The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
1995), in our study wavelengths within the red edge region
were almost absent.
The PLSR model appears to be a powerful alternative to
univariate statistical methods (Darvishzadeh et al., 2008).
Compared to the other investigated methods, it achieved
relatively better results. It seems that important information will
be lost by selecting only two wavelengths for narrow band
vegetation indices.
LAI
CCC
Figure 3. Cross-validated prediction of grass variables in
Majella National Park, Italy, using the entire reflectance spectra
in partial least squares regression models. Left: estimated LAI
versus measured LAI; right: for canopy chlorophyll content.
Estimation of biochemical and biophysical characteristics of
heterogonous grassland with mixtures of different grass species
is challenging in remote sensing (Roder et al., 2007), as the
measured signal correspond to different grass species. In our
study, an indicator of this was the observed high variations in
the SPAD readings within a given subplot (not shown).
Nevertheless, by using hyperspectral remote sensing with a
large number of narrow spectral bands and powerful
multivariate regression techniques, the biophysical grass
characteristics could be retrieved with acceptable accuracy.
5. CONCLUSION
The most important conclusions that can be drawn from this
study are as follows:
- Compared with LAI, canopy chlorophyll content was
estimated with higher accuracy in all models.
- LAI was best estimated by partial least square regression
which utilize more than two wavelengths from the entire
spectral region (400 nm to 2500 nm) to estimate the
variable of interest.
- SAVI2 is a potentially useful vegetation index for
extracting canopy variables such as LAI. However, the
selection of appropriate wavelengths and bandwidths is
important.
Partial least squares regression provided the most useful
explorative tool for unraveling the relationship between canopy
spectral reflectance and grass characteristics at canopy scale.
In summary, multivariate calibration methods, which until now
have only been used in a few cases concerning the remote
sensing of grasslands, can enhance estimates of different grass
variables, and thus present new prospects for mapping and
monitoring heterogeneous grass canopies from air- and space-
borne platforms.
ACKNOWLEDGEMENTS
We would like to acknowledge the assistance of the park
management of Majella National Park, Italy, and in particular
of Dr. Teodoro Andrisano. We extend our gratitude to Dr. Istiak
Sobhan for his assistance during the field campaign. Special
thanks go to Dr. Michal Daszykowski for his assistance in
applying the TOMCAT toolbox and for his valuable comments.
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