M. Schardt, H. Kenneweg and H. Sagischewski
                        Technical University of Berlin


   The aim of this project is to investigate the applicability of Thematic
Mapper data and existing classification methods for forest damage
classification of Norway spruce (Picea abies).  The test site Harz - one of
the most severely damaged parts in Germany - is located between Hannover,
Magdeburg and Goettingen.  Pure stands of Norway spruce are the most frequent
type of silviculture in the Harz Mountains.  Today these stands show a variety
of forest decline caused by the so-called "Neuartigen Waldschaden" ranking
from alomost no injuries in the lower regions to most severe damages like
deforestation symptoms in the higher regions.  The strongly affected stands
are additionally damaged by storm and beetle outbreaks (Ips typographus).

   From the results of the signature analysis and of former classifications it
can be deduced that a distinctive as well as an extensive mapping of forest
damage requires the integration of auxiliary information, and for this purpose
a GIS is applied including a digital terrain model, forest planning data and
soil maps.

                               1. INTRODUCTION

   The so-called "Neuartige Waldschaden" first occurred in the early eighties.
In contrast to forest damages observed until then (mostly caused by storm and
insects), which were very limited in space and time and whose symptoms were
well-known, the "Neuartige Waldschaden" occur on a large scale, mainly in the
mountainous regions.  This forest disease is characterized by discoloration
and needle loss and in extreme cases, it leads to the deforestation of
relatively large forest areas.  These damaged stands are then also more
susceptible both to storm damage and to insect outbreaks mostly caused by bark

   At first, forest damage inventories were carried out by field sampling
methods, later on with color infrared photos (BML 1984 and Hildebrandt 1984).
The disadvantage of the former method are that forest decline symptoms due to
dead trees are not taken into account and thus do not appear in the damage
statistics.  This results in a systematic underestimation of forest damages.
In contrast to sampling methods, satellite data allow a complete pixel-by-
pixel covering assessment on the state of the vegetation integrating also
missing trees.  For the applicability of these methods it is necessary to
define damage classes which correspond both to the requirements of satellite
remote sensing and existing field methods of forest damage estimation.

   The start of the Landsat Thematic Mapper System in 1984 with its improved
spatial (30x30 m) and spectral resolution (7 bands: blue, green, red, near
infrared, two middle infrared and thermal) offered new possibilities for
inventory methods based on Thematic Mapper data.  Forster (1989) was one of
the first to attempt a classification of extent and intensity of damages in a
small investigation area of the Harz Mountains by means of satellite remote
sensing.  He was able to show that in homogeneous stands forest damages can be
classified with an accuracy of up to nearly 70%.  Taking Forster's work as a
basis, the aim of the joint project between the TU Berlin and the TU Dresden
now is to determine the extent of the forest damages of the entire Harz
Mountains by means of TM-data for the year 1991, and to record the dynamics
of forest damages by means of MSS- and TM-data from 1972 to 1991.  In this
investigation both the separability of needle loss and deforestation symptoms
will be taken into account.

   The following report deals with the results of the project group of the TU
Berlin, whose investigations have been concentrating on the Lower-Saxony part
of the Harz Mountains.  As support for the satellite classification, a Forest
Information System (FIS) was established.  The FIS consists of a digital
elevation model (DEM) as well as digital forest stand maps and digital soil
maps.  By merging and overlaying this additional information with the
satellite data it is possible to include potentially negative factors such as
tree age, changing soil characteristics and illumination conditions in the
classification process.

   Extensive data processing by means of an FIS merely for better
classification results would not make proper use of the actual potential of
such an FIS and would therefore not justify the amount of work put into it.
Thus special attention was given to the possibility of using these data for
planning tasks, too.

