36
1: Richard lucas, Aled Rowlands, Olaf Niemann, Ray Merton
tration has been retrieved using AVIRIS, HyMap and Hyperion data (Johnson
1994; Matson et al. 1994; LaCapra et al. 1996; Martin and Aber 1997) and lignin,
among other biochemicals, has been quantified using AVIRIS data (Johnson
1994). Indices such as the Water Band Index (WBI) (Penuelas et al. 1997) and
the Normalised Difference Water Band Index (NDWBI) have also been used as
indicators ofleaf and canopy moisture content (Ustin et al. 2001).
Despite successes with the retrieval of foliar chemicals, the process is complicated by the distortion effects of the atmosphere, low SNR, complicated tree
canopy characteristics and the influence of the underlying soils and topography. Many understorey species, which exhibit wide ranging foliar chemical
diversity, also cannot be observed. Even so, continued efforts at retrieving algorithms for quantifying foliar biochemicals are advocated given the importance
of many (e. g., N and C) in global cycles.
Key biophysical attributes that can be retrieved using hyperspectral data
include measures of foliage and canopy cover (e. g., FPC or LAI) (Spanner et
al. 1990a; Spanner et al. 1990b; Gong et al. 1995), the fraction of absorbed
photosynthetically active radiation (fAPAR) and also measures of canopy architecture, including leaf angle distributions. Although woody attributes (e. g.,
branch and trunk biomass) cannot be sensed directly, these can often be inferred (Asner et al. 1999). Approaches to retrieving biophysical properties
from spectral reflectance measurements include the integration of canopy radiation models with leaf optical models (Otterman et al. 1987; Franklin and
Strahler 1988; Goel and Grier 1988; Ustin et al. 2001) or the use of empirical
relationships and vegetation indices (Treitz and Howarth 1999). Other specific techniques include derivative spectra, continuum removal, hierarchical
foreground/background analysis and SMA (Gamon and Qiu 1999), with most
associated with either physically-based canopy radiation models (Treitz and
Howarth 1999) or empirical spectral relationships (indices).
Key biophysical attributes that can be retrieved using hyperspectral data include measures of foliage and canopy cover (e. g., Foliage Projected Cover (FPC)
or Leaf Area Index (LAI)) (Spanner et al. 1990a; Spanner et al. 1990b; Gong et
al. 1995), the fraction of absorbed photosynthetically active radiation (fAPAR)
and also measures of canopy architecture, including leaf angle distributions.
Although woody attributes (e. g., branch and trunk biomass) cannot be sensed
directly, these can often be inferred (Asner et al. 1999). Approaches to retrieving biophysical properties from spectral reflectance measurements include the
integration of canopy radiation models with leaf optical models (Otterman et
al. 1987; Franklin and Strahler 1988; Goel and Grier 1988; Ustin et al. 2001) or
the use of empirical relationships and vegetation indices (Treitz and Howarth
1999). Other specific techniques include derivative spectra, continuum removal, hierarchical foreground/background analysis and SMA (Gamon and
Qiu 1999), with most associated with either physically-based canopy radiation
models (Treitz and Howarth 1999) or empirical spectral relationships (indices).
1: Richard lucas, Aled Rowlands, Olaf Niemann, Ray Merton
tration has been retrieved using AVIRIS, HyMap and Hyperion data (Johnson
1994; Matson et al. 1994; LaCapra et al. 1996; Martin and Aber 1997) and lignin,
among other biochemicals, has been quantified using AVIRIS data (Johnson
1994). Indices such as the Water Band Index (WBI) (Penuelas et al. 1997) and
the Normalised Difference Water Band Index (NDWBI) have also been used as
indicators ofleaf and canopy moisture content (Ustin et al. 2001).
Despite successes with the retrieval of foliar chemicals, the process is complicated by the distortion effects of the atmosphere, low SNR, complicated tree
canopy characteristics and the influence of the underlying soils and topography. Many understorey species, which exhibit wide ranging foliar chemical
diversity, also cannot be observed. Even so, continued efforts at retrieving algorithms for quantifying foliar biochemicals are advocated given the importance
of many (e. g., N and C) in global cycles.
Key biophysical attributes that can be retrieved using hyperspectral data
include measures of foliage and canopy cover (e. g., FPC or LAI) (Spanner et
al. 1990a; Spanner et al. 1990b; Gong et al. 1995), the fraction of absorbed
photosynthetically active radiation (fAPAR) and also measures of canopy architecture, including leaf angle distributions. Although woody attributes (e. g.,
branch and trunk biomass) cannot be sensed directly, these can often be inferred (Asner et al. 1999). Approaches to retrieving biophysical properties
from spectral reflectance measurements include the integration of canopy radiation models with leaf optical models (Otterman et al. 1987; Franklin and
Strahler 1988; Goel and Grier 1988; Ustin et al. 2001) or the use of empirical
relationships and vegetation indices (Treitz and Howarth 1999). Other specific techniques include derivative spectra, continuum removal, hierarchical
foreground/background analysis and SMA (Gamon and Qiu 1999), with most
associated with either physically-based canopy radiation models (Treitz and
Howarth 1999) or empirical spectral relationships (indices).
Key biophysical attributes that can be retrieved using hyperspectral data include measures of foliage and canopy cover (e. g., Foliage Projected Cover (FPC)
or Leaf Area Index (LAI)) (Spanner et al. 1990a; Spanner et al. 1990b; Gong et
al. 1995), the fraction of absorbed photosynthetically active radiation (fAPAR)
and also measures of canopy architecture, including leaf angle distributions.
Although woody attributes (e. g., branch and trunk biomass) cannot be sensed
directly, these can often be inferred (Asner et al. 1999). Approaches to retrieving biophysical properties from spectral reflectance measurements include the
integration of canopy radiation models with leaf optical models (Otterman et
al. 1987; Franklin and Strahler 1988; Goel and Grier 1988; Ustin et al. 2001) or
the use of empirical relationships and vegetation indices (Treitz and Howarth
1999). Other specific techniques include derivative spectra, continuum removal, hierarchical foreground/background analysis and SMA (Gamon and
Qiu 1999), with most associated with either physically-based canopy radiation
models (Treitz and Howarth 1999) or empirical spectral relationships (indices).