Satellite-Derived Shoreline Analysis of the Emilia-Romagna Coast (Italy) from 1984 to 2023

Understanding shoreline variability and trends over time is essential for effective coastal management. However, studying the dynamic nature of the shoreline, defined as the intersection of water and land surfaces, can be quite complex due to various non-linear processes that operate across different temporal and spatial scales. In this context, the advent of satellite imagery has created new opportunities for long-term shoreline analysis by providing global coverage with high temporal resolution and enabling the acquisition of historical datasets. Typical methodologies using these data sources commonly involve the creation of satellite-derived shorelines (SDS) time series, which offer multidecadal records of variability, trends, and changes with a cross-shore accuracy of approximately 10 m on microtidal beaches.

In this study, SDS positions along the Emilia–Romagna (ER) coast in the northern Adriatic Sea were reconstructed using the CoastSat toolbox, incorporating both Landsat (5–9) and Sentinel–2 images for the entire period from 1984 to 2023. The ER coast is not only a significant tourist destination in Italy, but it is also increasingly exposed to erosion and coastal flooding due to the combined effects of low average heights, subsidence, sea–level rise, and urbanization. Consequently, a large portion of the coastline is artificially protected through various defense strategies, including both defense structures and nourishment measures, and stacked by long piers and jetties. This setting was considered in the analysis since it introduces a main bias in the coastal evolution and in shoreline variability.

A dataset of 2200 cross-shore transects, spaced 50 meters apart, was automatically generated based on the local orientation of the beach, and shoreline positions were reconstructed from the cross-shore distances computed along each transect. In particular, the large number of available instantaneous shorelines was used to compute annually averaged positions. Corrections for tidal and wave setups were applied to reduce the main sources of error in SDS. To achieve this, the average beach face slopes were derived from available topo-bathymetric data by Arpae-ER. Local measurements from tide gauges (TG) in Marina di Ravenna and Porto Garibaldi and from the Nausicaa (I and II) buoys were used to derive the other processing parameters.

The resulting annually averaged shorelines enabled the analysis of long-term shoreline trends from 1984 to 2023, as well as the assessment of interannual shoreline variability. Shoreline advancement during the study period, despite sea-level rise and subsidence, is primarily due to repeated nourishment interventions aimed at preventing coastal erosion, which helped the maintenance of an “artificial stability” along the coastline.

To evaluate the reliability of the generated shoreline products, a technical validation process was conducted. Given the complex interpretation of an annually averaged shoreline position, accuracy was assessed through visual interpretation of the processed shorelines and comparisons with the datasets available for the same period from topo-bathymetric monitoring. The time-averaging strategy in this study provides reliable averaged shoreline positions, minimizing the effects of short-term fluctuations and temporary runup excursions. This highlights the potential of satellite-optical imagery for coastal applications.