   Because of the poor health state of the forest, the forest administration
of Lower Saxony plans to convert the widespread spruce monocultures into more
stable deciduous and coniferous mixed forest stands or pure deciduous stands
(Niedersachsische Landesforsten, 1992).  If put into practice, these plans
should be accompanied by investigations into how they could be supported by
the FIS established during this project.  The aim is to show how far satellite
classification as a component of the FIS is able to give information on the
health state of individual stands, i.e. the urgency of stand conversions;
however, the actual spectrum of the statements on further planning which
could be supported by such data is much greater.

   The TU-project described here is part of the European Joint Project for
forest damage assessment initiated by the ECE/FAO and UNEP-GRID and entitled
"Large Area Operational Experiment for Forest Damage Monitoring Using
Satellite Remote Sensing" (Kenneweg et al., 1993).  The aim of the project is
the development of an integrated classification system and the definiton of
integrated and comparable damage classes.  Test areas are in Poland, the
Slovak Republic, the Czech Republic and in Germany.  The investigations are
carried out in cooperation with research groups from Finland, France and the


   The test site of the western Harz is located between Hannover, Magdeburg
and Goettingen.  The main tree species of the test site are spruce (Picea
abies) occurring in the higher regions of the Harz and beech (Fagus sylvatica)
growing in the lower parts.  Pure spruce stands are the most frequent result
of silviculture in the former mining area of the Harz.  Today these stands
show a variety of forest decline symptoms ranking from almost no injuries to
most severe damages.  The size of the forested area is about 80,000 ha.  The
location of the test site Harz is shown in  Figure 1.


Satellite Data

   Thematic Mapper and MSS data of different years (1972-1992) are used for
the forest damage inventory and monitoring.  By examining MSS data, the
reduction of crown density caused by the forest disease will be detected.  TM
data from 1984-1992 are used to classify changes of needle loss.

   The satellite images were geometrically corrected into the Gauss-Kruger
coordinates using the pass point method.  The RMS-error of the geometrical
correction amounts to 18-26 meters.

Forestry Information System (FIS)

   From the result of the signature analysis and former classifications it can
be deduced that a distinctive mapping of forest disease needs the integration
of auxiliary information.  Therefore, a Forestry Information System (FIS) was
established.  The components of the FIS will subsequently be described.

A. Digital terrain model (DTM)

   A digital terrain model is available to eliminate the negative influence of
topographical parameters (Schardt, 1987).  The topographic parameters, slope,
aspect and illumination are calculated from the elevation values provided by
the digital terrain model.

   The digital terrain model is geometrically registered and superimposed on
the satellite data.  The spatial differences between the DTM and the satellite
data amount to about 15 meters due to the y- and x-axis and less than 5 meters
due to the z-axis (height above sea level) which is quite satisfactory for
classification purposes on a scale of less than 1:50,000.

B. Forest management plans

   In order to integrate stand parameters such as tree age, tree species and
stand structure, the forest management plans (70 map sheets consisting of over
15,000 planning units) of the complete Harz Mountains were digitized using the
Geographical Information System ARC/Info.  Because of the insufficient
geometrical accuracy of the forest survey (gaps and overlapping of neighboring
map sheets of up to 100 meters) each of the maps had to be geometrically
corrected in ARC/Info using a rubber-sheet algorithm and pass points derived
from topographical maps.  After the geometrical correction, the digitized map
sheets are merged together into one forest map containing the complete forest
area of the Harz Mountains.

   As attribute data the digital stand description of the forestry
administration could be used.  The attribute data containing about 100
different stand parameters are prepared according to the requirements of
satellite classifications and computer-aided forest management planning.
By combining the attribute data and digitized forest maps, the planning units
can be stratified according to single parameters or different combinations of
the stand parameters.  The result of stratification can be, for example, maps
demonstrating the distribution of different age classes, tree species, tree
heights or diameter classes.

C. Soil maps

   Digital soil maps are a central component of the Forestry Information
System.  Together with the digital terrain model, forest management maps, and
the forest damage classification these maps will be used for feasibility
studies of the Forestry Information System according to forestry management
planning.  The soil maps were digitized by the Institute of Soil Science of
Niedersachsen.  The results of this investigation will not be discussed in
this paper.

