Shoreline change assessment using geospatial tools: a study on the Ganges deltaic coast of Bangladesh

Mullick, Md. Reaz Akter; Islam, K. M. Ashraful; Tanim, Ahad Hasan

doi:10.1007/s12145-019-00423-x

Cited by 49 publications

(20 citation statements)

References 53 publications

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“…Another indicator of this process was the progradation of the margins of the bays bordered by beaches with the interlacing of strips of sand. A correlated alternation of erosion and accretion periods was observed by Fromard, Vega, and Proisy (2004) and Mullick, Ashaful, and Tanim (2019) on the mangrove shorelines. Fromard, Vega, and Proisy (2004) analyzed the coast of Guyana from 1956 to 1999 and associated these changes with the dynamics of the mud banks and the different growth stages of the mangroves.…”

Section: Discussionmentioning

confidence: 79%

“…Fromard, Vega, and Proisy (2004) analyzed the coast of Guyana from 1956 to 1999 and associated these changes with the dynamics of the mud banks and the different growth stages of the mangroves. Mullick, Ashaful, and Tanim (2019) assessed the Ganges deltaic coast of Bangladesh from 1977 to 2017 and observed that in estuaries, mangroves tend to accretion on the sides, while coastal areas undergo erosion.…”

Section: Discussionmentioning

confidence: 99%

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Shoreline Variation and Identification of Local Erosion Geoindicators on the Brazilian Amazon Coast

Teixeira

Bandeira

Dantas

2021

Journal of Coastal Research

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An analysis was conducted of the change in the shoreline in the northern part of Pará (Amazonian coast, Brazil) over a medium-term period. The analysis of the variation of the shoreline was carried out by tracing the spring high tide level in LANDSAT images from 1988 to 2019. With this tracing, it was possible to verify the behavior of the processes of erosion and accretion in the different morphological units (mangroves, beaches, cliffs, and rocky shores). The results show that changes in the shoreline occurred differently in the four morphological units, both in spatial and temporal scales. Regionally, the northern coast of Pará showed a trend of stability between losses and gains in areas, but with a slight predominance of the accretion process. The mangroves and beaches showed periods of alternation of predominance between the processes, while in the stretches with cliffs, the smallest areas of retreat were detected. The stretches of shoreline with a rocky shore remained stable throughout the study period. The study allowed the identification of local geoindicators for diagnosis and identification of the erosion process in coastal macrotidal regions. These results can assist in the identification of areas at risk of coastal erosion, as well as in coastal management in Pará State and related areas.

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Section: Discussionmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Shoreline Variation and Identification of Local Erosion Geoindicators on the Brazilian Amazon Coast

Teixeira

Bandeira

Dantas

2021

Journal of Coastal Research

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“…For this study, the DSAS software version 5 developed by the USGS [49] was used to generate orthogonal transects spaced 100 m apart along the 80 km covering the Yanbu city coast and calculate change statistics accordingly, using six distinct approaches including EPR, LRR and WLR. Note that according to [50] when rate of shoreline change calculated at different segments the LRR method performed better than the EPR and WLR. The EPR (short time period) method was conducted to monitor changes between successive shoreline pairs, namely : 1965-1980, 1980-1988, 1988-2000, 2000-2010, 2010-2019.…”

Section: Calculation and Interpretation Of Shoreline Change Ratesmentioning

confidence: 93%

Monitoring long-term shoreline changes along Yanbu, Kingdom of Saudi Arabia using remote sensing and GIS techniques

Niang

2020

Journal of Taibah University for Science

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This study presents the shoreline position changes of Yanbu coastal zone from 1965 to 2109, using multitemporal satellite data and the Digital Shoreline Analysis System (DSAS) application. The shoreline change rates were calculated based on End Point Rate (EPR), Linear Regression Rate (LRR), Weighted Linear regression (WLR) and Net Shoreline Movement (NSM) statistical methods to assess the short-and long-term trends. The maximum accretion reached was 1655.9 m (30.66, 32.32 and 36.9 m/year based on EPR, LRR and WLR methods respectively) while the maximum erosion was −1484.8 m (−37.9 m/year, −32.7 m/year and −33.5 m/year based on EPR, LRR and WLR methods respectively). An area of about 20 sq. km of sea and islets has been backfilled or dug in for various facilities. Thus, major changes in the configuration of the coastline are linked to human activities. This study provides a synoptic outlook of the degree of potential threat to the coastal system and their potential consequences.

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“…Digital Shoreline Analysis System (DSAS) is an analysis system based on the ArcGIS platform developed by the U.S. Geological Survey for analyzing the temporal and spatial change rate of coastline (Himmelstoss et al 2018). The system can simulate the temporal and spatial change rate of coastline in a period (Murat et al 2019;Mullick et al 2020;Matin and Hasan 2021). However, DSAS was rarely used in the lake shoreline analysis.…”

Section: Digital Shoreline Analysis System (Dsas)mentioning

confidence: 99%

Geospatial quantitative analysis of the Aral Sea Shoreline changes using RS and GIS techniques

Yue

Liu

et al. 2021

Earth Sci Inform

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The variations in the lake area, water level, and water volume change of the Aral Sea from 1987 to 2018 were analyzed in this study, which is based on the multi-temporal Landsat images and remote sensing water body index (Normalized Difference Water Index, NDWI). Lake shorelines were automatically digitized and visualized using Net Shoreline Movement (NSM) and Linear Regression Rate (LRR) by Digital Shoreline Analysis System (DSAS). The surface area, water level, and water volume change of the North Aral Sea were not changed significantly in 1987-2018. The maximum lake area appeared in March 2015 with 3550.15 km 2 , the minimum lake area appeared in October 2001with 2830.56 km 2 . However, since 2010 the South Aral Sea split into the East Aral Sea and the West Aral Sea, the area change has shrunk dramatically. The area of the East Aral Sea decreased by 17852.65 km 2 (89.99 %) from March 2011 to September 2018. The area of the West Aral Sea decreased by 2203.62 km 2 (35.96 %) from August 2014 to March 2017. The distance of lake shoreline changes and rates of net movement along transects were determined by DSAS. The two largest positive values of NSM were 18715.93 and 12268.67 m, which showed lake shoreline retreat, occurred in the periods of 2010-2018 in the East Aral Sea and 1987-1999 in the South Aral Sea, respectively. The two periods in which the largest negative value of NSM appeared was (1987-1993) and (2001-2010) in the North Aral Sea, and the lake shoreline expanding distance was 145.43 and 545.84 m, respectively.

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Shoreline change assessment using geospatial tools: a study on the Ganges deltaic coast of Bangladesh

Cited by 49 publications

References 53 publications

Shoreline Variation and Identification of Local Erosion Geoindicators on the Brazilian Amazon Coast

Shoreline Variation and Identification of Local Erosion Geoindicators on the Brazilian Amazon Coast

Monitoring long-term shoreline changes along Yanbu, Kingdom of Saudi Arabia using remote sensing and GIS techniques

Geospatial quantitative analysis of the Aral Sea Shoreline changes using RS and GIS techniques

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