Assessing Shallow Soft Deposits through Near-Surface Geophysics and UAV-SfM: Application in Pocket Beaches Environments

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Abstract

This study employs a multimethod approach to investigate the sediment distribution in two pocket beaches, Ramla Beach and Mellieha S Beach, in Malta. Both study sites were digitally reconstructed using unmanned aerial vehicle (UAV) photogrammetry. For each case, an ERT and a dense network of ambient seismic noise measurements processed through a horizontal-to-vertical spectral ratio (HVSR) technique were acquired. Electrical resistivity tomography (ERT) analysis enables the estimation of sediment thickness in each beach. HVSR analysis revealed peaks related to beach sediments overlying limestone rocks in both sites and also indicated a deeper stratigraphic contact in Mellieha S Beach. Based on ERT measurements, sediment thickness is calculated for each HVSR measurement. Interpolation of results allows for bedrock surface modelling in each case study, and when combined with digital terrain models (DTMs) derived from photogrammetric models, sediment volumes are estimated for each site. The geometry of this surface is analyzed from a geological perspective, showing structural control of sediment distribution due to a normal fault in Mellieha S Beach and stratigraphic control facilitated by a highly erodible surface in Ramla Beach. The results emphasize the importance of adopting a three-dimensional perspective in coastal studies for precise sediment volume characterization and a deeper understanding of pocket beach dynamics. This practical multimethod approach presented here offers valuable tools for future coastal research and effective coastal management, facilitating informed decision making amidst the growing vulnerability of coastal zones to climate change impacts.

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APA

Galone, L., D’Amico, S., Colica, E., Iregbeyen, P., Galea, P., Rivero, L., & Villani, F. (2024). Assessing Shallow Soft Deposits through Near-Surface Geophysics and UAV-SfM: Application in Pocket Beaches Environments. Remote Sensing, 16(1). https://doi.org/10.3390/rs16010040

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