D. Topographical maps

   Digital topographical raster maps were integrated in the Forestry
Information System in order to automate the process of geometrical correction
of the digitized forest management maps.  These maps were made available by
the Surveying Administration of Niedersachsen.


   Training areas are test sites used for the signature analyses and for
defining the statistical characteristics of signatures of differently damaged
stands.  The characteristics are used as input for the computer-aided
classification.  The training areas should be representative for different
illuminations, slope and aspect conditions, heights and natural age classes as
well as for the different stages of damages occurring in the Harz Mountains.
For the selection of training areas, aerial photos were interpreted and field
work was carried out.

Aerial photos

   Aerial infrared photos on a scale of 1:6,000 and 1:7,000 were taken for
small and representative areas of the Harz Mountains.  They are used to
estimate the crown density and the percentage distribution of the different
damage classes.

   For the estimation of the crown density, a 1x1-mm grid (6x6 m on the
ground) was superimposed on the aerial photos.  The relation between grid
points covering a tree and those covering a gap between trees can be definend
as the crown cover percentage.

   The percentage of the different needle-loss classes was estimated by
interpretation of 3 trees per sample plot on a 30x30-m grid.  The
interpretation was carried out using the AFL interpretation key (AFL, 1988).
Delineation of training areas and verification areas in the satellite image is
very difficult without bridging the gap between ground and satellite by aerial

Field work

   Field work was done for altogether 300 training areas to evaluate stand
parameters which cannot be derived from aerial photos such as ground
vegetation and natural age classes of the stands.

                            5. SIGNATURE ANALYSIS

   The statistical analysis of the training areas was carried out by means of
the statistical analysis system SAS.  The results of the statistical analyses
can be summarized as follows:

Influence of crown density on the reflection of forest stands

   From the first results of the signature analysis it can be deduced that
even slight differences of crown density have an enormous influence on the
reflection of forest stands (Schardt, 1990).  The reflection difference can
mainly be seen in bands 4 (near infrared) and 5 (middle infrared).  The
scattergrams in  Figure 2 and  Figure 3 show the mean values of training areas
representing different crown cover percentages separately for different
needle-loss classes (x-axis).  The y-axis represents the grey values in bands
4 and 5.  All these stands belong to the same age and illumination class so
that reflection differences are mainly caused by different crown cover
percentages of the stands.

   In band 4 (near infrared) decreasing grey values are associated with a
decreasing crown cover percentage within the range from about 65% to 90%.
A further reduction of crown density from about 65% to 10% leads to increasing
grey values.  This phenomenon is due to the increasing influence of high
reflecting grass vegetation on the reflection of open stands.

   In band 5 (middle infrared) a slight reduction of grey values goes hand in
hand with a reduction of crown density within the range from about 65% to 90%.
Within the range from 65% to 10% a significant increase of grey values is
associated with a decreasing crown closure as in band 4.  The high deviation
of mean values in band 4 and 5 of open stands (10% to 60% crown cover
percentage) is due to different types of ground vegetation.

Influence of needle-loss on the reflection of forest stands

   An increasing percentage of needle-loss goes hand in hand with a
significant increase in grey values in band 5 (middle infrared) and a slight
decrease in grey values in band 4 (near infrared).  This signature
characteristic can be demonstrated by comparing training areas with different
needle-loss symptoms belonging to the same crown cover category.  The negative
correlation of these two bands due to needle loss symptoms can be shown more
clearly be the quotient of bands 4 and 5.

   Nevertheless, in comparing the signatures of dense stands which are
characterized by severe needle-loss symptoms with those of open stands without
needle-loss symptoms, no clear separation is possible.  From that it can be
deduced that the reduction of crown closure and reduction of needles has a
similar influence on the reflection of forest stands.  Therefore, a
satisfactory classification of needle loss without taking deforestation
symptoms into account is seen as very problematic.

