Remote sensing of coastal ecosystems provides fundamental information for the effective assessment of valuable natural habitat. A synoptic view of the shallow submerged and emerged coastal landscape can offer the quantitative ability to obtain spatially explicit data over large and complex areas, where the heterogeneity of habitat is mixed by vegetation and sediment/soil interactions at the interface with water and atmosphere. In the present paper, by combining field radiometry with contemporary airborne hyperspectral imagery and topographic and bathymetric LiDAR data, an innovative approach with the application of Spectral Mixture Analysis (SMA) and Multiple Linear Regression models is proposed in order to define shallow coastal seabed, beach and dune habitat at the finest scale. The implemented FHyL (Field spectral libraries, airborne Hyperspectral imagery and LiDAR altimetry) processing chain leads to an innovative mapping results obtained by an integration of multisensory data. Presence and typology of vegetated and unvegetated beach is represented as the abundance of each physical response within the hyperspectral reflectance by building multisensory and multidimensional hyperspectral - LiDAR mixture space. Mineralogical and sedimentological proprieties of the beach sediment was estimated by using field and airborne spectral libraries combined with sediment sampling in a multiple linear regression statistical model. Therefore, FHyL represents the multisensory data fusion process to classify and map vegetation presence and distribution, as well as sediment properties and geomorphology of complex coastal seabed, and beaches dunes systems. The present research is a novel input for multilayered analysis in biophysical studies and its application on multi temporal dataset modeling of coast evolution. © 2013 IEEE.
FHYL: Field spectral libraries, airborne hyperspectral images and topographic and bathymetric LiDAR data for complex coastal mapping
Cappucci, S.;
2013-01-01
Abstract
Remote sensing of coastal ecosystems provides fundamental information for the effective assessment of valuable natural habitat. A synoptic view of the shallow submerged and emerged coastal landscape can offer the quantitative ability to obtain spatially explicit data over large and complex areas, where the heterogeneity of habitat is mixed by vegetation and sediment/soil interactions at the interface with water and atmosphere. In the present paper, by combining field radiometry with contemporary airborne hyperspectral imagery and topographic and bathymetric LiDAR data, an innovative approach with the application of Spectral Mixture Analysis (SMA) and Multiple Linear Regression models is proposed in order to define shallow coastal seabed, beach and dune habitat at the finest scale. The implemented FHyL (Field spectral libraries, airborne Hyperspectral imagery and LiDAR altimetry) processing chain leads to an innovative mapping results obtained by an integration of multisensory data. Presence and typology of vegetated and unvegetated beach is represented as the abundance of each physical response within the hyperspectral reflectance by building multisensory and multidimensional hyperspectral - LiDAR mixture space. Mineralogical and sedimentological proprieties of the beach sediment was estimated by using field and airborne spectral libraries combined with sediment sampling in a multiple linear regression statistical model. Therefore, FHyL represents the multisensory data fusion process to classify and map vegetation presence and distribution, as well as sediment properties and geomorphology of complex coastal seabed, and beaches dunes systems. The present research is a novel input for multilayered analysis in biophysical studies and its application on multi temporal dataset modeling of coast evolution. © 2013 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
