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 recen...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/8/2117 |
| _version_ | 1827743829087748096 |
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| author | Paola Souto-Ceccon Gonzalo Simarro Paolo Ciavola Andrea Taramelli Clara Armaroli |
| author_facet | Paola Souto-Ceccon Gonzalo Simarro Paolo Ciavola Andrea Taramelli Clara Armaroli |
| author_sort | Paola Souto-Ceccon |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:34:03Z |
| format | Article |
| id | doaj.art-755d826badad4c008d0499f93e52dfe0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:34:03Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-755d826badad4c008d0499f93e52dfe02023-11-17T21:12:17ZengMDPI AGRemote Sensing2072-42922023-04-01158211710.3390/rs15082117Shoreline Detection from PRISMA Hyperspectral Remotely-Sensed ImagesPaola Souto-Ceccon0Gonzalo Simarro1Paolo Ciavola2Andrea Taramelli3Clara Armaroli4Department of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, ItalyICM (CSIC), Passeig Marítim de la Barceloneta 37–49, 08003 Barcelona, SpainDepartment of Physics and Earth Sciences, University of Ferrara, 44122 Ferrara, ItalyScuola Universitaria Superiore (IUSS), 27100 Pavia, ItalyDepartment of Biological, Geological and Environmental Sciences (BIGeA), University of Bologna Alma Mater Studiorum, 40126 Bologna, ItalyCoastal 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.https://www.mdpi.com/2072-4292/15/8/2117Satellite Derived ShorelineshyperspectralPRISMA |
| spellingShingle | Paola Souto-Ceccon Gonzalo Simarro Paolo Ciavola Andrea Taramelli Clara Armaroli Shoreline Detection from PRISMA Hyperspectral Remotely-Sensed Images Remote Sensing Satellite Derived Shorelines hyperspectral PRISMA |
| title | Shoreline Detection from PRISMA Hyperspectral Remotely-Sensed Images |
| title_full | Shoreline Detection from PRISMA Hyperspectral Remotely-Sensed Images |
| title_fullStr | Shoreline Detection from PRISMA Hyperspectral Remotely-Sensed Images |
| title_full_unstemmed | Shoreline Detection from PRISMA Hyperspectral Remotely-Sensed Images |
| title_short | Shoreline Detection from PRISMA Hyperspectral Remotely-Sensed Images |
| title_sort | shoreline detection from prisma hyperspectral remotely sensed images |
| topic | Satellite Derived Shorelines hyperspectral PRISMA |
| url | https://www.mdpi.com/2072-4292/15/8/2117 |
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