Influence of tree age and illumination on the classification of damage

   Tree age has a similar influence on the reflection of forest stands as
defoliation and deforestation.  Independent of the damage class to which they
belong, older stands seem to be more damaged than younger stands.  Forest
stands on south and west slopes show a significantly higher reflection than
those growing on north and east slopes.

   The results of the signature analysis show that a more precise
classification of damage is to be expected when integrating digitized forest
management plans (tree age) and digital terrain models in the process of

                       6. DEFINITION OF DAMAGE CLASSES

   A focal point of this investigation is the definition of a damage class
which should correspond both to the requirements of satellite remote sensing
and the requirements of the forest administration on forest damage
inventories.  Remote sensing means measurement of spectral signatures.
Spectral signatures of forests are mainly influenced by the amount of needle
biomass per pixel.  In damaged forest stands this parameter results from both
stand density and the average degree of defoliation of the remaining trees.
Stand density as a (possible) damage symptom is completely neglected by field
sampling methods like ICP-Forest and similar estimation methods, whereas with
remote sensing methods it can/must be taken into account.

   The following matrix illustrates a damage class definition resulting from
different stages of defoliation (columns) as well as from different stages of
deforestation (rows).  This definition was suggested by the working group of
the Technical University of Berlin as a basis for discussion.  It will be
tested under operational conditions.

   Average crown    Defoliation class
   cover %          C0   C1   C2   C3
   -------------    --   --   --   --   D
       75-88%       D0   D1   D2   D2   a
       65-74%       D1   D2   D2   D3   m
       45-64%       D2   D2   D3   D3   a
       20-44%       D3   D3   D3   D4   g
        0-19%       D4   D4   D4   D4   e

Classification of defoliation (columns of matrix)

   Satellite classifications of forest damage using Thematic Mapper data
cannot give any information on the defoliation stages of single trees because
from picture elements with a spatial resolution of 30x30 m only an integrated
information on approximately 10 to 50 trees can be derived.  Because of the
heterogeneous spatial distribution of defoliation stages, most of the pixels
integrate trees that belong to different defoliation classes.  Therefore, it
must be examined whether these stages must be assigned to categories according
to their respective portion of different defoliation classes or to categories
containing a single defoliation class.  The latter class definition is only
possible if defoliation classes are distributed homogeneously so that mainly
one defoliation class occurs within one pixel.

   In the first phase of the German Harz project the defoliation categories
were defined as the portion of strongly damaged trees belonging to the
defoliation classes S2, S3, and S4 according to the common damage class
definition established by the working group of aerial photo interpretation
(AFL, 1988; or VDI, 1990):

   S0 =  0-10% needle loss
   S1 = 11-25% needle loss
   S2 = 26-60% needle loss
   S3 = 61-90% needle loss
   S4 = dead tree

   Because of the nonhomogeneous distribution of damage classes within stands
or picture elements of the satellite data, the damage cannot be defined as one
single damage class.  The classes must rather be defined as the portion of
differently damaged trees.  So new defoliation categories which better meet
the conditions of the spatial resolution of satellite images were defined by
Forster (1989):

   C0 =  0- 10% strongly damaged trees (S2-S4)
   C1 = 11- 33% strongly damaged trees (S2-S4)
   C2 = 34- 66% strongly damaged trees (S2-S4)
   C3 = 67-100% strongly damaged trees (S2-S4)

   Other definitons classifying the damages of stands are suggested by
Schmidtke (1987) and Neumann (1990).  One of these damage classifications
will be used in this investigation or, if necessary, new damage classes will
be developed.  Using the damage class definitions described above, the
defoliation category of a whole stand can then be calculated from its
composition of differently classified pixels.

Classification of deforestation / crown density

   The question of the necessity and practicability of integrating
deforestation stages as a damage symptom into the classification is also
investigated very intensively in this project.  Deforestation due to
decreasing crown density can be defined as the percentage of crown cover.
The maximal crown cover percentage of a stand without any gap can be assumed
as 84-88% (Assman, 1961).  Only in very young stands (thickets and young pole
timbers) does a crown cover percentage over 88% occur.

