Coastal dunes are found at the boundary between continents and seas representing unique transitional mosaics hosting highly dynamic habitats undergoing substantial seasonal changes. Here, we implemented a land cover classification approach specifically designed for coastal landscapes accounting for the within-year temporal variability of the main components of the coastal mosaic: vegetation, bare surfaces and water surfaces. Based on monthly Sentinel-2 satellite images of the year 2019, we used hierarchical clustering and a Random Forest model to produce an unsupervised land cover map of coastal dunes in a representative site of the Adriatic coast (central Italy). As classification variables, we used the within-year diversity computed through Rao's Q index, along with three spectral indices describing the main components of the coastal mosaic (i.e., Modified Soil-adjusted Vegetation Index 2-MSAVI2, Normalized Difference Water Index 2-NDWI2 and Brightness Index 2-BI2). We identified seven land cover classes with high levels of accuracy, highlighting different covariates as the most important in differentiating them. The proposed framework proved effective in mapping a highly seasonal and heterogeneous landscape such as that of coastal dunes, highlighting Rao's Q index as a sound base for natural cover monitoring and mapping. The applicability of the proposed framework on updated satellite images emphasizes the procedure as a reliable and replicable tool for coastal ecosystems monitoring.

Mapping Coastal Dune Landscape through Spectral Rao’s Q Temporal Diversity / Marzialetti, Flavio; Di Febbraro, Mirko; Malavasi, Marco; Giulio, Silvia; Acosta, Alicia Teresa Rosario; Carranza, Maria Laura. - In: REMOTE SENSING. - ISSN 2072-4292. - 12:14(2020). [10.3390/rs12142315]

Mapping Coastal Dune Landscape through Spectral Rao’s Q Temporal Diversity

Marzialetti, Flavio;Malavasi, Marco
;
2020-01-01

Abstract

Coastal dunes are found at the boundary between continents and seas representing unique transitional mosaics hosting highly dynamic habitats undergoing substantial seasonal changes. Here, we implemented a land cover classification approach specifically designed for coastal landscapes accounting for the within-year temporal variability of the main components of the coastal mosaic: vegetation, bare surfaces and water surfaces. Based on monthly Sentinel-2 satellite images of the year 2019, we used hierarchical clustering and a Random Forest model to produce an unsupervised land cover map of coastal dunes in a representative site of the Adriatic coast (central Italy). As classification variables, we used the within-year diversity computed through Rao's Q index, along with three spectral indices describing the main components of the coastal mosaic (i.e., Modified Soil-adjusted Vegetation Index 2-MSAVI2, Normalized Difference Water Index 2-NDWI2 and Brightness Index 2-BI2). We identified seven land cover classes with high levels of accuracy, highlighting different covariates as the most important in differentiating them. The proposed framework proved effective in mapping a highly seasonal and heterogeneous landscape such as that of coastal dunes, highlighting Rao's Q index as a sound base for natural cover monitoring and mapping. The applicability of the proposed framework on updated satellite images emphasizes the procedure as a reliable and replicable tool for coastal ecosystems monitoring.
2020
Mapping Coastal Dune Landscape through Spectral Rao’s Q Temporal Diversity / Marzialetti, Flavio; Di Febbraro, Mirko; Malavasi, Marco; Giulio, Silvia; Acosta, Alicia Teresa Rosario; Carranza, Maria Laura. - In: REMOTE SENSING. - ISSN 2072-4292. - 12:14(2020). [10.3390/rs12142315]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11388/335251
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