DETECTION OF SPATIOTEMPORAL CHANGES IN PALAR - PORUNDALAR DAM, DINDIGUL DISTRICT, TAMIL NADU, INDIA USING GEOMATICS TECHNOLOGIES

M Lavanya, M. Muthukumar

Abstract


Water is an essential natural resource which indicates the economic growth of a region along with its various sustainable development plans. However, the rapid development on demographic, economic, and technological trends results in demolishing the favorable environment condition for water availability and results it scarcity. Though, the global warming condition and the anthropogenic activities affects the climatic conditions, the natural water resources and its sustainability need to maintain for future generations. The impacts of global warming, climate change and manmade activities affects water resource availability and its quality. It is mandatory to monitor the water resources in order to manage the resource. So, it is important to detect the surface water bodies and to analyze the Spatio-temporal changes of water bodies. It helps to provide sustainable development plans in water resource management. In the recent researches, remote sensing is one of the cost effective technology which used to detect and analyze the changes of spatial features and also to monitor the natural resources present on the earth surface. The study area chosen for the analysis is the Palar- Porundalar Dam which is the largest water body present in Palani Taluk, Dindigul District, Tamil Nadu, India. The present study, strives to detect the water spread of the Palar-Porundalar Dam for the years 1997, 2009 and 2021 using multi temporal satellite images with the help of Normalized Difference Water Index (NDWI) and to identify the changes over the above said periods. The result indicates that for the year 1997 the surface water spread detected upto 4.84 sqkm, for the year 2009 the surface water spread detected upto 4.81 sqkm and in the year 2021 the surface water spread detected upto 4.88 sqkm. Finally, the validation of the result carried out using accuracy assessment method manually by using kappa coefficient formula. The validation result indicates that there is 85.08% of the match detected among the classified and the reference data. The overall accuracy is 92.59%.


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References


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