   In the matrix shown above, 5 different crown cover categories are defined.
The first category (75-88% crown cover) represents dense stands, whereas the
last category (0-19% crown cover) represents very strongly to totally
deforested stands.  The resulting stand condition categories (D0-D4) are
calculated by the total needle loss of a stand, which is the sum of
defoliation of single trees as well as the needle loss caused by missing

   D0 = < 10% needle loss + missing trees
   D1 = about 11-25% needle loss + missing trees
   D2 = about 26-50% needle loss + missing trees
   D3 = about 51-75% needle loss + missing trees
   D4 = > 75% needle loss + missing trees

   The practical applicability of this new damage class definition, which
takes into consideration the possibilities of damage classification by means
of satellite imagery, has to be investigated in this project.  Classifications
in a smaller area of the Harz Mountains ("Ackerbruchberg area") showed that
Thematic Mapper is a suitable instrument to classify the above mentioned
deforestation (crown density) classes.

   Nevertheless, auxiliary data such as digitized forest management plans or
other maps providing information about the actual forest distribution are
needed for a decision whether or not stand density has to be regarded as a
damage symptom, since low stand density may also be the result of
silvicultural treatment of forest stands.  Without any further interpretation
of the classification result, no statement concerning the damage situation of
the classified area can be made.  Furthermore, detailed knowledge about the
influence of the ground vegetation on the reflection of the whole stand is

Advantages of this new class definition

-  As in former forest damage inventories, the important information of
   needle loss of single trees in terms of portions of the different damage
   classes (S2-S4) will be integrated.

-  The very important information of the stand density which indicates the
   stability of a stand will also be considered.  For a correct interpretation
   of these new damage classes, knowledge of the classified area must be taken
   into account or digitized forest maps must be superimposed on the
   classification results in order to know what state the stand is supposed
   to be in.

-  The picture elements of the satellite data integrate trees (more or less
   damaged) as well as shadow in less deforested stands or ground information
   in stands with a decreased crown density.  This new class definition is
   therefore more adequate to the signal which is measured by the sensor.
   It is not possible to isolate the defoliation symptoms from picture
   elements integrating defoliation as well as deforestation featues.  This
   operation is only possible with the classification of defoliation
   categories within one deforested stage.

Disadvantages of this new class definition

-  The signal measured by the sensor system does not provide any information
   about the causes of a low crown density.  Here it is pointed out once more
   that auxiliary information is absolutely necessary to interpret the
   classification result correctly.  Additional information about the
   silvicultural treatment of the forest can be provided by digital forest
   maps.  For large areas, digital forest maps are not available and cannot
   be digitized in a reasonable time.  Knowledge about the forest is therefore
   necessary to decide whether the decreased canopy density is caused by
   silvicultural treatment of the forest or by forest damage.

-  The problem of recognizing damages caused by air pollution among clearcuts,
   windthrown areas or something similar 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 reflectance of forest stands.  The typical reflection
   characteristic of forest stands can be modified strongly by different types
   and phenological stages of ground vegetation.  Thus, a lower classification
   accuracy of deforestation and defoliation categories can be expected in
   stands with low densities.  Therefore, for stands with very low canopy
   density, no assessment of different defoliation categories is possible.
   Detailed field information and knowledge about the reflection
   characteristics of the forest floor is necessary to estimate the influence
   of ground vegetation on the classification accuracy.

-  Grass vegetation of deforested stands sometimes has an identical reflection
   to the vegetation of agriculturally used areas.  Additional information by
   the categories of forest and nonforest must be added.  Another possibility
   to overcome this difficulty is a multi-temporal approach.

