Mathias Schardt, Technical University of Berlin

                   Klaus Martin, SLU Consulting Grafelfing

            Manfred Keil, German Aerospace Research Establishment

ABSTRACT:  In 1987 large parts of the "Nurmberger Reichswald" were damaged by
a moth infestation (Lymantria monacha).  The damaged area and the level of
damages were mapped by using satellite data from Landsat Thematic Mapper.  The
additional information of stand conditions (soil maps) and tree age (forest
management plans) was integrated with a geographical information system
(Arc/Info).  For verification, the results of the classification were compared
with the damage inventory of the Bavarian Forest Experiment and Research
Station, based on a color-infrared photo-interpretation.  The classification
result was merged together with the digitized maps in order to provide further
information about the damage distribution in relation to different soil and
stand parameters.

                               1. INTRODUCTION

   In 1987 large parts of the "Nurnberger Reichwald" were damaged by a moth
infestation (Lymantria monacha).  Very often this moth appears in great
numbers and then leads to very serious forest decline.  The caterpillar eats
the needles and leaves of all tree species, preferably the needles of spruce
(Picea abies) and pine (Pinus silvestris).  The most serious effect of the
outbreak occurred in the pine stands of the forest district "Allersberg",
which is located 30 km south of Nurnberg.  The forest of this district is
characterized by pure pine stands of nearly the same age class growing on dry
and sandy soils.  The homogeneous age class distribution (mostly old stands)
is a result of a very severe moth outbreak in the beginning of the 20th
century.  Spruce only occurs on soils with sufficient water supply along small
rivers and in the understory of pine stands.  The homogeneity of the pine
stands makes the forest very vulnerable to insect outbreaks.

   The distribution of the damages in the pine stands was homogeneous too, so
that it was possible to separate training areas mainly consisting of one
damage class.  The aim of this investigation was to demonstrate the
applicability of Landsat Thematic Mapper data for mapping the damaged area and
the level of damages.  For this purpose the additional information of stand
conditions was integrated with a geographical information system (GIS).

                          2. MATERIALS AND METHODS

Thematic Mapper data

   For the investigation, Landsat Tematic Mapper scenes of three different
dates (April and October 1987, and April 1988) were used.  The scenes were
geometrically rectified using 20 control points based on Gauss-Kruger
coordinates and a polynomial of second order.  After rectification, the
difference between the three scenes was less than one pixel.  The scenes were
also radiometrically adjusted using undamaged stands as reference areas.

Color-infrared aerial photographs

   In September 1987, color-infrared aerial photographs (scale 1:10,000) were
taken of the damaged area.  A photo-interpretation performed by the Bavarian
Forest Experiment and Research Station was used for ground truth and for the
verification.  In this investigation altogether 24,628 sample plots (three
trees per plot) located on a 25x25m grid were interpreted in order to get
information about the area, the distribution and the percentage of damage
classes.  Four damage classes were separated (Table 1).  The aerial infrared
photographs and the photo-interpretation were used for the selection of
training areas.  Training areas, especially undamaged stands, were also
checked on the ground.

Table 1.  Damage class definition of the Bavarian Experiment and Research

   Damage class         Loss of needles
   ------------         ---------------
        0                    10%
        1                    10-50%
        2                    51-90%
        3                    91-100%

Digital soil maps and forest management plans

   Keil et al. (1988a,b) showed that the reflectance of pine stands is
strongly influenced by the effects of soil conditions.  To integrate the
information on different water and nitrogen supply conditions, soil maps of
the forest district Allersberg were digitized.  To minimize the registration
error between the Thematic Mapper image and the soil map caused by the "edge
effect", the total of 30 different soil types were merged into only 5
different main classes by using the geographical information system Arc/Info
(Keil et al., 1990):

       - sands with low water supply
       - clays with low water supply
       - sands with sufficient water supply
       - clays with sufficient water supply
       - other soil types (mostly wet soils along small rivers)

With this procedure the registration error was less than one pixel.  In order
to integrate further information about tree age and other important stand
parameters, the boundaries of the departments of the forest management plan of
the district Allersberg were digitized.

                            3. SIGNATURE ANALYSIS

Signature comparison of undamaged pine stands on different soil types

   In order to examine the influence of different soil-moisture contents on
the reflectance of pines, the signature histograms of unaffected pine stands
on wet (mostly clay soils) and on dry soils (mostly sandy soils) were
compared.  This investigation demonstrated increased signatures associated
with a decreasing moisture content of the soil in band 5 (middle infrared) and
band 7 (far-middle infrared).  The causes of the different reflectance can
first be seen in the lower water content and the lower water transpiration of
the pine needles of stands growing on soils of low water supply.  Secondly,
the transparent crowns and the slightly reduced crown closure of those pine
stands lead to an increasing portion of illuminated ground vegetation
consisting of bilberry and grass.  In bands 1-4 (blue, green, red and near
infrared) no significant signature changes associated with different site
conditions could be observed.

Signature comparison of differently damaged pine stands (all soil-types)

   For the signature analysis, damage classes three and four were combined.
 Figure 1,  Figure 2 and
 Figure 3 show the histograms of stands with damage classes 0, 1 and
2/3 without any prestratification due to different classes of soil moisture
for the Thematic Mapper bands 3 (red), 4 (near infrared) and 5 (middle

   The histograms of band 5 (middle infrared) show a significant increase of
grey values associated with an increasing percentage of needle loss ( Figiure 3).  By using the reflectance difference in this band, it is possible to
separate damage classes 0, 1, and 2/3.  From this result it can be deduced
that a decreasing water supply and an increasing needle loss of pine trees
leads to similar signature characteristics.  Thus a stratification of the area
into areas of lower and higher water supply categories is necessary to
increase the separability of different classes of needle loss.  In band 3
(visible red) the grey-values also increase with loss of needles, but not as
significantly as in band 5 ( Figure 1).

