Zi and Z5 
al Z value. 
ored either 
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ected with 
led by the 
utting and 
utting and 
EM, slope 
'st may be 
or dividing 
s. 
nputes the 
'olumes or 
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Xf polygon 
7 
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ygon point 
area. The 
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s with the 
hmetic or 
of digital 
gration of 
ents etc.) 
Input data are two DEMS, one for the existing terrain, and the 
other for the planned structures surrounded by border lines. In 
the combined DEM the planned elevations are stored where the 
planning DEM exists. Outside the planned structures the Z 
values of the existing terrain are taken. 
The combined DEM can then be represented in form of 
perspective views or evaluated in form of profiles. 
62 Prediction of Soil Erosion 
A more complex function is used for the combination of the 
influencing factors for soil erosion. In most investigations the 
expected annual soil loss per unit area is described by the 
universal soil loss equation (USLE) (Wischmeier/Smith 1978) 
as the product of 6 influencing factors: 
A R*K*L*'*S*C*P 
with 
= expected annual soil loss per unit area 
= rainfall factor 
soil erodability factor 
slope-length factor 
= slope-steepness factor 
= cover and management factor 
= support practice factor 
MAYER > 
Il 
Amongst other applications, the USLE is being applied to 
terrain planning in land consolidation projects (Sigle 1991). 
The maximum tolerable slope length of the restructured terrain 
is computed by combining the slope factor S (derived from a 
digital slope model) and the soil erodability factor K. K is 
stored in a SCOP model which was built up from digitized 
polygon areas of a map of soil classes. All other influence 
factors could be kept constant for a local land consolidation 
area. 
The combination results in a digital slope-length model which 
can be represented in a map of classes of tolerable slope- 
length. 
A graphical presentation is given for a small part of the land 
consolidation project Sulzfeld in fig. 6. Data acquisition was 
done by the land consolidation authority of Baden- 
Württemberg. In practical use the soil loss prediction maps 
could be considerably improved compared to fig. 6 by using 
colour hatching or a z-coded raster representation for the slope- 
length classes. 
The Sulzfeld project has an extension of 5km x 5km. The 
DEM data were acquired by a photogrammetric grid 
measurement (44 178 points including 18 461 points on break 
lines, form lines and border lines). The K-factors were 
digitized by 869 polygon areas with 16 485 polygon points. 
The total project was realized under MS-DOS on a 80386 PC 
(33 MHz). It required a disk capacity of 40 MB and computing 
times of 64 minutes for DEM interpolation, 18 minutes for 
derivation of a digital slope model, 9 minutes for building up 
the K factor model, 58 minutes for the derivation of the slope- 
length model by combining the two SCOP models, and 3 
minutes for the output of a soil loss prediction map for the total 
area. 
   
  
881 
  
  
  
  
  
  
  
EE dt 
terrain data 
  
  
  
slope map 
Slope-length classes 
E 
ER 50-120m 
E 120-200 m 
» 200m 
« 50m 
tolerable slope-length 
Fig. 6: Soil loss prediction for a land consolidation project 
7. OTHER APPLICATIONS FOR A DEM 
INTERSECTION 
Two applications are described in the following which are used 
for the estimation of agricultural land (e.g. for land 
consolidation projects). 
7.1 Slope Statistics 
SCOP includes a module for the derivation of a digital slope 
model from a DEM. The slope model has the same data 
structure as the DEM, but the terrain heights are replaced by 
slope values (steepness in per cent). Break line information is 
rigorously considered in the slope model. 
For land estimation the slope model is intersected with pieces 
of land (polygon areas) by using several slope values as class 
limits. The results are the slope class areas for each piece of 
land. 
7.2 Soil Value Statistics (Polygon Overlay) 
An example for a polygon overlay is the computation of soil 
value areas for pieces of land. 
Soil value classes usually exist in form of polygon areas in soil 
value maps. The polygon areas have to be digitized and to be 
converted into a SCOP model which is then intersected with 
another set of polygon areas (e.g. pieces of land). Class limits 
for the intersection may be any soil values in between the 
different soil value classes of the map.