ng 
un 
ed 
se 
on 
  
re 
lle 
th 
ly 
je 
es 
Dy 
W 
nt 
1e 
pe 
ch 
d. 
ge 
pe 
to 
1e 
le 
  
60% is either not possible at all or only with an 
unsatisfying degree of precision. One of the reasons for 
this is that the potential differences in reflection caused 
by needle loss are superimposed by the reflection of the 
ground vegetation, which strongly influences the 
signature in open stands. However, it can be assumed 
that these stands show strong damages and thus belong 
predominantly to the category C3 (damaged trees more 
than 66%). It can be assumed that C3+ will often be find 
in the deforestation class 1 (solution of damage degree 
"improvement" after damaged trees are taken out) and 
C3- in the deforestation class 2. Thus a subdivision into 
different needle-loss categories is not necessary. 
Disadvantages of the damage class definition 
As mentioned above, it is not possible to isolate the 
defoliation symptoms from picture elements integrating 
defoliation as well as deforestation features. A precise 
separation of defoliation categories is only possible with 
the classification within one deforestation stage. 
Unfortunately, these both effects may superimpose each 
other and thus lead to a less satisfying classification 
result of the needle loss. 
The problem of recognising damages caused by air 
pollution among clearcuts, windthrow areas or other 
damages leading to a decreasing crown density cannot 
be solved if information on regular cutting operations and 
on disaster (storm, snow, ice, insect attacks etc.) is not 
available. 
A decreasing canopy density leads to an increasing 
influence of ground vegetation on the reflection of forest 
stands. The typical reflection characteristic of forest 
stands can be modified strongly by different types and 
phenological stages of ground vegetation. Detailed field 
information and knowledge about the reflection 
characteristic of the forest floor is necessary to estimate 
the influence of ground vegetation on the classification 
accuracy. 
7.4 Classification 
In the final phase of the project the damage-class 
definition had to be tested. It must be made sure that 
only those methods are applied which are also 
operational for small-scale classifications (total test area), 
since the classifications in the pilot studies were carried 
out for smaller investigation areas only. This is 
particularly important for the integration of ancillary 
information available for smaller test areas but not 
covering the entire region. 
It is not necessary to use the same classification 
algorithm for all the test areas as long as it is ensured 
that the classification of forest damages is carried out 
according to the damage classes mentioned above. 
739 
7.5 Verification 
The verification of the classification result must be 
directed towards the "Ground Truth" available for the test 
area (e.g. aerial photographs or field experiments). 
Methods therefore cannot be standardised. 
Irrespective of the method applied, it must be ensured 
that for all the verification areas in the entire test region 
the damage symptoms "needle loss categories in 
percentage" are recorded and "crown density" according 
to the percentage of canopy density. Only then there can 
be a guarantee that the classification of the damage 
class definition described above can be verified. One of 
the verification results is to be the analysis of the causes 
of mis-classifications so that the classification results can 
be analysed in detail by the user. 
8. RESULTS OF THE STUDIES IN THE TEST SITES 
The final image classification and the presentation of its 
results was performed in all test sites following the 
common methodology approved by the Advisory Board of 
the Large Area Experiment for Forest Damage 
Monitoring in Europe using Satellite Remote Sensing. 
This methodology was based on the suggestions of the 
TWG. 
The results of the forest damage classification were 
printed on coloured maps in scale of 1:100,000 (Polish 
Sudety Mountains, Krusne Hory, Harz) and 1:200.000 
(Fichtel- and Erzgebirge). The results of the particular 
projects are summarized below. Detailed information on 
the investigations in the test areas can be derived from 
Henzlik, 1995; Zawila-Niedzwiecki, 1994; Reuter & 
Akgóz, 1995 and Scharat et al, 1995: 
Necessity of ancillary information 
The investigations in pilot studies showed that the 
integration of a DEM and forest stand parameters 
derived from digital forest maps increased the 
classification accuracy only slightly. The digitisation of 
forest maps is very time consuming. Digitising large 
amounts of these data just for the purpose of optimising 
satellite classification is not advisable and thus not 
feasible for small scale classifications. The thamatic 
information of forest maps should only be integrated into 
the classification process if it is already available in digital 
format. 
However, the integration of additional information is 
necessary in order to seperate forest from non forest 
(open stands «2096 crown closure). This can be derived 
either by using the digital forest layer of topographic 
maps or by classifying a forest mask through multi- 
temporal classification of Landsat-data. 
Signature analysis 
All pilot studies stated that the influence of stand density 
on the spectral signature of Landsat TM data is high. The 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B4. Vienna 1996