   In band 4 (near infrared) a decrease of the reflectance goes hand in hand
with an increase of needle loss.  The difference of reflectance between stands
of damage class 0 and 1 is not sufficient to separate them clearly.  A
decisive reason for the signature overlapping can be seen in the immense
influence of ground vegetation of damaged pine stands, because grass and
bilberry cause high reflection in band 4.

   Because of the negative correlation of TM band 4 (near infrared) and 5
(middle infrared) according to needle loss symptoms, the separability of the
damage classes can be increased by computing the difference band TM 4 minus
TM 5 (Forster, 1989).   Figure 4 shows the signature of pine stands without
consideration of different soil conditions.

Signature comparison of differently damaged pine stands (stratified by soil)

   Finally after stratification of the training areas into dry and wet soils,
a more satisfactory separation of all three damage classes (0, 1 and 2/3) was
possible ( Figure 5 and  Figure
6 ).  From this histogram comparison it can be seen
quite clearly that the most significant improvement of separability could be
reached for the damage classes S0 and S1.

                              4. CLASSIFICATION

   The visual interpretation of the Thematic Mapper color composite already
shows different damage classes.  For classification, the simple method of
thresholding was used.  Each damage class was fixed in the difference band
TM 4 minus TM 5 by separate thresholds for areas of dry and wet soils (Table
2).  The stratification of the Thematic Mapper image into areas of dry and wet
soils was performed digitally using the digitized soil map.  In comparison to
the maximum likelihood method, the simple threshold method offered a very fast
and distinct method for this application.

Table 2.  Thresholds (grey values) for separating damage classes in the
          difference band TM 4 minus TM 5.

                             Damage class
   Soil conditions      2/3       1         0
   ---------------     -----    -----     -----

      Dry soils         <73     73-82      >83

      Wet soils         <75     75-84      >84

Combining the classification result with the digitized maps

   Combining the classification results with the soil map and the forest
management plan can provide additional information about damage
characteristics in the form of tables (Arnold et al., 1989).  Pine stands
on dry and sandy soils were more damaged than stands on wet and clay soils
(Table 3).  Younger stands were less damaged than older ones (Table 4).

Table 3.  Damage class distribution on different soil types.

                             Percentage of damage class
   Soil conditions             0         1        2/3
   ---------------           -----     -----     ------

      Dry soils              46.5%     40.7%     12.8%
      Wet soils              78.6%     19.5%      1.9%

      Sands                  58.3%     34.9%      7.8%
      Clays                  85.5%     13.5%      1.9%

Table 4.  Damage class distribution in relation to stand age.

                        Percentage of damage class
   Stand age              0         1        2/3
   ---------            -----     -----     ------

      <20               57.5%     36.9%      5.6%
      20-60             33.3%     56.7%     10.0%
      >60               34.0%     57.5%      8.5%


   To verify the results, the differences between the damage distribution of
the TM-classification and the aerial infrared photo-interpretation were
computed for each of the 34 compartments.  To calculate the average
classification error, these differences were weighted by the size of each
compartment.  Table 5 shows a satisfactory correspondence (more than 80%)
between the satellite classification and the photo-interpretation.

Table 5.  Verification of the classification results.

   Damage class         % correspondence
   ------------         ----------------

        0                      80.7%
        1                      80.3%
        2/3                    95.7%

                                6. CONCLUSIONS

   Thematic Mapper data is qualified for mapping biotic damage such as moth
infestation.  A prerequisite for a successful classification is a homogeneous
distribution of the damages so that one pixel can be defined as one damage

   The reflectance of pine stands depands very much on the water supply of the
trees arising from different soil types.  The consequences of this phenomenon
are that poor water supply and needle loss symptoms have the same effect on
the signature.  By integrating additonal information on soil types into the
process of classification using digitized soil maps provided by a geographical
information system, it is possible to improve the classification accuracy.

                                7. REFERENCES

Arnold, F., H.-U. Hartmann and M. Keil.  1989.  Fernerkundungsdaten als Input
   in das raumliche Informationssystem LANIS am Beispiel der Waldkarte
   Regensburg.  Fernerkundung, Daten und Anwendungen, Leitfaden 1.  Beitrage
   der Interessengemeinschaft Fernerkundung, Wichmann Verlag, Karlsruhe.

Keil, M., M. Schardt, A. Schurek and R. Winter.  1988a.  Forest mapping with
   satellite imagery in Bavaria.  Proceeding of the Willi Nordberg Symposium,

Keil, M., J. Janot, I. Roth, M. Schardt, A. Schurek and R. Winter.  1988b.
   Waldkartierung mit Satellitendaten in Bayern.  2. DLR-Statusseminar
   "Untersuchung und Kartierung von Waldschaden mit Methoden der
   Fernerkundung", Oberpfaffenhofen.

Keil, M., M. Schardt, A. Schurek and R. Winter.  1990.  Untersuchung und
   Kartierung von Waldschaden mit Methoden der Fernerkundung.  Auswertung von
   Satellitenbilddaten.  DLR Abschulssdokumentation Teil B7, pp. 71-135.

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