ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
Radiometric correction: At present ATCOR 3 is the only commercially available software to sufficiently
optimise the radiometric correction of RS data under alpine conditions. It delivers radiance or alternatively
reflectance data which can directly be used for modelling purposes (regional and global climatic change
models, etc.). ATCOR3 is a parametrical correction software which combines topographic and
atmospheric normalisation. With respect to the Alpine wide application this correction seems to be
sufficient. For detailed investigation of specific vegetation parameters there is still a lack for in-flight
calibration coefficients (except for Landsat 7), aerosol and water vapour content as well as information
regarding visibility within the atmosphere during image acquisition, which could further improve the
correction results. With respect to small scale maps a topographic normalisation is not urgent, as
illumination differences are averaged in low resolution pixels.
Radiometric adjustment: When satellite scenes from different flight paths shall be classified together,
as is likely for the entire Alps, either the radiometric adjustment of these satellite scenes or a separate
classification of each flight path (including several satellite scenes) is necessary. The radiometric
adjustment can be performed by linear regression of aggregated image data based on statistical
analysis (Gallaun et al., 1999).
Data preparation: If an accurate update of settlement areas is required, it might be advisable to
calculate texture feature images and introduce them in the classification procedure. With respect to an
Alpine wide data base, for all other land cover parameters no additional image features are
recommended.
Classification: Common classification algorithms such as Maximum Likelihood, Thresholding, or
unsupervised classification (Isodata), are offered by all standard image processing systems. Therefore,
it is recommended to apply these classification methodologies also for the Alpine data base. It depends
very much on the data as well as the preference of the person dealing with this task, which
classification algorithm is preferred. However, with respect to the simplicity of the approach and the
necessary interaction, the Maximum Likelihood algorithm is most recommended. This is especially due
to the potential that different persons deal with the classification task, but the results will still be
comparable when they are based on the same training data. Once a classification methodology has
been fixed, the subsequent classifications for updating of the data base should follow the same
methodology to enable comparability of the results.
Verification: Verification of the satellite image classification results is essential to get a measure of
reliability of the data base. As measure for the overall classification accuracy the Kappa coefficient
proved to be the most suitable. However, as the accuracy of single classified categories may vary
much, it is recommended to additionally calculate the mean accuracy of each class and store it in the
meta data base. The guidelines for sampling were already described with the ground truthing.
Data implementation: In the Alpine Information System data from different sources shall be combined
for analyses. These data may comprise raster data (e.g. classification results), vector data (information
from regional information systems, such as traffic network), statistical information, a.s.o. Therefore, it is
recommended to build the Alpine Information System on a software which supports all these data
formats. In general, raster data also could be vectorised. With respect to pixel based satellite
classification results this presupposes aggregation of the classification results to larger units in a
sensible way. But common software for this procedure is not yet sufficient.
For further use of the data by various institutions it is essential to store the respective meta data in the
data base. At least those data should be stored, which are described in WP11 of the ALPMON project.
Furthermore, the question of data geometry has to be generally solved. For the Alpine Information
System the best solution would be to store the data in a common projection which is used European
wide, and additionally to provide the respective information for transformation of the data into the
national co-ordinate systems.
Hardware and software facilities: If the Alpine Convention envisages, to perform the classification
and monitoring of Alpine vegetation parameters themselves, at least the following hardware and
software equipment is necessary:
� work station with at least 36 gigabytes storage capacity
� standard image processing software
� geocoding software, which allows parametric, sensor-specific image rectification with block
adjustment
� a common GIS.
JR, RSDE, ALU, LMU, Seibersdorf, WSL 93