50 
play an important role for the 
classification. 
For the results under discussion the 
UN-ECE forest damage classification 
scheme was used as an European 
standard, though structural elements 
are not yet considered in it. 
yellowing 
leaves,needles 
loss 
11-25% 
26-60% 
61-100% 
< 10 % 
0 
1 
2 
11-25 % 
1 
2 
2 
26-60 % 
2 
3 
3 
> 60 % 
3 
3 
3 
lead tree 
4 
Table 1 UN-ECE forest damage 
classification scheme for trees. 
Class definition 0: healthy, 1: low 
damage, 2: medium damage, 3: heavy 
damage, 4: dead 
The principal differences in the 
evaluation of visual features for 
damage classification: leaves or 
needles loss, discolouration on one 
side, structural elements in addition 
in the other side indicate a general 
problem in photo interpretation. 
1 Problem definition 
The analysis of multispectral scanner 
data which was the main goal of the 
BMFT project, it is distinct from the 
photo interpretation for the follo 
wing reasons: 
The spatial resolution of scanners is 
defined by the instantenous field of 
view (IFOV) or the image element. Its 
size is determined by the sensor's 
field of view (FOV) and the flight 
altitude. 
For the image element the integral 
spectral radiance is recorded. The 
integral reflected spectral radiance 
may be produced from forest cover of 
one species, generating so-called 
"pure pixels", or surface elements, 
which contain also portions of other 
plant communities, road network, 
gravel etc. , resulting in so-called 
"mixed pixels", with their conse 
quences on spectral analysis. A 
comparison of CIR-film and multi 
spectral data performance for forest 
damage evaluation is made in table 2. 
Multispectral scanner data cover a 
wider spectral range than CIR-photos. 
For interpretation purposes the 
spectrum of vegetation can be devided 
into three rather distinct regions 
Johnson, 1969; Sinclair et. al., 
1971; Walter et al., 1981; Rock et 
al.; 1986. In the visible region 
(400-700 nm) the main part of the 
radiation energy is absorbed by plant 
pigments of the upper leave or needle 
layer. Reflection and transmission is 
rather low. In the near infrared 
(NIR, 700-1300 nm) the reflection of 
green vegetation is high, depending 
on the species and number of layers. 
Reflectance is influenced by cell 
structure background and water 
content. In the short wave infrared 
(SWIR, 1300-4000 nm) reflectance can 
be attributed to water content, cell 
structure and background. Leaf area 
index in relation to canopy 
reflectance is more important in the 
infrared than in the visible. 
Spectral reflectance depends on plant 
species, age, form and orientation of 
leaves or needles, branches, stem, 
background and health status, Koch, 
1987; Hermann et al., 1988, Kirchhof 
et al. 1988, Hoffmann et al. 1989. 
On the ground are soil cover, surface 
roughness, relief, mineral supply, 
humidity and heavy metal content main 
parameters, which influence the 
optical behaviour, Collins, 1983; 
Kronberg, 1985, The saisonal 
variation of spectral signature of 
plants is described by Hildebrandt, 
1976, and Tanner et al., 1981. 
2 Objective of spectral measurements 
In support of the cooperative 
research project of the BMFT 
additional spectroradiometer 
measurements became necessary. 
Spectral analyses of forest stands in 
the test site Stadtwald Frankfurt 
revealed the importance of primary 
and/or secondary effects of damage on 
the change of spectral signatures, 
Guttmann et al., 1987. More detailed 
information could not be derived from 
multispectral scanner data of pixels 
sizes 5 x 5 m 2 to 10 x 10 m 2 . 
As primary effects of damage were 
identified 
- change of the spectral signature of 
tree components (branches, leaves, 
needles, barks, lichens) 
- discolouration of leaves, needles 
- loss of biomass, leaves, needles 
- orientation of branches, leaves, 
needles, roll of leaves 
- change of crown structure and 
texture, anomalous ramification 
The course of the spectral signature 
will be changed by above mentioned 
primary effects. Loss of biomass, 
change of crown structure and 
anomalous branching produce an 
increase of optical transparency, of 
shadow portions and as a consequence 
an augmentation of background 
radiation as secondary effects. 
For the improvement of our understan 
ding of spectral signature changes by 
tree damage, a measurement program of 
tree components was developed. The 
central theme is the understanding of 
reflectance behaviour of tree com 
ponents in different compositions 
(layers), and its application for the 
selection of spectral bands and image 
processing algorithms for multispec 
tral classification to optimize da 
mage identification and separation,