SCShores: a comprehensive shoreline dataset of Spanish sandy beaches from a citizen-science monitoring programme

被引:1
|
作者
Gonzalez-Villanueva, Rita [1 ]
Soriano-Gonzalez, Jesus [2 ]
Alejo, Irene [1 ]
Criado-Sudau, Francisco [2 ]
Plomaritis, Theocharis [3 ]
Fernandez-Mora, Angels [2 ]
Benavente, Javier [4 ]
Del Rio, Laura [4 ]
Nombela, Miguel Angel [1 ]
Sanchez-Garcia, Elena [2 ]
机构
[1] Univ Vigo, Ctr Invest Marina, XM1, Vigo 36310, Spain
[2] Balearic Isl Coastal Observing & Forecasting Syst, Palma De Mallorca 07121, Spain
[3] Univ Cadiz, Fac Marine & Environm Sci, Dept Appl Phys, Puerto Real 11510, Cadiz, Spain
[4] Univ Cadiz, Fac Marine & Environm Sci, Dept Earth Sci, Puerto Real 11510, Cadiz, Spain
关键词
D O I
10.5194/essd-15-4613-2023
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Sandy beaches are ever-changing environments, as they experience constant reshaping due to the external forces of tides, waves, and winds. The shoreline position, which marks the boundary between water and sand, holds great significance in the fields of coastal geomorphology, coastal engineering, and coastal management. It is crucial to understand how beaches evolve over time, but high-resolution shoreline datasets are scarce, and establishing monitoring systems can be costly. To address this, we present a new dataset of the shorelines of five Spanish sandy beaches located in contrasting environments that is derived from the CoastSnap citizen-science shoreline monitoring programme. The use of citizen science within environmental projects is increasing, as it allows both community awareness and the collection of large amounts of data that are otherwise difficult to obtain. This dataset includes a total of 1721 individual shorelines composed of 3 m spaced points alongshore, accompanied by additional attributes, such as elevation value and acquisition date, allowing for easy comparisons. Our dataset offers a unique perspective on how citizen science can provide reliable datasets that are useful for management and geomorphological studies. The shoreline dataset, along with relevant metadata, is available at 10.5281/zenodo.8056415 (Gonz & aacute;lez-Villanueva et al., 2023b).
引用
收藏
页码:4613 / 4629
页数:17
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