The Digital Shoreline Analysis System (DSAS) Version 4.0 - An ArcGIS extension for calculating shoreline change

Thieler, E. Robert; Himmelstoss, Emily A.; Zichichi, Jessica L.; Ergul, Ayhan

doi:10.3133/ofr20081278

Cited by 694 publications

(481 citation statements)

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“…A linear regression rate (LRR) for each transect was calculated by fitting a least squares regression to shoreline date versus horizontal distance from the baseline. The slope of the regression line is the change in shoreline position as a function of time (Morton et al 2004;Thieler et al 2009). The mean rate of change for each marsh shoreline was determined by averaging the LRRs for each transect across the entire length of the marsh edge.…”

Section: Shoreline Change Analysismentioning

confidence: 99%

“…The Digital Shoreline Analysis System (DSAS), an extension of the geographic information system (GIS) software ArcGIS 9.3 (ESRI, Redlands, CA), was designed to compute shoreline rate-of-change statistics (Thieler et al 2009). DSAS has been used to analyze changes in shoreline position in a range of coastal systems, including sandy beaches (Thieler and Danforth 1994;Morton et al 2005;Esteves et al 2009), coastline cliffs (Addo et al 2008;Brooks and Spencer 2010), estuarine and lagoon boundaries (Cowart et al 2010;Kuleli 2010), and coastal wetlands (Borrelli 2009;Gorokhovich and Leiserowiz 2012).…”

Section: Introductionmentioning

confidence: 99%

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Rates and Forcing of Marsh Edge Erosion in a Shallow Coastal Bay

McLoughlin

Wiberg

Safak

et al. 2014

Estuaries and Coasts

103

113

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Salt marshes can experience a significant land loss through erosion and retreat of their perimeter edges. Rates of shoreline change between 1957 and 2007 were determined for four salt marshes in a Virginia coastal bay using aerial photographs and the Digital Shoreline Analysis System (DSAS). High average rates of lateral erosion of 1.0-1.6 m year −1 were found at three marshes, while the edge of the fourth marsh, along the mainland edge of the bay, remained stable. Erosion rates were temporally consistent during the 50-year period at the three eroding sites, although there was a significant spatial variation in rates of change along the length of the edges at these sites. A simple parametric wave model and the SWAN (Simulating WAves Nearshore) spectral wave model were used to calculate incident wave energy flux along the marsh boundaries at each of the sites. Values of wave energy flux agreed fairly well between the two models but are sensitive to the manner in which wave energy flux is calculated. A stronger relationship was found between wave energy flux and volumetric erosion rates along the marsh edges than with lateral erosion rates. This is an important consideration when examining the effects of future sea level rise on marsh loss.

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Section: Shoreline Change Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rates and Forcing of Marsh Edge Erosion in a Shallow Coastal Bay

McLoughlin

Wiberg

Safak

et al. 2014

Estuaries and Coasts

103

113

View full text Add to dashboard Cite

show abstract

“…The digital cartography consists in vector information residing in a constantly updated GIS platform, and includes railroad network, road network, infrastructure and other geographical features. Additional georeferenced points were obtained from topographic surveys performed in years 1932, 1939, 1941, 1964 and 1971. The multi-temporal coastline positions were detected using the edge of vegetation as a proxy to map shoreline positions (Ford, The multi-temporal analysis of shoreline changes was performed using the Digital Shoreline Analysis System (DSAS 4.3) (Thieler, Himmelstoss, Zichichi, & Ergul, 2009). DSAS allows computing rate-of-change statistics from multiple historic shoreline positions residing in a GIS.…”

Section: Methodsmentioning

confidence: 99%

Coastal erosion and loss of wetlands in the middle Río de la Plata estuary (Argentina)

2016

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“…Photo interpretation of aerial photographs from six time periods between 1961 and 2006 was undertaken, using georeferenced images (GDA-1994-MGA-Zone 56). Time-series analysis using the digital shoreline analysis system (DSAS) software version 3.2, an ArcGIS extension for calculating historic shoreline change (Thieler et al 2005), was carried out in order to track changes in vegetation and high water lines for 11 beaches along the Illawarra coast. It could be argued that the pattern of shoreline change observed is not so much a physical attribute of the section of shoreline under study but rather a manifestation or outcome of the interaction between process and structure.…”

Section: Methods Of Study and Ranking Of Variablesmentioning

confidence: 99%

Assessing vulnerability to sea-level rise using a coastal sensitivity index: a case study from southeast Australia

2010

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Many of the world's coasts appear vulnerable to the impacts of climate change and sea-level rise. This paper assesses the application of a coastal sensitivity index (CSI) to the Illawarra coast, a relatively well-studied shoreline in southeast Australia. Nine variables, namely (a) rock type, (b) coastal slope (c) geomorphology (d) barrier type (e) shoreline exposure (f) shoreline change (g) relative sealevel rise (h) mean wave height and (j) mean tide range, were adopted in calculation of the CSI (the square root of the product of the ranked variables divided by the number of variables). Two new variables, shoreline exposure and barrier type, were trialled in this analysis and the extent to which these increased the discriminatory power of the index was assessed. Four iterations of the CSI were undertaken using different combinations of ranked variables for each of 105 cells in a grid template, and the index values derived were displayed based on quartiles, indicating sections of coast with very high, high, moderate and low sensitivity. Increasing the number of variables increased the discriminatory power of the index, but the broad pattern and the rank order were very similar for each of the iterations. Rocky and cliffed sections of coast are least sensitive whereas sandy beaches backed by low plains or dunes record the highest sensitivity. It is difficult to determine shoreline change on this coast, because individual storms result in substantial erosion of beaches, but there are prolonged subsequent periods of accretion and foredune rebuilding. Consequently this variable is not a good indicator of shoreline sensitivity and the index is unlikely to provide a clear basis for forecasting future recession of beaches. The results of this study provide a framework for coastal managers and planners to prioritize efforts to enhance the resilience or consider adaptation measures in the coastal zone within a study region. Sensitivity of the coast if considered in conjunction with other social factors may be an input into broader assessments of the overall vulnerability of coasts and their communities.

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The Digital Shoreline Analysis System (DSAS) Version 4.0 - An ArcGIS extension for calculating shoreline change

Cited by 694 publications

References 0 publications

Rates and Forcing of Marsh Edge Erosion in a Shallow Coastal Bay

Rates and Forcing of Marsh Edge Erosion in a Shallow Coastal Bay

Coastal erosion and loss of wetlands in the middle Río de la Plata estuary (Argentina)

Assessing vulnerability to sea-level rise using a coastal sensitivity index: a case study from southeast Australia

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