Medium resolution satellite imagery as a tool for monitoring shoreline change. Case study of the Eastern coast of Ghana

Jayson-Quashigah, Philip-Neri; Addo, Kwasi Appeaning; Kodzo, Kufogbe Sosthenes

doi:10.2112/si65-087.1

Cited by 50 publications

(20 citation statements)

References 12 publications

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“…For this purpose, Digital Shoreline Analysis System (DSAS) (Thieler et al, 2009) has been used. DSAS is a plugin for ArcGIS commercial software to evaluate extracted shoreline with reference data (Jayson-Quashigah et al, 2013). The Net Shoreline Movement function of DSAS has been used for accuracy assessment which calculates perpendicular distances with defined spacing between input and reference data (Oyedotun, 2014).…”

Section: Resultsmentioning

confidence: 99%

The Efficiency of Random Forest Method for Shoreline Extraction From Landsat-8 and Gokturk-2 Imageries

Bayram

Erdem

Akpınar

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction.. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of "TUBITAK Project (Project No: 115Y718) titled" Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model -Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example ". Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and waterbody classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5 th band) and GOKTURK-2 (4 th band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies.

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Section: Resultsmentioning

confidence: 99%

The Efficiency of Random Forest Method for Shoreline Extraction From Landsat-8 and Gokturk-2 Imageries

Bayram

Erdem

Akpınar

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…To estimate the horizontal shoreline mobility, alongshore shoreline locations were manually digitized from each of the orthophotos in ARCGIS. The high watermark (HWM) [41] was adopted as the shoreline proxy for the shoreline position in order to be consistent with the marker used for shoreline analyses in the region [4][5][6]17]. The variations of the monthly shoreline locations were then determined.…”

Section: Orthophoto and Dem Generation From Uav Photogrammetrymentioning

confidence: 99%

“…Nonetheless, determining the complete values conforming to the model outcomes is challenging, partly due to the difficulties of scaling from the laboratory level to the real world, and partially because of the utilization of a wave climate different from that occurring in nature. Beach morphological surveys along the coast of Ghana, West Africa and other coastal areas of the world have always been accomplished through in-situ and topographic measurements using real-time kinematic differential Global Positioning Systems (GPS) [4][5][6]. In relation to both space and time, these surveys are intermittent, and the area covered is often small.…”

Section: Introductionmentioning

confidence: 99%

Application of Shore-Based Video and Unmanned Aerial Vehicles (Drones): Complementary Tools for Beach Studies

et al. 2020

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Video camera systems have been used over nearly three decades to monitor coastal dynamics. They facilitate a high-frequency analysis of spatiotemporal shoreline mobility. Video camera usage to measure beach intertidal profile evolution has not been standardized globally and the capacity to obtain accurate results requires authentication using various techniques. Applications are mostly site specific due to differences in installation. The present study examines the accuracy of intertidal topographic data derived from a video camera system compared to data acquired with unmanned aerial vehicle (UAV, or drone) surveys of a reflective beach. Using one year of 15-min video data and one year of monthly UAV observations, the intertidal profile shows a good agreement. Underestimations of intertidal profile elevations by the camera-based method are possibly linked to the camera view angle, rectification and gaps in data. The resolution of the video-derived intertidal topographic profiles confirmed, however, the suitability of the method in providing beach mobility surveys matching those required for a quantitative analysis of nearshore changes. Beach slopes were found to vary between 0.1 and 0.7, with a steep slope in May to July 2018 and a gentle slope in December 2018. Large but short-scale beach variations occurred between August 2018 and October 2018 and corresponded to relatively high wave events. In one year, this dynamic beach lost 7 m. At this rate, and as also observed at other beaches nearby, important coastal facilities and infrastructure will be prone to erosion. The data suggest that a low-cost shore-based camera, particularly when used in a network along the coast, can produce profile data for effective coastal management in West Africa and elsewhere.

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“…This step has been realized by Digital Shoreline Analysis System (DSAS) (Thieler et al, 2009). DSAS is a tool for evaluation a shoreline with reference data (Jayson-Quashigah et al, 2013). The Net Shoreline Movement function of DSAS has been employed for comparison which calculates perpendicular distances with defined spacing between input and reference data (Oyedotun, 2014).…”

Section: Simplification Of Extracted Shorelinementioning

confidence: 99%

Comparison of Two Simplification Methods for Shoreline Extraction From Digital Orthophoto Images

Bayram

Sen

Selbesoglu

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The coastal ecosystems are very sensitive to external influences. Coastal resources such as sand dunes, coral reefs and mangroves has vital importance to prevent coastal erosion. Human based effects also threats the coastal areas. Therefore, the change of coastal areas should be monitored. Up-to-date, accurate shoreline information is indispensable for coastal managers and decision makers. Remote sensing and image processing techniques give a big opportunity to obtain reliable shoreline information. In the presented study, NIR bands of seven 1:5000 scaled digital orthophoto images of Riga Bay-Latvia have been used. The Object-oriented Simple Linear Clustering method has been utilized to extract shoreline of Riga Bay. Bend and Douglas-Peucker methods have been used to simplify the extracted shoreline to test the effect of both methods. Photogrammetrically digitized shoreline has been taken as reference data to compare obtained results. The accuracy assessment has been realised by Digital Shoreline Analysis tool. As a result, the achieved shoreline by the Bend method has been found closer to the extracted shoreline with Simple Linear Clustering method.

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Medium resolution satellite imagery as a tool for monitoring shoreline change. Case study of the Eastern coast of Ghana

Cited by 50 publications

References 12 publications

The Efficiency of Random Forest Method for Shoreline Extraction From Landsat-8 and Gokturk-2 Imageries

The Efficiency of Random Forest Method for Shoreline Extraction From Landsat-8 and Gokturk-2 Imageries

Application of Shore-Based Video and Unmanned Aerial Vehicles (Drones): Complementary Tools for Beach Studies

Comparison of Two Simplification Methods for Shoreline Extraction From Digital Orthophoto Images

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