Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor Acquisition
Coastal sand dunes are highly dynamic aeolian landforms where different spatial patterns can be observed due to the complex interactions and relationships between landforms and land cover. Sediment distribution related to vegetation types is explored here on a single ridge dune system by using an ai...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/8/1229 |
| _version_ | 1797570865280319488 |
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| author | Emiliana Valentini Andrea Taramelli Sergio Cappucci Federico Filipponi Alessandra Nguyen Xuan |
| author_facet | Emiliana Valentini Andrea Taramelli Sergio Cappucci Federico Filipponi Alessandra Nguyen Xuan |
| author_sort | Emiliana Valentini |
| collection | DOAJ |
| description | Coastal sand dunes are highly dynamic aeolian landforms where different spatial patterns can be observed due to the complex interactions and relationships between landforms and land cover. Sediment distribution related to vegetation types is explored here on a single ridge dune system by using an airborne hyperspectral and light detection and ranging (LiDAR) remote sensing dataset. A correlation model is applied to describe the continuum of dune cover typologies, determine the class metrics from landscape ecology and the morphology parameters, and extract the relationship intensity among them. As a main result, the mixture of different vegetation types such as herbaceous, shrubs, and trees classes shows to be a key element for the sediment distribution pattern and a proxy for dune sediment retention capacity, and the anthropic fingerprints can play an even major role influencing both ecological and morphological features. The novelty of the approach is mostly based on the synergistic use of LiDAR with hyperspectral that allowed (i) the benefit from already existing processing methods to simplify the way to obtain thematic maps and coastal metrics and (ii) an improved detection of natural and anthropic landscape. |
| first_indexed | 2024-03-10T20:31:30Z |
| format | Article |
| id | doaj.art-f0931080bea242e0a2629ec6e939c154 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:31:30Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f0931080bea242e0a2629ec6e939c1542023-11-19T21:24:53ZengMDPI AGRemote Sensing2072-42922020-04-01128122910.3390/rs12081229Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor AcquisitionEmiliana Valentini0Andrea Taramelli1Sergio Cappucci2Federico Filipponi3Alessandra Nguyen Xuan4Institute for Environmental Protection and Research (ISPRA), 00144 Rome, ItalyInstitute for Environmental Protection and Research (ISPRA), 00144 Rome, ItalyNational Agency for New Technologies Energy and Sustainable Development (ENEA), 00123 Rome, ItalyInstitute for Environmental Protection and Research (ISPRA), 00144 Rome, ItalyInstitute for Environmental Protection and Research (ISPRA), 00144 Rome, ItalyCoastal sand dunes are highly dynamic aeolian landforms where different spatial patterns can be observed due to the complex interactions and relationships between landforms and land cover. Sediment distribution related to vegetation types is explored here on a single ridge dune system by using an airborne hyperspectral and light detection and ranging (LiDAR) remote sensing dataset. A correlation model is applied to describe the continuum of dune cover typologies, determine the class metrics from landscape ecology and the morphology parameters, and extract the relationship intensity among them. As a main result, the mixture of different vegetation types such as herbaceous, shrubs, and trees classes shows to be a key element for the sediment distribution pattern and a proxy for dune sediment retention capacity, and the anthropic fingerprints can play an even major role influencing both ecological and morphological features. The novelty of the approach is mostly based on the synergistic use of LiDAR with hyperspectral that allowed (i) the benefit from already existing processing methods to simplify the way to obtain thematic maps and coastal metrics and (ii) an improved detection of natural and anthropic landscape.https://www.mdpi.com/2072-4292/12/8/1229beach–dune systemsediment retentioncoastal sand and vegetation patternsspectral librariesairborne hyperspectralLiDAR |
| spellingShingle | Emiliana Valentini Andrea Taramelli Sergio Cappucci Federico Filipponi Alessandra Nguyen Xuan Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor Acquisition Remote Sensing beach–dune system sediment retention coastal sand and vegetation patterns spectral libraries airborne hyperspectral LiDAR |
| title | Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor Acquisition |
| title_full | Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor Acquisition |
| title_fullStr | Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor Acquisition |
| title_full_unstemmed | Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor Acquisition |
| title_short | Exploring the Dunes: The Correlations between Vegetation Cover Pattern and Morphology for Sediment Retention Assessment Using Airborne Multisensor Acquisition |
| title_sort | exploring the dunes the correlations between vegetation cover pattern and morphology for sediment retention assessment using airborne multisensor acquisition |
| topic | beach–dune system sediment retention coastal sand and vegetation patterns spectral libraries airborne hyperspectral LiDAR |
| url | https://www.mdpi.com/2072-4292/12/8/1229 |
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