The Digital Shoreline Analysis System (DSAS) version 3.0, an ArcGIS extension for calculating historic shoreline cange

Thieler, E. Robert; Himmelstoss, Emily A.; Zichichi, Jessica L.; Miller, Tara L.

doi:10.3133/ofr20051304

Cited by 167 publications

(188 citation statements)

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“…The LRR can be determined by fitting a least squares regression line to all shoreline points for a particular transect as shown in Fig. 2 [5]. The regression line is placed so that the sum of the squared residuals (determined by squaring the offset distance of each data point from the regression line and adding the squared residuals together) is minimized.…”

Section: Methodsmentioning

confidence: 99%

“…The rate of shoreline change can be calculated by DSAS (Digital Shoreline Analysis System), an extension for ArcGIS. DSAS is a digital shoreline analysis tool that can be used to compute rate-of-change statistics for a time series of historical shoreline data which is developed by United States Geological Survey (USGS) [5]. The DSAS has been successfully applied in many types of beach in the world, such as sandy beach [6], silt and clay beach [3], [7].…”

Section: Introductionmentioning

confidence: 99%

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Shoreline Change Analysis of Peat Soil Beach in Bengkalis Island Based on GIS and RS

Sutikno¹,

Sandhyavitri²,

Haidar³

et al. 2017

IJET

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shoreline Change Analysis of Peat Soil Beach in Bengkalis Island Based on GIS and RS

Sutikno¹,

Sandhyavitri²,

Haidar³

et al. 2017

IJET

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“…In this stage, a short-term shoreline change and changes rate for this study were calculated by using DSAS version 4.2, a freely extension ArcGIS tool developed by the USGS [Thieler, 2009].…”

Section: The Methodsmentioning

confidence: 99%

Determination of Coastal Belt in the Disaster Prone Area: A case study in the Coastal area of Bantul Regency, Yogyakarta, Indonesia

Widianto¹,

Damen²

2014

IJG

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Mitigation strategy development is needed for protecting coastal communities and preserving coastal resources from the impact of coastal hazards. It is believed that coastal land use planning can be proposed to reduce impact of coastal hazards. The coastal belt boundary development is one of the mitigation approach which is common use in many countries as an inexpensive solution compared to hard structure development. Our study was located in the coastal area of Bantul regency, a low lying coastal area and densely populated region, where some clustered people lives proximity to the sea. In this study, a new method for determining coastal belt boundary was developed in the study area. The Spatial Multi Criteria Evaluation (SMCE) tool of Ilwis plays as powerful tool for assessing coastal vulnerability in term of multihazard events. To calculate the width of the coastal belt boundary, the Ilwis-SMCE outcomes were employed. By applying this method, there were three scenarios of the setbacks wide for both direct and pairwise method to be proposed. In general, the coastal area of Bantul Regency is dominated by high vulnerable area respectively impacted by multi-hazard events (tsunami, high wave and erosion-accretion), by incorporating some coastal features, such as topography, biophysics, economic, and social-culture. The results of the analysis are consistent with what was observed in the actual areas. Taken into account for hazard and vulnerability indices, the coastal belt boundary established in this area should wider than those already determined by existing regulations. Abstrak penyusunan strategi mitigasi diperlukan untuk melindungi masyarakat pesisir dan melestarikan sumber daya pesisir dari dampak bahaya pesisir. Perencanaan penggunaan lahan daerah pesisir dapat diusulkan untuk mengurangi dampak bahaya pesisir. Pengembangan batas sempadan pantai merupakan salah satu pendekatan mitigasi yang umum digunakan

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“…Thus, linear distances between the 10-m level curve and the current coastline have been calculated. Then, we created a raster layer based on the standard deviation of the distances to better identify advances or retreats of the coastline [20].…”

Section: Coastal Vulnerability Indexmentioning

confidence: 99%

“…The 2017 coastline has been taken as a reference to assess its temporal evolution (both regression and accretion). High growth rates corresponds to high vulnerability values, while negative growth rates with low values [20]. Swell Factor: Average Rate of Significant Waves (Fo).…”

Section: Coastal Vulnerability Indexmentioning

confidence: 99%

Analysis of Flood Risk Due to Sea Level Rise in the Menor Sea (Murcia, Spain)

et al. 2018

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This article analyzes the coastal vulnerability and flood risk due to sea level rise in the Menor Sea, Murcia (Spain). The vulnerability has been estimated from Sentinel-2 and Landsat 8 satellite imagery using Remote Sensing techniques. The risk of coastal flooding was calculated based on various time scenarios (X 0 -current, X 1 -100 years, X 2 -500 years, X 3 -1000 years, X 4 -Storm, X 5 -Tsunami). Geographic Information System and Remote Sensing techniques were used to build a regional model to predict changes in the mean sea level for several future scenarios, showing susceptible areas to be flooded. We have included new parameters to the model such as swell, mareal range or neotectonic factors aiming to better adjust it to the local conditions. The results showed a high risk of flooding in the barrier beach and coastal areas of the Menor Sea, with a medium to very high degree of vulnerability for the most populated and touristic areas. The maximum and minimum expected increase of the water sheet for the 100 year scenarios ranged from +4.22 to +5.69 m. This methodology can establish sectors that need structural measures to minimize the impact of the sea level rise occurring due to natural tendency in the short or long term, as well as by extreme events such as storm surges or tsunamis. Furthermore, it can be used in other areas to assist land management decision makers to reduce or mitigate the vulnerability and risk presented against the rise of the sea level.

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The Digital Shoreline Analysis System (DSAS) version 3.0, an ArcGIS extension for calculating historic shoreline cange

Cited by 167 publications

References 0 publications

Shoreline Change Analysis of Peat Soil Beach in Bengkalis Island Based on GIS and RS

Shoreline Change Analysis of Peat Soil Beach in Bengkalis Island Based on GIS and RS

Determination of Coastal Belt in the Disaster Prone Area: A case study in the Coastal area of Bantul Regency, Yogyakarta, Indonesia

Analysis of Flood Risk Due to Sea Level Rise in the Menor Sea (Murcia, Spain)

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