Abstract
The applications of remote sensing in monitoring land cover features are an essential tool of natural resources management schemes. The sedimentation mapping of shallow shorelines is insufficient using passive remote sensing images because of the image corrections and weather implications that need to be considered, while active remote sensing data can overcome the difficulties of the weather interference and reach to more reliable results. The current research work took place in the shoreline on Umluj city, west of Saudi Arabia, representing one of the most sensitive wetland habitats within the country. Sentinel-1 images were downloaded and analyzed to delineate the sedimentation process from the European Space Agency. The archive image was acquired in August 2018, while the crisis emerged was acquired in March 2019 after an unusual rainfall event that took place in the vicinity of the study area. Remote sensing techniques of sedimentation mapping end change detection were implemented in the study area to estimate the sedimentation process and its influences on the wetlands. The wetland habitats were decreased by nearly 87% throughout the period of flash floods between November 2018 and March 2019. Meanwhile, sediment deposits along the shoreline of the study area increased by nearly 171%. Therefore, monitoring of the shorelines sedimentation and the wetland habitats using temporal remote sensing data are decision-making priorities to conserve the natural resources within similar arid environments.
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Data availability
The Sentinel-1 data used to support the findings of this study have been freely accessed from the European Space Agency website “https://scihub.copernicus.eu/dhus/#/home”.
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Acknowledgements
This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant No. (D-006-155-1440). The authors, therefore, acknowledge with thanks, DSR technical and financial support.
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Elhag, M., Bahrawi, J.A. Sedimentation mapping in shallow shoreline of arid environments using active remote sensing data. Nat Hazards 99, 879–894 (2019). https://doi.org/10.1007/s11069-019-03780-4
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DOI: https://doi.org/10.1007/s11069-019-03780-4