ıbul 2004
jrassland
grassland
variation
>-fields & Soil
n object-
nition.
[S
N and the
mean for
present in
al in-field
pe of +1
shown in
use types
The mean
| given as
deviation
lines.
sugar beet
he WDVI.
and red
literature
senescing
ot exceed
Figure 6).
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
Figure 6. HYPERION spectral in-field variation of the main
land use types present in the Limpach Valley test
area, represented as percent deviation of + 1
standard deviation from mean reflectance.
Land Use LAT min LAI mean LALmax
Type
Sugar beet 2.92 3.36 3.90
Senescing 0.03 0.09 0.15
potatoes
Table 3. In-field variation of green LAI determined from
HYPERION data for sugar beet and senescing
potatoes. The variation of LAI is calculated based
on the spectral variation of the WDVI.
It can be concluded, that in-field variations of green LAI can
be retrieved from HYPERION spectral data, with mean values
of LAI representing the lush green status of sugar beet and
the almost completely senesced phenological stage of the
potatoes cultivars.
S. CONCLUSIONS
Two approaches were applied in this work for hyperspectral
image classification from HYPERION data: SAM and an
object-oriented analysis. Both methods greatly suffer from
low spectral variations among the different agricultural land
cover types due to the late phenological stage of the
cultivars at the time of data take.
With the exception of the classes maize, stubble-fields and
intensively used grassland, the majority of agricultural
fields could not be classified successfully by applying a
Spectral Angle Mapper algorithm. As a consequence of the
late phenological stages, the spectral behaviour of various
land cover types is very similar. In addition, the small-
spaced pattern of many fields in the area produces numerous
mixed pixels at HYPERIONS's ground resolution of 30 m,
which further decreases the classification accuracy. However,
the spectral in-field variation of single fields observable by
HYPERION bears the potential of retrieving biogeophysical
and —chemical variations within fields, as could be
demonstrated in the case of LAI.
Classification results for the object-oriented classification
method are disapointing due to several reasons: (a) low
Spectral variation'at dataset-specific acquisition time; most
crops are either already harvested or senesced, (b)
classification rules for agricultural crop classes for this
Study are mainly based on spectral features and do not imply
. 871
relational, textural or shape features, (c) crop fields in
Switzerland are small in size and textural differences in
HYPERION datasets are low due to the sensor's geometric
resolution and (d) relations between crop types are
inexistent and cultivation changes every season.
eCognition's advances of implying relational, textural or
shape features in its hierarchical classification process can
unfortunately not be applied to this land use classification.
Agricultural land use classification from HYPERION data is
expected to yield better results if the dataset was acquired at
the most promising time of year from a spectral point of
view, i.e., during the growing season of most agricultural
crops in June. Vitality-related crop type specific
charcteristics have their largest impact on spectral behaviour
at this time of year. As can be seen from the results in Figure
3 and Figure 4, the small-spaced structure of Swiss
agricultural fields can partionally be met by HYPERION's
ground resolution, although mixed pixels remain a common
problem. However, the potential of an object-oriented
approach based on relational, textural and shape features can
not be fully exploited in agricultural applications due to
HYPERION's coarse spatial resolution for such techniques.
6. REFERENCES
Baaz, M. et al, 2003. eCognition User Guide V. 3.0.
http://www definiens-imaging.com/ (accessed 24 Apr. 2003).
Boardman, J.W., and Kruse, F.A., 1994. Automated Spectral
Analysis: A Geological Example Using AVIRIS Data, North
Grapevine Mountains, Nevada. Proc 10" Thematic
Conference on Geological Remote Sensing, Environmental
Research Institute of Michigan, San Antonio (TX), pp.407-
418.
Bouman, B.A.M., van Kasteren, H.W.J., and Uenk, D., 1992.
Standard Relations to Estimate Ground Cover and LAI of
Agricultural Crops from Reflectance Measurements. Europ.
J. Agronomy, 4, pp.249-262.
Clevers, J.G.P.W., Büker, C., van Leeuwen, H.J.C., and
Bouman, B.A.M., 1994. A Framework for Monitoring Crop
Growth by Combining Directional and Spectral Remote
Sensing Information. Remote Sens. Environ., 50, pp.161-170.
Datt, B., McVicar, T.R., Van Niel, T.G., Jupp, D.L.B., and
Pearlman, J.S, 2003. Preprocessing EO-1 Hyperion
Hyperspectral Data to Support the Application of
Agricultural Indexes. /EEE Trans. Geosci. Remote Sensing,
41(2), pp.1246-1259.
Goodenough, D.G., Dyk, A., Niemann, O., Pearlman, J.S.,
Chen, H., Han, T., Murdoch, M., and West, C., 2003.
Processing HYPERION and ALI for Forest Classification.
IEEE Trans. Geosci. Remote Sensing, 41(2), pp.1321-1331.
Kneubühler, M., 2002. Spectral Assessment of Crop
Phenology Based on Spring Wheat and Winter Barley.
Remote Sensing Series 38, University of Zürich, Switzerland,
pp.149.
Richter, R., 2003. Atmospheric / Topographic Correction for
Airborne Imagery. ATCOR-4 User Guide, Version 3.0, DLR-
IB 564-02/03, DLR, Wessling, Germany, pp.66.
Story, M., and Congalton, R., 1986. Accuracy Assessment: A
User's Perspective. PE&RS, 52(3), pp.397-399.
Toutin, T., 1985. Analyse mathématique des possibilités
cartographiques du systéme de Spot. Ph.D. Thesis, ENSG.
