536
have been shown to add value when combined with optical remote sensing data
(Ramsey et al. 1998; Rosso et al. 2005; Niculescu et al. 2016). Change detection is
an important tool for wetland restoration monitoring because it provides measurements of incremental changes that can be used for inventory and benchmark purposes; knowledge of these changes can then be integrated with adaptive management
plans and used to target specific restoration goals (Tuxen et al. 2008).
The success of restoration, however, is difficult to assess. The degree of success
for many of these restoration sites is still being debated, especially since there is no
full agreement on criteria used to measure success. The creation, enhancement, or
restoration of coastal habitats requires much time and constant attention (Klemas
2013). Remote sensing offers accurate automated methods for detecting change in
restored wetlands. Vegetation change detection is a powerful indicator of restoration
success. The restoration projects use vegetative cover as an important indicator of
restoration success.
Synthetic aperture radar (SAR) technology provides the increased spatial resolution that is necessary in regional wetland mapping, and SAR data have been used
extensively for this objective (Bourgeau-Chavez et al. 2005; Lang and McCarty
2008; Novo et al. 2002). Radar has the capability of penetrating the plant cover
canopy and detecting submerged sectors and soil surface moisture. Although the
spatial resolution of radar images does not allow thorough and detailed habitat mapping, these images are useful for mapping wetland vegetation. The radar polarimetry and polarimetric parameters contribute significantly to the improvement of
vegetation identification based on polarization channels. Multipolarization and
multi-frequency radar devices are also used for the classification of wetland vegetation depending on their wavelengths, polarizations and backscattering mechanisms
and can be used to estimate the density and size of the vegetation. Microwave radiation polarization, like radar beam incidence angle and wave frequency, has long
been acknowledged as an important parameter for object recognition and understanding object features. Access to the scattering matrix permits several analytical
approaches and hence various ways of assessing the potential of multi-polarized
radar images. One approach consists of synthesizing pixel-based signal strength,
which should have been measured at the same frequency for any polarization configuration (linear and/or circular) (Niculescu et al. 2015a). The sensitivity of microwave energy to water and its ability to penetrate vegetative canopies makes SAR
ideal for the detection of the hydrologic features under vegetation.
SAR image time series such as those provided by the Sentinel-1 satellite allow
significant improvements in vegetation classification. The key advantage of satelliteborne SAR imaging is its independence of cloud cover, and because it is an active
sensing system, its independence of sun-induced reflection. Consequently, SAR
imagery has become an important tool for distinguishing different vegetation
classes. Recently, polarimetric SAR images have been analyzed using decomposition theorems such as alpha/entropy decomposition, which increases the accuracy
of vegetation analysis from microwave data. However, there is a wide choice of
remote-sensing satellites, radar, and optical. Whereas optical satellites usually operate in one imaging mode, radar satellites can be programmed to work indifferent
S. Niculescu et al.
have been shown to add value when combined with optical remote sensing data
(Ramsey et al. 1998; Rosso et al. 2005; Niculescu et al. 2016). Change detection is
an important tool for wetland restoration monitoring because it provides measurements of incremental changes that can be used for inventory and benchmark purposes; knowledge of these changes can then be integrated with adaptive management
plans and used to target specific restoration goals (Tuxen et al. 2008).
The success of restoration, however, is difficult to assess. The degree of success
for many of these restoration sites is still being debated, especially since there is no
full agreement on criteria used to measure success. The creation, enhancement, or
restoration of coastal habitats requires much time and constant attention (Klemas
2013). Remote sensing offers accurate automated methods for detecting change in
restored wetlands. Vegetation change detection is a powerful indicator of restoration
success. The restoration projects use vegetative cover as an important indicator of
restoration success.
Synthetic aperture radar (SAR) technology provides the increased spatial resolution that is necessary in regional wetland mapping, and SAR data have been used
extensively for this objective (Bourgeau-Chavez et al. 2005; Lang and McCarty
2008; Novo et al. 2002). Radar has the capability of penetrating the plant cover
canopy and detecting submerged sectors and soil surface moisture. Although the
spatial resolution of radar images does not allow thorough and detailed habitat mapping, these images are useful for mapping wetland vegetation. The radar polarimetry and polarimetric parameters contribute significantly to the improvement of
vegetation identification based on polarization channels. Multipolarization and
multi-frequency radar devices are also used for the classification of wetland vegetation depending on their wavelengths, polarizations and backscattering mechanisms
and can be used to estimate the density and size of the vegetation. Microwave radiation polarization, like radar beam incidence angle and wave frequency, has long
been acknowledged as an important parameter for object recognition and understanding object features. Access to the scattering matrix permits several analytical
approaches and hence various ways of assessing the potential of multi-polarized
radar images. One approach consists of synthesizing pixel-based signal strength,
which should have been measured at the same frequency for any polarization configuration (linear and/or circular) (Niculescu et al. 2015a). The sensitivity of microwave energy to water and its ability to penetrate vegetative canopies makes SAR
ideal for the detection of the hydrologic features under vegetation.
SAR image time series such as those provided by the Sentinel-1 satellite allow
significant improvements in vegetation classification. The key advantage of satelliteborne SAR imaging is its independence of cloud cover, and because it is an active
sensing system, its independence of sun-induced reflection. Consequently, SAR
imagery has become an important tool for distinguishing different vegetation
classes. Recently, polarimetric SAR images have been analyzed using decomposition theorems such as alpha/entropy decomposition, which increases the accuracy
of vegetation analysis from microwave data. However, there is a wide choice of
remote-sensing satellites, radar, and optical. Whereas optical satellites usually operate in one imaging mode, radar satellites can be programmed to work indifferent
S. Niculescu et al.