ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
for windows, a GIS system developed by RSDE, was applied. For the Engadine test site, additionally to
RSG the Satellite-Ortho Module from PCI was used.
Geocoding results in most cases were sufficient with sub-pixel accuracy due to parameter adjustment.
Additionally, the overlay of topographic information with geocoded satellite images could demonstrate
good correspondence between the data sets. However, results were not completely sufficient in the
Engadine test site, where significant deviations occurred in high altitudes, mainly in the high resolution
IRS pan image.
Restrictions in the geocoding process of high resolution digital satellite image data and the quality of its
results are mainly related to incorrect pre-processing of the digital image data and a DEM accuracy as
well as accuracy of the reference data not corresponding to the pixel resolution of the satellite image.
Implementation of a co-registration process of the geocoded images further improves the location
accuracy of the geocoded images.
3.3.4 Topographic normalisation
There is a general agreement within the consortium that topographic normalisation leads to an
improvement of the image data in mountainous terrain. In particular the Minnaert correction produces
useful results. The C-correction seems not to be the appropriate solution, but rather tends to extreme
over-correction of steep slopes. However, all corrections are based on empirical analysis thus being
highly depending on the actual image data.
A prerequisite to topographic normalisation is an accurate DEM and precisely geocoded image data.
For an optimum correction the spatial resolution of the DEM should be four times higher than the
spatial resolution of the image data. Using Landsat TM images the required cell size would be less
than 10m, for other image data such as SPOT or IRS-1C it would be even lower. In general there are
no such DEMs available for larger regions. The consequence of using lower resolution DEMs is the
lack of topographic details within one grid cell which might disturb the correction locally.
Geometric displacements between the image and the DEM is another potential source of erroneous
corrections. The model performs the correction according to the DEM. If the image is slightly shifted
the transition between illuminated and shadowed 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.
An ideal result for topographic correction could be obtained if the entire area was covered by one, and
only one cover type. 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. The more
cover types are separated the better the correction will be. On the other hand the recognition of these
cover types is the major motivation for performing the topographic correction. So it might be useful to
apply the correction and subsequent classification in an iterative way.
3.3.5 Atmospheric correction
There has been continual discussion on the meaning and quality of divers atmospheric correction
algorithms within the project team during the project meetings as well as regular communication. On
one hand, atmospheric correction seems to be a pre-requisite to 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.
� Methods not relying on local atmospheric measurements, are too inaccurate. For example, the
Point Spread method seems to be ineffective. Applied on the satellite data the procedure only
sharpened image features. A problem that may occur by using the Tasselled 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
JR, RSDE, ALU, LMU, Seibersdorf, WSL 76