AN ASSESSMENT OF LAND COVER CHANGE DYNAMICS OF GAJA CYCLONE IN COASTAL TAMIL NADU, INDIA USING SENTINEL 1 SAR DATASET

• Main Authors: K. Nivedita Priyadarshini, V. Sivashankari, S. Shekhar
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-08-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W8/323/2019/isprs-archives-XLII-3-W8-323-2019.pdf

<p>Land cover change is a dynamic phenomenon addressing environmental issues including natural calamities. Recent advancements in geospatial technology and availability of remote sensor data have fostered monitoring and mapping of land cover changes more precisely. Remote sensing is widely used where emerging research findings are focused mainly on coastal hazard studies. Tropical cyclones being an extreme weather event are more powerful and hazardous to southern parts of the Indian subcontinent. Aftermath of the cyclone is extreme causing land cover changes like defoliation, water logging, destruction of cultivable lands, plantations shrub vegetation, dissolving salt pans etc. The tropical cyclones are fierce to devastate the coastal districts of Tamil Nadu and make it a prey to these cyclones. In this paper, an attempt has been made to assess the pre and post cyclonic land cover change by utilizing potential microwave Synthetic Aperture Radar (SAR) dataset. The study portrays the occurrence of a severe cyclonic storm named <q>Gaja</q> that was formed over Bay of Bengal which hit Tamil Nadu on 15^th of November 2018 causing high death toll and demolition. The study focuses on the pre and post damage assessment provoked by Gaja cyclone. For analysis, a methodical procedure was followed by utilizing the Sentinel 1 SAR dataset. Random Forest (RF) classifier approach was incorporated for mapping land cover types as it reduces the variance among the classes thus yielding accurate predictions. Results demonstrate that classified imagery using dual polarization SAR dataset outperforms well for RF classifier thus escalating the overall accuracy.</p>