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.

One of the societal outcomes envisaged for the United Nations Decade of Ocean Science for Sustainable Development (2021‐2030) is a safe ocean where the safety of operations at sea and the coast are ensured. The Indian coast is prone to tropical cyclones, and the recent Ockhi, Gaja, Nilam, and Nivar cyclones devastated coastal Tamil Nadu in southern India, particularly the fishing community. This brought into focus the need to develop tools for the safety of fishermen at sea. A user-friendly application was developed named “Thoondil” (meaning fishing rod in the local language) by the National Centre for Coastal Research and made operational by the Department of Fisheries, Government of Tamil Nadu. It comprises a web-GIS-based dashboard for the state administrators and an android application for the fishermen. The salient features of the mobile application include the compass, weather, rescue plan, offline maps that provide the route to the nearest ports, incidence reporting, weather details, and potential fishing zone. The Thoondil dashboard provides information about the users and travel details, and helps to get information about the fishermen at sea at any point of time. A two-way communication between the administrators and the fishing community is enabled. The system is available in vernacular language with more than 15,000 downloads in a couple of months. Based on user interactions, it has evolved as a pan-India application and currently replicated in Kozhikode, Kerala. The use of the app has reduced risk to the fishing community, especially during hazards and has also contributed to the resilience of the coastal fishing communities.

Asia’s largest lake and the world’s foremost tropical lagoon, Chilika, stands as a testament to ecological diversity. The lake is diverse in biodiversity and is a sanctuary for over 400 distinct brackish and freshwater species. However, the lake is confronted by ceaseless pressures from a confluence of natural forces and anthropogenic activities. These challenges threaten to unleash ecological transformations that could reshape this ecological marvel. This study examined the spatial–temporal variation in the lake for the years 1988 to 2017 using archival remote sensing data. The Normalized Difference Water Index (NDWI) derived from Landsat 5-TM and Landsat 8-OLI was used to understand the expansion and contraction happening in the extent of the lake. To determine the water spread area, from each NDWI image, the minimum (Min.) pixel values, maximum (Max.) pixel values, and mean pixel values were extracted, and a yearly composite for was created the aforementioned years.

Changing trend in coastal ecosystem can be quantified by performing time series analysis. Time series analysis performed using long term remote sensing data will help us to identify the dynamic changes happening in the coastal ecosystem and its surrounding regions. In the present study we performed time series analysis on northern sector of Chilika Lake and its nearby regions of Odisha, which is situated in the east coast of India using three decades of freely available Landsat archive data. In order to detect dynamic changes trend parameters were calculated by using available data sets from Landsat Thematic Mapper (TM) and Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) for the observation period from 1988 to 2017. Two multi-spectral indices i.e. NDVI and EVI were generated from the available data sets and the trend analyses were performed using Theil-Sen (T-S) regression method by identifying robust trend parameters (slope and pvalue). The average mean values for NDVI and EVI were recorded at 0.4 and 0.2. Significant positive trend was observed in both vegetation indices (NDVI and EVI) with a mean slope value of 0.004 and 0.003 and pvalue of 0.03 and 0.02 respectively.

Changing trend in coastal ecosystem can be quantified by performing time series analysis. Time series analysis performed using long term remote sensing data will help us to identify the dynamic changes happening in the coastal ecosystem and its surrounding regions. In the present study we performed time series analysis on northern sector of Chilika Lake and its nearby regions of Odisha, which is situated in the east coast of India using three decades of freely available Landsat archive data. In order to detect dynamic changes trend parameters were calculated by using available data sets from Landsat Thematic Mapper (TM) and Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) for the observation period from 1988 to 2017. Two multi-spectral indices i.e. NDVI and EVI were generated from the available data sets and the trend analyses were performed using Theil-Sen (T-S) regression method by identifying robust trend parameters (slope and pvalue). The average mean values for NDVI and EVI were recorded at 0.4 and 0.2. Significant positive trend was observed in both vegetation indices (NDVI and EVI) with a mean slope value of 0.004 and 0.003 and pvalue of 0.03 and 0.02 respectively.