Shoreline Definition and Detection: A Review

Boak, Elizabeth H.; Turner, Ian L.

doi:10.2112/03-0071.1

Cited by 1,104 publications

(771 citation statements)

References 21 publications

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“…The shoreline evolution was determined by superimposing the shorelines extracted from aerial The location of the shoreline was defined based on the previous high tide high water level (Boak and Turner, 2005). As a basic approximation, considering a 1:8 reflective slope with 1.7 m tide range, we can assume a lateral error of about 6.8 m on the shoreline detection.…”

Section: Shoreline Variationmentioning

confidence: 99%

Planform and mobility in the Meaípe-Maimbá embayed beach on the South East coast of Brazil

2016

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Section: Shoreline Variationmentioning

confidence: 99%

Planform and mobility in the Meaípe-Maimbá embayed beach on the South East coast of Brazil

2016

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“…Third-grade 193 polynomic correction with two-dimensional ground control points (GCPs) was used for 194 photo georeferencing in ESRI® ArcGIS 9.3 software. GCPs were carefully chosen 195 feature that can serve as a shoreline indicator or proxy, so that it properly reflects real 204 shoreline position and evolution (Moore, 2000;Boak and Turner, 2005). In this work 205 the high-water line (HWL) and the dune foot were used as shoreline proxies.…”

Section: Approximate Position Of Table 1 188 189mentioning

confidence: 99%

“…In this work 205 the high-water line (HWL) and the dune foot were used as shoreline proxies. The HWL 206 constitutes the most widely used shoreline proxy (Boak and Turner, 2005), and is 207 usually considered equivalent to the last high tide mark or the wet/dry line identifiable 208 on beach sand on the photographs (Crowell et al, 1997). Despite its limitations 209 regarding short-term variability, it is generally deemed as a valid indicator of shoreline 210 position (Gorman et al, 1998), and so it was used along the whole Atlantic coast of 211…”

Section: Approximate Position Of Table 1 188 189mentioning

confidence: 99%

Shoreline change patterns in sandy coasts. A case study in SW Spain

2013

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12 13Coastal changes on sandy shorelines are continuous and occur at diverse spatial and 14 temporal scales. Gaining knowledge on beach change processes increases our capability 15 to manage risks affecting the increasing population living in coastal areas, especially 16 shoreline erosion. Processes and factors involved in medium-and short-term beach 17 changes depend on the morphological and dynamic characteristics of the coast. In this 18 work, the decadal behaviour of 58 sandy beaches along the 150 km long South-Atlantic 19 coast of Spain, between Guadalquivir river mouth and the Strait of Gibraltar, is analysed 20 in order to investigate the relationships between shoreline change patterns and the 21 diverse morphological and dynamic factors controlling beach evolution in the area. For 22 this purpose, georectified aerial photographs spanning the period 1956-2008 were 23 compared in a GIS environment to calculate rates of shoreline change. Short-term 24 evolution of beach profiles was also analysed in selected areas of interest. 25 Manuscript Click here to view linked ReferencesResults show that the study area exhibits a great variety of shoreline evolution trends, 26 with prevailing erosion in the northern and central sectors and stability or even accretion 27 in the southern sector. In general, sediment availability is the main factor determining 28 coastal erodibility in the area, largely conditioned by the reduction in fluvial sediment 29 supply caused by river basin regulation. Nearshore bathymetry also has a great 30 significance, as it controls wave refraction-diffraction patterns and wave energy 31 concentration on certain zones. Human interventions on the coast also represent a major 32 influence on beach erodibility in the study area. Severe detrimental effects are caused at 33 certain points by shore-normal engineering structures blocking longshore drift. 34Additionally extensive urban development in backbeach environments has a significant 35 influence on sediment budget at certain areas. 36On the basis of these results, a morphological and evolutive classification of sandy 37 beaches is proposed, taking into account the way beach morphology influences 38 erosive/accretionary processes. Rectilinear beaches and enclosed beaches typically 39show dynamic equilibrium or even accretion trends, whereas reef-supported beaches 40 tend to be dominated by erosion. Headland-bay beaches show complex evolution 41 patterns greatly influenced by local conditions, such as specific shoaling processes or 42 local winds. This classification is useful not only in forecasting general shoreline 43 behaviour in the near future, but also in selecting the most adequate type of intervention 44 when managing retreating coasts. 45 46

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“…Shoreline is the line where the land and water bodies meet and is called boundary separation between both (Boak and Turner, 2005;Dolan et al, 1991). In the coastal area, it is considered to be one of the most dynamic regions because it is mainly attributed to the natural Earth phenomena such as tide effect, wind speed, wind direction, sea level rise, natural disaster and anthropogenic processes (Mustaqim et al, 2014;Luu et al, 2015;Li, et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Before shoreline is acquired, we have to determine the shoreline indicator which represents the true shoreline position. The shoreline indicators are categorized into three groups: Detectable visually features, tidal datum-based indicators and indicators based on processing method to extract shoreline (Boak and Turner, 2005;Li et al, 2002;Gens, 2010).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Classification Techniques on Fused Optical and SAR Images for Shoreline Extraction: A Case Study at Northeast Coast of Peninsular Malaysia

Manaf¹,

Mustapha²,

Sulaiman³

et al. 2016

Journal of Computer Science

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Shoreline is a very important element to identify exact boundary at the coastal areas of a country. However, in order to identify land-water boundary for a large region using traditional ground survey technique is very time consuming. Alternatively, shoreline can be extracted by using satellite images that minimizes the mapping errors. The trend of extracting shoreline has been shifted from image processing to machine learning and data mining techniques. By using machine learning technique, the satellite images could be classified into land and water classes in order to extract shoreline. However, the result is meaningless if it has cloud and shadow on the water-land boundary. In this study, we compare the accuracy and Kappa Coefficient of six machine learning techniques namely Maximum Likelihood, Minimum Distance, Mahalanobis Distance, Parallelepiped, Neural Network and Support Vector Machines on three type of images; single optical multispectral, single SAR and fused image. A case study for this research is done alongside Tumpat beach, located at the Northeast Coast of Peninsular Malaysia. All the machine learning techniques have been tested on the three types of images. The experimental results show that classification using SVM on single multispectral image has the highest accuracy among all. However, the classified of fused image using SVM is considered much more accurate because it can cater the cloud and shadow problem. Additionally, the classification on 5 and 10 m fused images also tested and the result shows that with the increase of spatial resolution of fused image, the classification accuracy also increases.

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Shoreline Definition and Detection: A Review

Abstract: BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Cited by 1,104 publications

References 21 publications

Planform and mobility in the Meaípe-Maimbá embayed beach on the South East coast of Brazil

Planform and mobility in the Meaípe-Maimbá embayed beach on the South East coast of Brazil

Shoreline change patterns in sandy coasts. A case study in SW Spain

Comparison of Classification Techniques on Fused Optical and SAR Images for Shoreline Extraction: A Case Study at Northeast Coast of Peninsular Malaysia

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