Group 2 was based on tidal data (e.g., mean high water or mean sea level) with studies such as Crapoulet et al. Group 1included those indicators that are based on visible coastal features (e.g., an earlier high-tide line or the wet/dry boundary). Boak and Turner classified shoreline indicators into 3 groups. As emphasized by Boak and Turner, shoreline indicator is utilized as a proxy to show shoreline position. The changing nature of the shoreline position drew the attention of coastal researchers to develop and adopt shoreline indicators.
Credit in this sense, should, thus, be given to earlier geoscientists such as Carr, de Boer and Carr, El-Ashry and Wanless, and Gulliver, whose work contributed to the advancement of information on shoreline change. This has meant that the term shoreline change is not limited to only the coast but encompasses lake and lagoon environments as well. Shoreline position detection is, thus, important especially considering the long history of human habitation of the coast and the banks of large waterbodies and their recent adaptation.
An understanding of the temporal and time scales in shoreline position is essential for science, engineering, and coastal managers. The temporal nature and time scale of shorelines must, therefore, be considered in shoreline investigation.
The position of the shoreline changes through time due to cross-shore and alongshore sediment movement in the littoral zone, and through changes in water levels. While this definition looks simple, it is indeed challenging in its practical application. For instance, the one on insights for reef restoration projects focused on erosion mitigation and designing artificial reefs in microtidal sandy beaches.Ī shoreline is the point of the physical border between land and water. Found research gaps were mostly addressed by the researchers themselves or addressed in other studies, while others have still not been addressed, especially the ones emerged from the recent work. Considering the benefits of these geospatial tools, and machine learning in particular, more utilization is essential to the continuous growth of the field. The results further revealed that tools for shoreline change analysis have changed from a simple beach transect (0.1%) to the utilization of geospatial tools such as DSAS (14.6%), ArcGIS/ArcMap (13.8%), and, currently, machine learning (5.1%). Again, more country collaborations exist among the developed countries compared with the developing countries. is the country with the most scientific production (16.9%) on the subject. Thus, there could be a bias in the present results due to the search criteria here employed. There is a chance that in the selection process one or more important scientific papers might be omitted due to the selection criteria. The bibliometric mapping method (bibliometric R and VOSviewer package) was utilized to analyze 1578 scientific documents (1968–2022) retrieved from Scopus and Web of Science databases. This study is a bibliometric analysis of the global scientific production of data sources and tools for shoreline change analysis and detection. This has made coastal systems and large inland water environments vulnerable, thereby activating research concern globally. The world has a long record of shoreline and related erosion problems due to the impacts of climate change/variability in sea level rise.