Spatial–temporal analysis of the shoreline change rate using automatic computation and geospatial tools along the Tetouan coast in Morocco

Benkhattab, Fatima Zahra; Hakkou, Mounir; Bagdanavičiūtė, Ingrida; Mrini, Abdelmounim El; Zagaoui, Hafid; Rhinane, Hassan; Maanan, Mehdi

doi:10.1007/s11069-020-04179-2

Cited by 31 publications

(15 citation statements)

References 46 publications

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“…The morphological changes of sandy beaches occur rapidly on a spatio-temporal scale as a response to natural (wind direction and speed, wave climate, sea-level fluctuations, etc.) processes [53]. Signs of climate change in the Baltic Sea can be more than just seawater level rise [54][55][56], increase in storminess [1], but also changes in the predominant wind and wave climate [43].…”

Section: Discussionmentioning

confidence: 99%

Natural and Anthropogenic Factors Shaping the Shoreline of Klaipėda, Lithuania

Kondrat

Šakurova

Baltranaitė

et al. 2021

JMSE

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Port of Klaipėda is situated in a complex hydrological system, between the Curonian Lagoon and the Baltic Sea, at the Klaipėda strait in the South-Eastern part of the Baltic Sea. It has almost 300 m of jetties separating the Curonian Spit and the mainland coast, interrupting the main path of sediment transport through the South-Eastern coast of the Baltic Sea. Due to the Port of Klaipėda reconstruction in 2002 and the beach nourishment project, which was started in 2014, the shoreline position change tendency was observed. Shoreline position measurements of various periods can be used to derive quantitative estimates of coastal process directions and intensities. These data can be used to further our understanding of the scale and timing of shoreline changes in a geological and socio-economic context. This study analyzes long- and short-term shoreline position changes before and after the Port of Klaipėda reconstruction in 2002. Positions of historical shorelines from various sources were used, and the rates (EPR, NSM, and SCE) of shoreline changes have been assessed using the Digital Shoreline Analysis System (DSAS). An extension of ArcGIS K-means clustering was applied for shoreline classification into different coastal dynamic stretches. Coastal development has changed in the long-term (1984–2019) perspective: the eroded coast length increased from 1.5 to 4.2 km in the last decades. Coastal accumulation processes have been restored by the Port of Klaipėda executing the coastal zone nourishment project in 2014.

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Section: Discussionmentioning

confidence: 99%

Natural and Anthropogenic Factors Shaping the Shoreline of Klaipėda, Lithuania

Kondrat

Šakurova

Baltranaitė

et al. 2021

JMSE

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“…Based on this, Keane et al proposed a hierarchical k-means tree index structure to compare the similarity between leaf nodes, which is suitable for large-scale image retrieval [13]. Another method is the locality-sensitive hashing-(LSH-) based method proposed in recent years to use hash learning in image retrieval, which is an approximate nearest neighbor search method [14] and improves the user's interactive experience, thereby helping to better develop the tourism industry. The analysis of the ideal intelligent scenic spot construction system needs to start from the perspective of tourists.…”

Section: Current Status Of Researchmentioning

confidence: 99%

Large-Scale Image Retrieval of Tourist Attractions Based on Multiple Linear Regression Equations

Song

2021

Advances in Mathematical Physics

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This paper presents an in-depth study and analysis of large-scale tourist attraction image retrieval using multiple linear regression equation approaches. This feature extraction method often relies on the partitioning of the grid and is only effective when the overall similarity of different images is high. The BOF model is borrowed from the method for text retrieval, which generally extracts the local features of an image by the scale-invariant feature transform algorithm and clusters them using k -means to obtain a low-dimensional visual dictionary and characterizes the image features with a histogram vector based on the visual dictionary. However, when there are many kinds of images, the dimensionality of the visual dictionary will be large and it is not convenient to construct the BOF model. The last fully connected layer is taken as the image feature, and it is dimensionalized by the principal component analysis method, and then, the low-dimensional feature index structure is constructed using the locality-sensitive hashing- (LSH-) based approximate nearest neighbor algorithm. The accuracy of our graph retrieval has increased by 8%. The advantages of feature extraction by a convolutional neural network and the high efficiency of a hash index structure in retrieval are used to solve the shortcomings of traditional methods in terms of accuracy and other aspects in image retrieval. The results show that compared with the above two algorithms, for most of the attractions, the method has a relatively obvious advantage in the accuracy of retrieval, and when there are few similar images of a particular attraction in the attraction image library, the accuracy of the query results is not much different from the first two methods.

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“…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 [18]. Besides, DSAS has numerous applications in studies of coastal behavior and shoreline dynamics as historical trend analysis [19][20][21][22][23], and the expected future shoreline morphology (e.g., [24,25]).…”

Section: Dsasmentioning

confidence: 99%

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

Lima¹,

Fernández-Fernández²,

Espinoza³

et al. 2021

Preprint

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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 to calculate the shoreline change by End Point Rate method. The EPR4Q tries to fill the gap of user-friendly and free open-source tool for shoreline analysis in Geographic Information System environment, since the most used software - Digital Shoreline Analysis System (DSAS) - although is a free extension, is suited for commercial software. Besides, the best free open-source option to calculate EPR called Analyzing Moving Boundaries Using R (AMBUR), since it is a robust and powerful tool, the complexity and heavy processes can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR on different examples of shorelines found in nature, extracted from the U.S. Geological Survey Open-File. The obtained results of each tool were compared with Pearson correlation coefficient. The validation results indicate that the EPR4Q tool created acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, guarantying that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.

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Spatial–temporal analysis of the shoreline change rate using automatic computation and geospatial tools along the Tetouan coast in Morocco

Cited by 31 publications

References 46 publications

Natural and Anthropogenic Factors Shaping the Shoreline of Klaipėda, Lithuania

Natural and Anthropogenic Factors Shaping the Shoreline of Klaipėda, Lithuania

Large-Scale Image Retrieval of Tourist Attractions Based on Multiple Linear Regression Equations

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

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