Shoreline Rotation Analysis of Embayed Beaches by Means of In Situ and Remote Surveys

Abstract

The objective of the present study is to achieve a better understanding of the possible rotation of embayed beaches using shoreline position data from two beaches on the central Tyrrhenian Sea (Italy) during a 64-year period. With this aim, this study tests the utility of Differential Global Positioning System (DGPS) and GPS RTK (Real Time Kinematic) dual-frequency navigation system for the in situ surveys, low-altitude aerial imagery collected by Unmanned Aerial Vehicle (UAV), and satellite polarimetric Synthetic Aperture Radar (SAR) measurements. The dataset consists of aerial photographs and orthophotos, DGPS, and remote surveys performed by UAV and SAR platform in 2018 along with wave data coming from the Ponza buoy. The results of the field data analysis explained a part of the beach variations in terms of coastline rotation around a virtual pivotal point. This rotation has been correlated with the wave directional shift in the recent period. The results of the comparison between the different techniques gave the possibility to investigate the limitations of remote survey methods for the identification of shoreline rotation.

Statistical Deviations in Shoreline Detection Obtained with Direct and Remote Observations

“…The highest H s value was observed in December 1999, in agreement with Piscopia [53]. The selection of the ten highest storms showed that the directions of the storm waves were confined between 238 • N and 272 • N. This result is consistent with the one obtained with the selection of all the storms (H s > 2 m) highlighted in red in Figure 6, showing that the highest waves were associated with southwestern, western, and northwestern directions [37]. With regard to astronomical sea level variations, the study area experiences a typical semi-diurnal tide, with a mean tidal range of 0.35 m, following the official Italian tide archives (http://www.mareografico.it, last access: 30 April 2019).…”

“…Based on the difference in beach width and berm height, the studied beach can be subdivided into the following two stretches: a western beach stretch including profiles T1 and T2, which is 45-60 m wide and characterized by lower berm height (1.10-1.30 m) and a mean emerged beach slope of 5.5%; an eastern beach stretch including profiles T3, T4, and T5, which is characterized by higher beach width of 74-87 m and higher berm height of 1.29-1.46 m, with a lower emerged beach slope of 1.48-3.94%. This different beach width was already explained by Di Luccio et al [37] in terms of partial clockwise beach rotation around a central pivotal point.…”

“…The study area includes the beach of Serapo, located in the Central Thyrrenian Sea (Italy), which exhibits a rather regular morphology and rectilinear shoreline that is complexly long, approximately 1.45 km (Figure 1), aligned in the NW-SE direction (for the statistical processing, we considered only a 1.34 km-long shoreline, directly sampled with GPS). This beach is characterized by homogeneous grain size features with median diameter around 0.38 mm, indicating medium sands [37]. The topographic survey data, acquired in September 2017, refer to coastline and to five beach profiles T1-T5 as reported in Table 1.…”

The “INNOVARE” Project: Innovative Plants for Distributed Poly-Generation by Residual Biomass

“…An inventory of potential sites for biomass collection in protected green areas is carried out through an innovative approach that exploits microwave satellite remote sensing. This technology provides non-invasive, non-cooperative and cost-effective synoptic observations of the Earth's surface that can support the management of both marine and terrestrial ecosystems [33,34]. Indeed, it was shown that spaceborne microwave remote sensing is a tool with considerable potential for observing wooden areas, forest reserves, and protected zones preserving the biodiversity and biomass heritage at relatively low costs, without direct interventions in unsafe/inaccessible locations and harsh environments [35].…”

Impact of Hurricane Maria on Beach Erosion in Puerto Rico: Remote Sensing and Causal Inference

“…Shoreline extraction for individual images (Step 3) will then be constrained to take place within the classification mask. Unsupervised classification. A machine learning algorithm (weka ‐means Witten et al, ) is used to partition the pixels within the classification mask into two clusters using both the VV and VH polarizations (Di Luccio et al, ). These clusters are labeled as “land” and “water” under the assumption that the water‐sand interface is the dominant source of backscattering contrast within that small region around the time‐averaged coastline. Long‐shore averaged shoreline position.…”