ibul 2004
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
5 @ Level3
(© Agriculture
@ Forest
© Urban
E-@ Level2
@ Forest!
Urbani
@ Intensively Used Grassland
5-0) Canola Crops
[Canola]
i + [Canola Variation]
= @ Low Vegetation Crops
@ [Soll]
[Stubble-Field]
[Potatoe]
= High Vegetation Crops
(0) Maize
QD sugar Beet
© Others
<O) Extensively Used Grassland
Figure 2. Class hierarchy showing the different levels of
classification and classes respectively. Classes in
brackets can not be reasonably seperated from each
other.
y
©
3.3 Spectral In-field Variability Assessment
The retrieval of biogeophysical and —chemical parameters
from hyperspectral data implies detectable gradients present
in the spectral data. Such variations bear the potential for in-
field parameter estimation from hyperspectral data.
In this study, the potential of assessing in-field variations
of green LAI (leaf area index) from HYPERION data in a
small-spaced heterogeneously vegetated area is
investigated, in addition to the classification efforts
described in Section 3.1. and Section 3.2. Spectral in-field
variation of a single field is quantified as percent deviation
of + | standard deviation from mean field reflectance. LAI is
an important biogeophysical parameter retrievable from
remote sensing data and serves as input into numerous
ecosystem models and crop growth models. LAI retrieval in
this study is based on a semi-empirical approach proposed
by [Clevers et al., 1994]. A corrected near-infrared
reflectance, known as Weighted Difference Vegetation Index
(WDVI), is calculated by subtracting the contribution of the
soil from the measured reflectance. The WDVI is then used
for estimation of LAI according to the inverse of an
exponential function, as given in Equation 1:
LAD *In1- WDVI
a Po (an) (1)
where LAI= Leaf Area Index, WDVI = Weighted Difference
Vegetation Index, a= complex combination of
extinction and scattering coefficients, and
Po (Anır) = asymptotically limiting value of the
WDVI at very high LAT values.
Standard values for a and Px (An) are taken from
literature [Bouman et al., 1992, Clevers et al., 1994].
4. RESULTS
4.1 Land Use Classification Results
Figure 3 indicates that due to the late date of HYPERION
data acquisition from a phenological point of view (August
18), either well established fields (maize, sugar beet,
grassland) on the one hand or strongly senescing cultivars
(canola, potatoes) and harvested (cereal stubble-fields) or
bare soil plots on the other hand can be found in the test
area. Under such conditions, the discrimination of different
land use types with comparable spectral signatures is a
challenging task.
Table 1 presents the class specific accuracies achieved with
the Spectral Angle Mapper approach. It can be seen that the
classes maize, intensively used grassland and stubble-fields
can be classified best.
Land Use User Producer Inclass
Type Accuracy Accuarcy Accuracy
Maize 0.7919 0.5198 0.8429
huenSively 0.5764 0.4854 0.5570
used grassland
Potatoe 0.2747 0.2315 0.1678
Canola 0.2143 0.1818 0.1224
Stubble-fields 0.7062 0.5170 0.7405
Extensively 0.1045 0.2692 0.0886
used grassland
Sugar beet 0.0562 0.3571 0.0538
Soil 0.1758 0.4103 0.1633
Canela 0.0526 0.3333 0.0500
variation
Overall 0.4396
Accuracy
Kappa 0.3377
Accuracy
Table 1. Spectral Angle Mapper accuracies determined on a
pixel-by-pixel basis for the main land use types
present in the Limpach Valley test area.
Forest or urban areas
Intensively used grassiand
Extensively used grassland
Maize
Canola
Canola variation
Sugar beet
Potatoe
Stubble fields
Soil
Figure 3. Land use classification result based on a Spectral
Angle Mapper (SAM) approach.
869
