Shoreline Detection from PRISMA Hyperspectral Remotely-Sensed Images

Coastal managers, policymakers, and scientists use shoreline accretion/erosion trends to determine the coastline's historical evolution and generate models capable of predicting future changes. Different solutions have been developed to obtain shoreline positions from Earth observation data in recent years, the so-called Satellite-Derived Shorelines (SDS). Most of the methodologies available in the literature use multispectral optical satellite imagery. This paper proposes two new methods for shoreline mapping at the subpixel level based on PRISMA hyperspectral imagery. The first one analyses the spectral signatures along defined beach profiles. The second method uses techniques more commonly applied to multispectral image analysis, such as Spectral Unmixing algorithms and Spatial Attraction Models. The results obtained with both methodologies are validated on three Mediterranean microtidal beaches located in two different countries, Italy and Greece, using image-based ground truth shorelines manually photointerpreted and digitised. The obtained errors are around 6 and 7 m for the first and second methods, respectively. These results are comparable to the errors obtained from multispectral data. The paper also discusses the capability of the two methods to identify two different shoreline proxies.

Automated summary

Coastal managers, policymakers, and scientists use shoreline accretion/erosion trends to understand the historical evolution and predict future changes of coastlines. This paper presents two new methods for subpixel-level shoreline mapping using PRISMA hyperspectral imagery. The first method analyzes spectral signatures along defined beach profiles, while the second method applies Spectral Unmixing algorithms and Spatial Attraction Models. The results, validated on Mediterranean microtidal beaches in Italy and Greece, show errors of approximately 6 and 7 m for the first and second methods, respectively, comparable to those obtained from multispectral data. The paper also discusses the capability of these methods to identify different shoreline proxies.