Image processing and Supervised Classification of LANDSAT data for Flood Impact Assessment on Land Use and Land Cover

Floods are one of the most frequent natural disasters in the world, causing economic damage as well as human losses. In the present research Pre and Post flood Landsat satellite data was processed through image processing techniques. Landsat satellite image was initially correct the geometry correction and radiometric correction carried out. Similarly follow the pre-processing techniques on satellite image. The GIS platform used to find out the pre and post flood impact on land use and land cover changes for supervised classification techniques in Chengalpattu taluk, Tamil Nadu. The study Area which are classified into five classes in supervised classification for water, urban, forest, barren land and agriculture using GIS platform. Using high resolution Landsat 8 images, study area were categorised into five types in Level 1 classifications. Supervised classifications provide better result in the representation of classes for pre and post flood impact on land use land cover. The overall accuracy of image classification obtained in 2015 and 2016 is 94.25 % and 90.8%. Hence the result proves that satellite data has capability for analysing the changes in LULC through image classification techniques due to flood.