The coastal regions of India are densely populated and most biological productive ecosystems which are threatened by erosion, natural disaster, and anthropogenic interferences. These threats have made priority in appraisal of shoreline dynamicity as part of sustainable management of coastal zones. The present study assessed the long- to short-term dynamicity of shoreline positions along the coast of Puri district, Odisha, India, during the past 25 years (1990–2015) using open-source multi-temporal satellite images (Landsat TM, ETM + , and OLI) and statistical-based methods (endpoint rate, linear regression rate and weighted linear regression). The long-term assessment during 1990–2015 shows that shoreline accredited at the rate of 0.3 m a−1 with estimated mean accretion and erosional rate of 1.18 m a−1 and 0.64 m a−1, respectively. A significant trend of coastal erosion is primarily observed on the northern side of Puri district coast. A cyclic pattern of accretion (during 1990–1995 and 2000–2004) and erosion (during 1995–2000 and 2009–2015) was observed during the assessment of short-term shoreline change. It exhibited significant correlation with the landfall of severe cyclones and identified cyclic phases after severe cyclonic storms, i.e., phase of erosion, phase of accretion and phase of stabilization. Overall, the natural processes specifically the landfall of tropical cyclones and anthropogenic activities such as the construction of coastal structures, encroachment and recent construction in the coastal regulatory zone, and construction of dams in upper catchment areas are the major factors accountable for shoreline changes. The output of the research undertaken is not only crucial for monitoring the dynamism of coastal ecosystem boundaries but to enable long- to short-term coastal zone management planning in response to recently reported high erosion along the Puri coast. Moreover, the usage of open-source satellite imageries and statistical-based method provides an opportunity in developing cost-effective spatial data infrastructure for shoreline monitoring and vulnerability mapping along the coastal region.
Shoreline changes Long- to short-term changes Endpoint rate (EPR) Linear regression rate (LRR) Weighted linear regression (WLR) Remote sensing
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We are thankful to the United States Geological Survey (USGS) for providing Landsat and multi-satellite high resolution (used in Fig. 12) data free of cost for this research. Pritam Chand is grateful to Director, National Institute of Hydrology (NIH) and Head, WRSD, NIH, Roorkee (India), for providing facilities and ample support to carry out this work.
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