Sediments moved by wind, rivers, and waves round in the same way due to geometry.

Nowadays, the employment of high-resolution Digital Surface Models (DSMs) and RGB orthophotos has become fundamental in coastal system studies. This work aims to explore the potentiality of low-cost Unmanned Aerial Vehicle (UAV) surveys to monitor the geomorphic and vegetation state of coastal sand dunes by means of high-resolution (2–4 cm) RGB orthophotos and DSMs. The area of study (Punta Marina, Ravenna, Italy), in the North Adriatic Sea, was considered very suitable for these purposes because it involves a residual coastal dune system, damaged by decades of erosion, fragmentation and human intervention. Recently, part of the dune system has been involved in a restoration project aimed at limiting its deterioration. RGB orthophotos have been used to calculate the spectral information of vegetation and bare sand and therefore, to monitor changes in their relative cover area extension over time, through the using of semi-automatic classification algorithms in a GIS environment. Elevation data from high-resolution DSMs were used to identify the principal morphological features: (i) Dune Foot Line (DFL); (ii) Dune Crest Line (DCL); Dune seaward Crest Line (DsCL); Stable Vegetation line (SVL). The USGS tool DSAS was used to monitor dune dynamics, considering every source of error: a stable pattern was observed for the two crest lines (DCL and DsCL), and an advancing one for the others two features (DFL and SVL). Geomorphological data, as well as RGB data, confirmed the effectiveness of planting operations, since a constant and progressive increase of the vegetated cover area and consolidation of the dune system was observed, in a period with no energetic storms. The proposed methodology is rapid, low-cost and easily replicable by coastal managers to quantify the effectiveness of restoration projects.

Recent advances in structure-from-motion (SfM) techniques have proliferated the use of unmanned aerial vehicles (UAVs) in the monitoring of coastal landform changes, particularly when applied in the reconstruction of 3D surface models from historical aerial photographs. Here, we explore a number of depth map filtering and point cloud cleaning methods using the commercial software Agisoft Metashape Pro to determine the optimal methodology to build reliable digital surface models (DSMs). Twelve different aerial photography-derived DSMs are validated and compared against light detection and ranging (LiDAR)- and UAV-derived DSMs of a vegetated coastal dune system that has undergone several decades of coastline retreat. The different studied methods showed an average vertical error (root mean square error, RMSE) of approximately 1 m, with the best method resulting in an error value of 0.93 m. In our case, the best method resulted from the removal of confidence values in the range of 0–3 from the dense point cloud (DPC), with no filter applied to the depth maps. Differences among the methods examined were associated with the reconstruction of the dune slipface. The application of the modern SfM methodology to the analysis of historical aerial (vertical) photography is a novel (and reliable) new approach that can be used to better quantify coastal dune volume changes. DSMs derived from suitable historical aerial photographs, therefore, represent dependable sources of 3D data that can be used to better analyse long-term geomorphic changes in coastal dune areas that have undergone retreat.

Coastal communities and assets are exposed to flooding and erosion hazards due to extreme storm events, which may increase in intensity due to climatological factors in the incoming future. Coastal managers are tasked with developing risk-management plans mitigating risk during all phases of the disaster cycle. This necessitates rapid, time-efficient post-event beach surveys that collect physical data in the immediate aftermath of an event. Additionally, the inclusion of local stakeholders in the assessment process via personal interviews captures the social dimension of the impact of the event. In this study, a local protocol for post-event assessment, the quick-response protocol, was tested on a pilot site on the Emilia–Romagna (Italy) coast in the aftermath of an extreme meteorological event that occurred in February 2015. Physical data were collected using both real-time kinematic Geographical Positions Systems and unmanned aerial vehicle platforms. Local stakeholders were interviewed by collecting qualitative information on their experiences before, during, and after the event. Data comparisons between local and regional surveys of this event highlighted higher data resolution and accuracy at the local level, enabling improved risk assessment for future events of this magnitude. The local survey methodology, although improvable from different technical aspects, can be readily integrated into regional surveys for improved data resolution and accuracy of storm impact assessments on the regional scale to better inform coastal risk managers during mitigation planning.