ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
Geometric displacements between the image and the DEM is another potential source of wrong
corrections. The model performs the correction according to the DEM. If the image is slightly shifted
the transition between illuminated and shadow areas will not any more correspond to the topography.
This leads to systematic errors on ridges and in valleys resulting in extreme over- or under-correction
of areas in the magnitude of the displacement.
The optimum result in topographic correction could be obtained if the entire area was covered by one
cover type, which is indicated by a specific Minnaert constant. Then it could be assumed that a
particular value of the Minnaert constant is valid for the entire area. In reality this scenario is rather
rare, in particular in the alpine area. In order to optimise the model a priori information on the
distribution of major cover types is helpful. This idea was considered in the implementation of the
model by separating vegetated and non-vegetated areas applying an NDVI. The more cover types are
separated the better the correction performs. On the other hand the recognition of these cover types is
the major motivation for performing the topographic correction. So it was useful to apply the correction
and subsequent classification in an iterative way.
Nevertheless, by applying different topographic normalisation procedures in the different test sites, the
satellite image quality could be significantly improved with respect to the subsequent classification of
the images.
2.2.4.2 Atmospheric Correction (WP7)
There has been continual discussion on the meaning and quality of divers atmospheric correction
algorithms within the project team. On one hand, atmospheric correction seems to be important for
accurate quantitative image interpretation. On the other hand, it has been stated that none of the
currently available atmospheric correction models is suitable for alpine areas. Several problems reduce
the applicability of atmospheric correction especially in alpine terrain:
� Methods not relying on local atmospheric measurements, are too inaccurate. For example, the
Point Spread method seems to be ineffective at all. Applied on the satellite data the procedure only
sharpened image features.
� Applying the method of Lavreau, quantitative analysis of the corrected scenes has to be handled
with care due to dramatic changes in the histograms of some spectral bands. A problem that may
occur by using the Tasseled Cap method is, that the simple linear combination of LANDSAT-TM
bands may not produce in every case the desired new band which is higher correlated to dust. It is
not clear to what extent the coefficients, which were derived from agricultural areas in Michigan in
June are valid for other areas and seasons. Also it should be kept in mind, that haze does not only
change digital numbers in a spectral band but also causes a loss of information.
� Other available atmospheric correction algorithms, like MODTRAN or LOWTRAN, implemented in
ATCOR, strongly rely on information about the atmospheric conditions at acquisition time of the
respective satellite image. This becomes a major disadvantage taking into account the poor
information usually available about the atmosphere. Having different measures of the horizontal
visibility within the research area at a time before or after satellite image acquisition makes
estimation of a mean value necessary. The result is not very accurate and may cause overcorrection
or under-correction. Over-correction, additionally, results in critical loss of information in
dark areas of the image.
Thus, the result of atmospheric correction is to be seen as very critical. Resulting changes appear to
be not due to real correction but to errors in the correction models. The shortcomings of the available
atmospheric correction algorithms are in our opinion according to the dramatic height differences in all
test sites. These are not considered properly in the correction models. In this direction efforts have to
be addressed to the scientific community dealing with atmospheric effects on satellite data especially in
alpine terrain.
Further discussion concerned the order of performing atmospheric correction before or after
topographic normalisation. In general, atmospheric correction is performed on original data, followed
by topographic normalisation. As atmospheric correction may cause information loss mainly within the
lower grey value ranges this becomes a problem for topographic normalisation. This is due to the fact
that diffuse illumination is important for atmospheric correction as well as for topographic
normalisation. Therefore, the opposite order was tested as well. Not being able to solve this problem
by performing the correction in two separate steps, an integrated approach for both correction tasks,
JR, RSDE, ALU, LMU, Seibersdorf, WSL 20