Shoreline change behavior study of Jambudwip island of Indian Sundarban using DSAS model

Das, Santosh Kumar; Sajan, Bhartendu; Ojha, Chandrakanta; Soren, Sabita

doi:10.1016/j.ejrs.2021.09.004

Cited by 19 publications

(5 citation statements)

References 30 publications

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“…Identifying the land–water interface is a difficult undertaking, especially when fluvial and marine processes are present at the same time. Integration of GIS, RS and DSAS is becoming increasingly popular for detecting shoreline using multispectral and hyperspectral satellite images, as DSAS is easy to incorporate with ArcGIS/ArcMAP and it can deliver a resilient suite of regression rates in a consistent and slickly reproducible way that can be used for huge volumes of data composed at manifold scales and is widely employed by many scientists and researchers throughout the world (Santos, do Nascimento, Mishra, & da Silva, 2021; Hossain et al ., 2021; Das, Sajan, Ojha, & Soren, 2021; Bouchahma & Yan, 2012; Li & Damen, 2010; Kuleli et al ., 2011; Alesheikh, Ghorbanali, & Nouri, 2007; Sarwar, Mahabub, & Woodroffe, 2013a, b).…”

Section: Introductionmentioning

confidence: 99%

Assessing the shoreline dynamics on Kuakata, coastal area of Bangladesh: a GIS- and RS-based approach

Uddin

Niloy

Haque

et al. 2022

AGJSR

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PurposeThis study aims to determine shoreline change statistics and net erosion and accretion, along the Kuakata Coast, a magnificent sea beach on Bangladesh’s southernmost point.Design/methodology/approachThe research follows a three stages way to achieve the target. First, this study has used the geographic information system (GIS) and remote sensing (RS) to detect the temporal observation of shoreline change from the year 1991 to 2021 through satellite data. Then, the digital shoreline analysis system (DSAS) has also been explored. What is more, a prediction has been done for 2041 on shoreline shifting scenario. The shoreline displacement measurement was primarily separated into three analytical zones. Several statistical parameters, including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR) and Linear Regression Rate (LRR) were calculated in the DSAS to quantify the rates of coastline movement with regard to erosion and deposition.FindingsEPR and LRR techniques revealed that the coastline is undergoing a shift of landward (erosion) by a median rate of 3.15 m/yr and 3.17 m/yr, respectively, from 1991 to 2021, 2.85 km2 of land was lost. Naval and climatic influences are the key reasons for this variation. This study identifies the locations of a significantly eroded zone in Kuakata from 1991 to 2021. It highlights the places that require special consideration while creating a zoning plan or other structural design.Originality/valueThis research demonstrates the spatio-temporal pattern of the shoreline location of the Kuakata beach, which would be advantageous for the region’s shore management and planning due to the impacts on the fishing industry, recreation and resource extraction. Moreover, the present research will be supportive of shoreline vulnerability. Hence, this study will suggest to the local coastal managers and decision-makers for particularizing the coastal management plans in Kuakata coast zone.

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Section: Introductionmentioning

confidence: 99%

Assessing the shoreline dynamics on Kuakata, coastal area of Bangladesh: a GIS- and RS-based approach

Uddin

Niloy

Haque

et al. 2022

AGJSR

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“…In 2018, Emily et al [28] introduced a Digital Shoreline Analysis System (DSAS) to calculate shoreline rate changes and forecast shoreline changes over the next 10 or 20 years. More recently, DSAS has been applied to account for shoreline rate changes [29][30][31]. Terrestrial measurements for shoreline demarcation due to tidal action, coastal structures, and littoral drift movements are recommended to achieve the most accurate results [29].…”

Section: Shoreline Changes Due To Coastal Structuresmentioning

confidence: 99%

Analysis of Long-Term Shoreline Observations in the Vicinity of Coastal Structures: A Case Study of South Bali Beaches

Rahmawati

Putro²,

Lee

2021

Water

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Recently, many rigid structures have been installed to cope with and efficiently manage coastal erosion. However, the changes in the coastline or isocenter and the movements of coastal sediment are poorly understood. This study examined the equilibrium shoreline and isocenter lines by applying a Model of Estimating Equilibrium Parabolic-type Shoreline (MeEPASoL) as an equilibrium shoreline prediction model. In addition, the inverse method was used to estimate littoral drift sediment transport from long-term beach profile observations. The movement of coastal sediments was analyzed using long-term beach profile observation data for three Indonesian beaches, namely, Kuta Beach for 13 years, Karang Beach in Sanur for 15 years, and Samuh Beach in Nusa Dua for 18 years. The littoral drift at every site was dynamically controlled by seasonal changes in the monsoon, the erosion and deposition patterns coupled with the presence of coastal structures, and limited sediment movement. Shoreline deformation in Kuta is generally backward deformed, with a littoral drift from south to north. In Sanur, the littoral drift vector carries sediment from the right and left sides and forms a salient behind the offshore breakwater. The littoral drift at Nusa Dua is dominantly from south to north, but the force of sediment transport decreases near the breakwater towards the north. Furthermore, the methods applied herein could aid the development of strategic coastal management plans to control erosion in subcells of coastal areas.

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“…For example, Nassar et al [30] described shoreline change detection along the North Sinai coast in Egypt during the period from 1989 to 2016. In the same way, Das et al [31] attempted to analyse the potential migration characteristics of Jambudwip Island of the Sundarban biosphere in the Bay of Bengal.…”

Section: Introductionmentioning

confidence: 99%

A GIS Procedure to Assess Shoreline Changes over Time Using Multi-temporal Maps: An Analysis of a Sandy Shoreline in Southern Italy over the Last 100 Years

Pepe

Costantino²,

Alfio³

2023

GaEE

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The aim of the paper is to identify a methodology capable of assessing shoreline changes through a geomatic approach based on the use of GIS (Geographic Information System) software. The paper describes a case study that reports the evolution of a coastline over a period of more than 100 years using medium and large-scale metric maps available in different periods. In fact, the coastlines were obtained from the source maps of the Italian Cadastre (dated 1890), from numerical cartography available on the coastline and acquired in different period at scales 1:5000 and 1:2000 and, more recently, from the Google Earth Pro platform. To analyse the evolution of the coastline a new procedure has been performed which is based on the use of GIS software, in particular a plugin called DSAS that allows the evaluation of the changes in the coastline and also obtains a statistical analysis of its evolution. The results showed the ease and applicability of the method in determining the evolution of the coastline and the strong erosion of a stretch of coastline with important socio-economic consequences and repercussions was highlighted in the analysed case study.

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Shoreline change behavior study of Jambudwip island of Indian Sundarban using DSAS model

Cited by 19 publications

References 30 publications

Assessing the shoreline dynamics on Kuakata, coastal area of Bangladesh: a GIS- and RS-based approach

Assessing the shoreline dynamics on Kuakata, coastal area of Bangladesh: a GIS- and RS-based approach

Analysis of Long-Term Shoreline Observations in the Vicinity of Coastal Structures: A Case Study of South Bali Beaches

A GIS Procedure to Assess Shoreline Changes over Time Using Multi-temporal Maps: An Analysis of a Sandy Shoreline in Southern Italy over the Last 100 Years

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