature generation. The satellite data was enhanced before classification using histogram equalization in ERDAS Imagine 8.7 to improve the image quality and to achieve better classification accuracy. In supervised classification, spectral signatures are developed from specified locations in the image. These specified locations are given the generic name ��training sites�� and are defined by the user. Generally a vector layer is digitized over the raster scene. The vector layer consists of various polygons overlaying different land use types. The training sites will help to develop spectral signatures for the outlined areas.

The land use maps pertaining of two different periods were used for postclassification comparison, which facilitated the estimation of changes in the land use category and dynamism with the changes. Postclassification comparison is the most commonly used quantitative method of change detection [15�C17] with fairly good results. Postclassification comparison is sometimes referred to as ��delta classification�� [18]. It involves independently produced spectral classification results from different data sets, followed by a pixel-by-pixel or segment-by-segment comparison to detect changes in the classes. The detailed methodology adopted was given in Figure 2.Figure 2Flow chart of methodology for land use/land cover and change detection.4.

Results and DiscussionKnowledge about land use/land cover has become important to overcome the problem of biogeochemical cycles, loss of productive ecosystems, biodiversity, deterioration of environmental quality, loss of agricultural lands, destruction of wetlands, and loss of fish and wildlife habitat. The main reason behind the LU/LC changes includes rapid population growth, rural-to-urban migration, reclassification of rural areas as urban areas, lack of valuation of ecological services, poverty, ignorance of biophysical limitations, and use of ecologically incompatible technologies.Present study area Tirupati is a rapid developing town and is a world famous pilgrim centre for the devotees of Lord Sri Venkateswara. During the past few decades, the study area has witnessed substantial increase in population (Table 1), economic growth, and industrialization, and transportation activities (Table 1) have negative impact on the environmental health of the region. Table 1Study area population and vehicle fleet*.Due to involvement of multiple data sets, we used latest technologies like remote Cilengitide sensing and GIS to quantify LU/LC.