Analysis of long- and short-term shoreline change dynamics: A study case of João Pessoa city in Brazil

Santos, Celso Augusto Guimarães; Nascimento, Thiago Victor Medeiros do; Mishra, Manoranjan; Silva, Richarde Marques da

doi:10.1016/j.scitotenv.2020.144889

Cited by 49 publications

(41 citation statements)

References 87 publications

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“…For each transect, the uncertainty regarding the analysis of shoreline change rate (U i ) is quantified as the quadrature sum of the uncertainty for each year's shoreline position, divided by the number of years between the first and last shoreline assessed (Gibbs et al, 2019). An individual transect uncertainty can be estimated from Equation 2 (Gibbs et al, 2019;Hapke et al, 2010;Rajasree et al, 2016;Santos et al, 2021):…”

Section: Estimated Shoreline Positioning and Change Rate Uncertaintiesmentioning

confidence: 99%

“…(TPMC) Environmental Services (Thieler et al, 2017), to calculate the statistical rate of shoreline change from different temporal series of the shoreline positions. DSAS is a software widely used for analysing shoreline dynamics around the world (Bheeroo et al, 2016;Bidorn, Sok, et al, 2021;Martínez et al, 2018;Mishra et al, 2021;Nassar et al, 2019;Nguyen et al, 2013;Petropoulos et al, 2015;Santos et al, 2021;Tran Thi et al, 2014). Statistical rates of shoreline change can be evaluated using DSAS by the following steps: (1) create a baseline parallel to a given coast to use as a reference point for computing shoreline change statistics at each transect;…”

Section: Datasets Georeferencing and Shoreline Extractionmentioning

confidence: 99%

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Seven decades of shoreline changes along a muddy mangrove coastline of the Upper Gulf of Thailand

Sok

Bidorn

Burnett

et al. 2022

Earth Surf Processes Landf

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This study evaluates the historical coastal change and development along the eastern coastline of the Upper Gulf of Thailand between 1953 and 2019 based on a series of aerial photographs and satellite images. Long-term ($70 years) and short-term ($10 years) shoreline movement rates were analysed using the Digital Shoreline Analysis System (DSAS). Results of this analysis indicate that the shoreline along the western coast (WBK) of the Bang Pakong River mouth has undergone severe coastal degradation, with a land loss of about 980 ha over the last seven decades. Meanwhile, the eastern coastline (EBK) has developed continuously over this time span, resulting in a land growth of about 552 ha. Based on historical shorelines recorded since 1954, the shoreline recession along the WBK coast accelerated over five decades. The average shoreline change rate reached approximately À9 m/year in 2002 and is shown to be related to land subsidence, which was due to groundwater

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Section: Estimated Shoreline Positioning and Change Rate Uncertaintiesmentioning

confidence: 99%

Section: Datasets Georeferencing and Shoreline Extractionmentioning

confidence: 99%

Seven decades of shoreline changes along a muddy mangrove coastline of the Upper Gulf of Thailand

Sok

Bidorn

Burnett

et al. 2022

Earth Surf Processes Landf

View full text Add to dashboard Cite

show abstract

“…The shoreline is defined straightforwardly as the line that overlaps the physical land-water boundary [3]. Shoreline data can be obtained from different sources, such as historical maps, aerial photography, Light Detection and Ranging (LIDAR) and differentialGlobal Positioning Systems (dGPS) surveys (e.g., [4][5][6][7][8]), video and satellite imagery (e.g., [9][10][11][12][13][14][15][16]) and recently, crowd-sourced smartphone images taken at CoastSnap stations [17]. The analysis of historical shoreline changes can combine several sources (e.g., [18][19][20][21][22]).…”

Section: Introductionmentioning

confidence: 99%

“…One advantage of using DSAS in coastal change analysis is that it can calculate the rate of change of shoreline time-series, evaluating and resolving the nature of shoreline dynamics and changing trends [39]. Besides, DSAS has numerous applications in studies of coastal behavior and shoreline dynamics, such as historical trend analysis [8,16,18,19,[40][41][42][43][44] and expected future shoreline morphology (e.g., [45,46]).…”

Section: Introductionmentioning

confidence: 99%

End Point Rate Tool for QGIS (EPR4Q): Validation Using DSAS and AMBUR

Lima

Fernández-Fernández

Espinoza

et al. 2021

IJGI

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This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS graphical modeler for calculating the shoreline change with the end point rate method. The EPR4Q tries to fill the gaps in user-friendly and free open-source tools for shoreline analysis in a geographic information system environment since the most used software—Digital Shoreline Analysis System (DSAS)—although being a free extension, it is created for commercial software. Additionally, the best free, open-source option to calculate EPR is called Analyzing Moving Boundaries Using R (AMBUR); since it is a robust and powerful tool, the complexity can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR with different types of shorelines found in nature, extracted from the US Geological Survey Open-File. The obtained results of each tool were compared with Pearson’s correlation coefficient. The validation results indicate that the EPR4Q tool acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, proving that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.

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“…Their results show that they can accurately forecast shoreline changes based on historical data. Santos et al, (2021) uses Digital Shoreline Analysis System (DSAS) to Analysis of long-and short-term shoreline change dynamics: A study case of João Pessoa city in Brazil. Over the past 34 years , this study looked at the spatiotemporal behavior of short-and long-term characteristics of the shoreline, as well as the driving mechanisms responsible for shoreline changes in Joo Pessoa.…”

Section: Introductionmentioning

confidence: 99%

Utilizing MIKE 21 Software to Create Simple Hydrodynamic Simulations

Subiyanto

Supian

2021

IJRCS

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This paper aim to create simple hydrodynamic simulation by using MIKE 21. The module used in MIKE 21 is LITPACK. LITPACK is one of the modules in MIKE 21 to solve hydraulic and sedimentation problems in coastal areas. Especially in this paper, the LITTLITE engine in LITPACK will be used. LITLINE determines the coastline position using a timeseries of wave climatic data. The model is based on a one-line theory, in which the cross-shore profile is expected to remain unaltered during erosion/accretion, with minor adjustments. Coastal morphology is thus only defined by coastline location (cross-shore direction) and coastal profile at a given long-shore position. The simulation used in this paper is the influence of groins on shoreline dynamics. The results of the simulation show that some areas will experience abrasion and some will experience accretion.

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Analysis of long- and short-term shoreline change dynamics: A study case of João Pessoa city in Brazil

Cited by 49 publications

References 87 publications

Seven decades of shoreline changes along a muddy mangrove coastline of the Upper Gulf of Thailand

Seven decades of shoreline changes along a muddy mangrove coastline of the Upper Gulf of Thailand

End Point Rate Tool for QGIS (EPR4Q): Validation Using DSAS and AMBUR

Utilizing MIKE 21 Software to Create Simple Hydrodynamic Simulations

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