Souto-Ceccon et al. 2023

Shoreline Detection from PRISMA Hyperspectral Remotely-Sensed Images.

Souto-Ceccon, P.1; Simarro, G.2; Ciavola, P.1; Taramelli, A.3; Armaroli, C.4

  1. Department of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, Italy
  2. ICM (CSIC), Passeig Marítim de la Barceloneta 37–49, 08003 Barcelona, Spain
  3. Scuola Universitaria Superiore (IUSS), 27100 Pavia, Italy
  4. Department of Biological, Geological and Environmental Sciences (BIGeA), University of Bologna Alma Mater Studiorum, 40126 Bologna, Italy

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.

Keywords: Satellite Derived ShorelineshyperspectralPRISMA

How to Cite: Souto-Ceccon, P.; Simarro, G.; Ciavola, P.; Taramelli, A.; Armaroli, C. Shoreline Detection from PRISMA Hyperspectral Remotely-Sensed Images. Remote Sens. 2023, 15, 2117.