                              7. CLASSIFICATION

   The classification was performed separately for different age classes and
illumination classes by a simple threshold method using the grey values of
band 4 and 5 (near and middle infrared).  The stratification of the different
age and illumination classes was realized using the digital terrain model and
the digitized forest management plans.  The comparison of the classification
result with aerial photos shows a satisfactory correspondence according to
deforestation symptoms.  In order to examine the classification result more
objectively, an independent aerial photo interpretation for representative
areas of the Harz was carried out simultaneously.

                                8. CONCLUSIONS

   Satellite data are useful for classifying forest damage when integrating
canopy density.  Although the establishment of a Forestry Information System
is too labor-intensive when used merely for the improvement of classification
results, integration of satellite classification into a Forestry Information
System can be very useful to support forestry management planning.  This
information cannot be derived from other already existing maps.  Satellite
data are important for documenting the temporal and spatial development of
forest damage.

   The ground resolution and the geometrical accuracy of the Thematic Mapper
system is not sufficient for overlaying with forestry maps at a scale of
1:10,000 without any further generalization.  Therefore, the forest units must
be joined together into larger areas in order to minimize the overlay error
caused by the edge effect.  It is not possible to define one "universal
method" for classification of forest damage.  Different types of damage, tree
species, site conditions and topographical conditions of the forest require
different classification strategies.

                                9. REFERENCES

Arbeitsgruppe Forstliche Luftbildinterpreten (AFL).  1988.  Auswertung von
   Color-Infrarot-Luftbildern.  Wien und Freiburg.  32 pp.

Assman, E.  1961.  Waldertragskunde, pp. 99-103.  Bayr. Landw. Verlag Munchen.

BML.  1984.  Waldschadenserhebung 1984, Bundesministerium fur Ernahrung,
   Landwirtshaft und Forsten.  Bonn.

Forster, B.  1989.  Untersuchung der Verwendbarkeit von Satellitenbilddaten
   zur Kartierung von Waldschaden.  DLR Forschungsbericht (DFVLR-89-06,

Hildebrandt, G.  1984.  Waldschadensinventur mit Hilfe der Fernerkundung.
   Z. Flugwissenschaft, Weltraumforschung, 8(1984), Heft5.

Kenneweg, H., M. Schardt & H. Sagischewski.  1993.  Large area operational
   experiment for forest damage monitoring in Europe using satellite remote
   sensing.  Proceedings of the International Symposium on Operationalization
   of Remote Sensing, April 19-23, 1993.

Neumann, M. & K. Schadauer.  1990.  Waldzustandsinventur, Methodische
   Uberlegungen und Detailauswertung.  FBVA - Berichte, Schriftenreihe der
   Forstlichen Bundesversuchsanstalt.

Niedersachsische Landesforsten.  1992.  Waldenwicklung Harz, Fachgutachten.
   Herausgeber: Niedersachsisches Ministerium fur Ernahrung, Landwirtschaft
   und Forsten.

Schardt, M.  1987.  Expositionsabhangiges  Reflexionsverhalten van
   Waldbestanden im Schwarzwald.  2. DFVLR-Statusseminar:  "Untersuchung und
   Kartierung von Waldschaden mit Methoden der Fernerkundung."  DFVLR
   Tagungsband, S. 328-347.

Schardt, M.  1990.  Verwendbarkeit von Thematic Mapper-Daten zur
   Klassifizierung von Baumarten und naturlichen Altersklassen.  Dissertation
   an der Forstwissenschaftlichen Fakultat der Universitat Freiburg.

Schmidtke, H.  1987.  Zur Definition von Schadniveaus fur bestandes- und
   betreibsweise Waldschadensinventuren.  Allgemeine Forstzeitschrift, Heft

Verein Deutscher Ingenieure (VDI).  1990.  Messen von Vegetationsschaden am
   naturlichen Standort.  Interpretationsschlussel fur die Auswertung von
   CIR-Luftbildern zur Kronenzustandserfassung von Nadel- und Laubgeholzen.
   VDI 3793, Blatt 2.