Advances in Remote Sensing

Volume 5, Issue 2 (June 2016)

ISSN Print: 2169-267X   ISSN Online: 2169-2688

Google-based Impact Factor: 1.5  Citations  

Integrated Approach Using Remote Sensing and GIS for Assessment of Groundwater Quality and Hydrogeomorphology in Certain Parts of Tummalapalle Area, Cuddapah District, Andhra Pradesh, South India

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DOI: 10.4236/ars.2016.52007    3,136 Downloads   6,010 Views  Citations

ABSTRACT

The present work deals with the assessment of groundwater potential zones and their suitability for drinking in the severely drought affected villages of Vemula mandal of Cuddapah District, Andhra Pradesh. This study is based on remote sensing and GIS approach. In this approach the IRS P6 LISS III Data (23.5 m Spatial Resolution) with Path: 100; Row: 063 of Indian Remote Sensing Satellite, Resourcesat IRS-P6 LISS-III has been utilized to analyse the onscreen interpretation and delineated different geomorphological units, lithological formations and geological structures. By integrating the above said parameters the hydrogeomorphological map is prepared on 1:50,000 scale. The study area is characteristically occupied by the Papaghni and Chitravati group of rocks. In the present study, the lithological formations on the basis of their genesis have been classified as fluvial, denudational and structural. Majority of lineaments are trending in NE-SW and NW-SE directions. The fluvial landforms namely valley fill moderate and valley have good groundwater prospects while shallow weathered buried pediplain has moderate to poor groundwater prospects. The chemical quality parameters of groundwater samples have indicated that the water samples are found to be suitable for drinking, agricultural, and industrial purposes.

Share and Cite:

Nagaraju, A. , Sreedhar, Y. , Thejaswi, A. and Dash, P. (2016) Integrated Approach Using Remote Sensing and GIS for Assessment of Groundwater Quality and Hydrogeomorphology in Certain Parts of Tummalapalle Area, Cuddapah District, Andhra Pradesh, South India. Advances in Remote Sensing, 5, 83-92. doi: 10.4236/ars.2016.52007.

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