94
To simulate vegetation such as grass or tree crowns, voxels can be filled by turbid
media parameterized with PAI and leaf optical properties (LOPs). Further details of
the DART model and examples of DART simulations can be found in GastelluEtchegorry et al. (2015). We use flux tracking in reflectance mode with the sun and the
atmosphere as the only radiation sources and used DART version 5.6.0 (v739). Optical
properties described in Sect. 4.4.1.1 and the forest reconstruction described in Sect.
4.4.1.2 are used to parameterize the forest canopy, background, and terrain in
DART. For details of model parameterization, see Schneider et al. (2014); for details
on the model-based upscaling of leaf-level traits, see Schneider et al. (2017). For the
modeling results shown in Sect. 4.5.1.1, we used sun and observation angles as in the
actual APEX and RapidEye acquisitions, respectively. We evaluate the performance
of the combined 3-D reconstruction and RT simulation approach in two ways: spectrally, by comparing averaged simulated spectra on the core (i.e., covered by TLS
measurements) site with those obtained by the APEX instrument, and, spatially, by
comparing simulated bands of RapidEye over an area of 900 m × 300 m with DARTsimulated reflectance at those particular wavelength regions.
4.4.3 Validation of Trait Predictions Using the RTM Approach
Three functional traits were derived from ALS data, canopy height (CH), PAI, and
foliage height diversity (FHD), forming a set of morphological traits. These three
were chosen because they are ecologically relevant and can be easily derived from
airborne laser scanning data. Three additional functional traits—chlorophylls
(CHL), carotenoids (CAR), and equivalent water thickness (EWT)—were chosen
and computed using specific band ratios from the IS data (Schneider et al. 2017),
forming a set of physiological traits. Both CH and CHL have been identified as
primary observables for RS-EBVs, so their validation and scaling is particularly
relevant. The traits were computed at a spatial aggregation unit of 6 m; for the ALS
data, all echo values within a 6 m × 6 m grid cell were used for the computation,
while for the IS data only sunlit pixels within the grid cell were retained for subsequent index computation. The shadow mask used for extracting sunlit pixels was
derived from a DSM based on the ALS data and the solar illumination angle at the
time of the IS overflight. For more details on the selected traits and their computation, please refer to Schneider et al. (2017).
The physiological traits used in this study are by definition leaf-level parameters,
which need to be upscaled or averaged to be representative for the tree or canopy
level. On the other hand, the morphological traits can be directly estimated from
ALS data for any spatial unit. However, the chosen spatial scale and context might
change how the data are interpreted (e.g., tree height needs to be estimated using
single-tree information, whereas vegetation height can be derived at all different
scales at the stand or plot level. See Fig. 4.7 for a map of the computed physiological
and morphological traits.
F. Morsdorf et al.
To simulate vegetation such as grass or tree crowns, voxels can be filled by turbid
media parameterized with PAI and leaf optical properties (LOPs). Further details of
the DART model and examples of DART simulations can be found in GastelluEtchegorry et al. (2015). We use flux tracking in reflectance mode with the sun and the
atmosphere as the only radiation sources and used DART version 5.6.0 (v739). Optical
properties described in Sect. 4.4.1.1 and the forest reconstruction described in Sect.
4.4.1.2 are used to parameterize the forest canopy, background, and terrain in
DART. For details of model parameterization, see Schneider et al. (2014); for details
on the model-based upscaling of leaf-level traits, see Schneider et al. (2017). For the
modeling results shown in Sect. 4.5.1.1, we used sun and observation angles as in the
actual APEX and RapidEye acquisitions, respectively. We evaluate the performance
of the combined 3-D reconstruction and RT simulation approach in two ways: spectrally, by comparing averaged simulated spectra on the core (i.e., covered by TLS
measurements) site with those obtained by the APEX instrument, and, spatially, by
comparing simulated bands of RapidEye over an area of 900 m × 300 m with DARTsimulated reflectance at those particular wavelength regions.
4.4.3 Validation of Trait Predictions Using the RTM Approach
Three functional traits were derived from ALS data, canopy height (CH), PAI, and
foliage height diversity (FHD), forming a set of morphological traits. These three
were chosen because they are ecologically relevant and can be easily derived from
airborne laser scanning data. Three additional functional traits—chlorophylls
(CHL), carotenoids (CAR), and equivalent water thickness (EWT)—were chosen
and computed using specific band ratios from the IS data (Schneider et al. 2017),
forming a set of physiological traits. Both CH and CHL have been identified as
primary observables for RS-EBVs, so their validation and scaling is particularly
relevant. The traits were computed at a spatial aggregation unit of 6 m; for the ALS
data, all echo values within a 6 m × 6 m grid cell were used for the computation,
while for the IS data only sunlit pixels within the grid cell were retained for subsequent index computation. The shadow mask used for extracting sunlit pixels was
derived from a DSM based on the ALS data and the solar illumination angle at the
time of the IS overflight. For more details on the selected traits and their computation, please refer to Schneider et al. (2017).
The physiological traits used in this study are by definition leaf-level parameters,
which need to be upscaled or averaged to be representative for the tree or canopy
level. On the other hand, the morphological traits can be directly estimated from
ALS data for any spatial unit. However, the chosen spatial scale and context might
change how the data are interpreted (e.g., tree height needs to be estimated using
single-tree information, whereas vegetation height can be derived at all different
scales at the stand or plot level. See Fig. 4.7 for a map of the computed physiological
and morphological traits.
F. Morsdorf et al.