The research presented in this paper belongs to a wider research aimed to test innovative remote sensed techniques for the environmental and ecological characterization of emerged and submerged coastal areas. Here we focus on multisensory data fusion methods for the estimation of beach sediment parameters (mineralogy, grain size and moisture content) applied to a 22 km long sandy beach, in the Sabaudia-Latina physiographic unit (central Italy). The lithologic composition and grain size distribution of sediments are primary determinants of their inherent reflectance properties. Moreover, moisture content is also known to have a strong influence on reflectance of soils and sediments. If the effects of sediment composition, grain size and moisture content could be distinguished spectrally, it might be possible to map these properties at synoptic scales using hyperspectral, or perhaps even broadband, remote sensing in conjunction with few field sampling measures. In this study, we attempt to estimate the distribution of each of the above parameters through a multi linear regression model of airborne hyperspectral bands.

Multisensory data fusion methods for the estimation of beach sediment features: mineralogical, grain size and moisture

Taramelli A
2013

Abstract

The research presented in this paper belongs to a wider research aimed to test innovative remote sensed techniques for the environmental and ecological characterization of emerged and submerged coastal areas. Here we focus on multisensory data fusion methods for the estimation of beach sediment parameters (mineralogy, grain size and moisture content) applied to a 22 km long sandy beach, in the Sabaudia-Latina physiographic unit (central Italy). The lithologic composition and grain size distribution of sediments are primary determinants of their inherent reflectance properties. Moreover, moisture content is also known to have a strong influence on reflectance of soils and sediments. If the effects of sediment composition, grain size and moisture content could be distinguished spectrally, it might be possible to map these properties at synoptic scales using hyperspectral, or perhaps even broadband, remote sensing in conjunction with few field sampling measures. In this study, we attempt to estimate the distribution of each of the above parameters through a multi linear regression model of airborne hyperspectral bands.
MIVIS; Sediment; Multilinear regression; Beach
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12076/1734
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