Monitoring of Chilika Lake mouth dynamics and quantifying rate of shoreline change using 30 m multi-temporal Landsat data

Abstract

Coastal erosion is one of the major and serious concerns for coastal communities residing in the low lying areas, especially near to estuary delta regions. These regions see lots of anthropogenic activities such as economic development, infrastructure and human settlement especially in rapidly developing countries such as India. Shoreline change is a natural process that occurs in coastal areas. But due to the stresses happening in the coast because of anthropogenic activities, understanding how shorelines change over time is important for sustainable management of coast. A crucial aspect of shoreline change monitoring is to identify the location and change over time which can be achieved by developing monitoring strategies using satellite remote sensing data. Performing shoreline change analysis using long term satellite records will help us to understand how shorelines respond to coastal development over time. In the present study we investigate shoreline erosion and accretion rate using three temporal Landsat scenes acquired over a thirty year period for the years 1988, 2000 and 2017. Digital Shoreline Change Analysis System (DSAS) an extension of ArcGIS software was used to compute rate of change statistics by calculating End Point Rate (EPR) values. We observed that Chilika coast is experiencing both erosion and accretion process with very high erosion rate of −13.6 m/yr and accretion of 13.5 m/yr, at Chilika Lake mouth. The average erosion and accretion rate of −1.13 m/yr and 1.41 m/yr were recorded for the study region.

Bibliometric Analysis of Data Sources and Tools for Shoreline Change Analysis and Detection

“…Shoreline position detection is, thus, important especially considering the long history of human habitation of the coast and the banks of large waterbodies and their recent adaptation [10]. This has meant that the term shoreline change is not limited to only the coast but encompasses lake and lagoon environments as well [12][13][14]. Credit in this sense, should, thus, be given to earlier geoscientists such as Carr [15], de Boer and Carr [16], El-Ashry and Wanless [17], and Gulliver [18], whose work contributed to the advancement of information on shoreline change.…”

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

“…Though mapping using Geographic Information System (GIS) presents a reliable solution to Earth Observation (EO) data processing, estimation accuracy is still a notable challenge in mapping coastal areas with high heterogeneity of land cover types. Due to the logical straightforwardness of the classification algorithms, their application for thematic GIS-based mapping and analysis of landscape dynamics presents a wellknown approach to cartographic workflow with existing case studies on Chilika Lake, the Mahanadi Delta, and the Odisha coastal area [50][51][52][53].…”

Land Use/Cover Dynamics and Associated Impacts on Eutrophication, Land Surface Temperature, and Ecosystem Service Values: An Eco-Climatological Investigation of Chilika Lake, India.

“…(Acharyya et al 2020). The lake is nourished by various tributaries, including the Daya River, and is surrounded by a variety of habitats, including marshlands, mudflats, open waters, and islands (Barik et al 2016;Vivek et al 2019). It is situated at a latitude of 19.8° N and a latitude of 85.5° E. The lagoon's estimated annual highest temperature is 40℃, with the lowest temperature of about 14°C (Nayarisseri et al 2018).…”