138 
ellipses) caused with deficient contour lines in the river 
beds (right) for the region Krsko (1). 
5.2 Extraction of characteristic points and lines 
Much better results were reached by detection and 
extraction of topographic features (ridges, summits, 
saddles, drainage lines and valleys) from contour lines. 
We applied one some possible methods which produce 
appropriate results. This expert system bases on TIN 
(triangular irregular network), created from contour lines. 
Principle of extraction characteristic lines is founded on 
determination and connection previously detected 
horizontal triangles of TIN to ridge or drainage lines. With 
interpolation and extrapolation considering contour lines, 
missing characteristic points are determinated (Heitzinger 
and Kager, 1998). 
Figure 7 shows that we can get quite good results also for 
the karst region (3), where the relief morphology is very 
complex. Especially generated characteristic points as 
bottoms of the sinkholes leads to most distinctive 
improvement to interpolated karst relief. 
Figure 7: Extracted characteristic points and lines at karst 
region (3) are shown as black points and lines. Grey lines 
are vector contour lines. 
A side product of TIN was also a DEM with resolution of 
40 m, which bridges missing data of contour lines, 
especially at the alpine regions where large “holes” 
appear without of any data. Additional datasets produced 
from contour lines which were used for interpolation are: 
characteristic topographic points, 
characteristic topographic lines, 
DEM 40 from TIN. 
6. DTM MODELING 
For modeling of a DTM 25 the program package SCOP, 
which is independent program system for the computation 
and utilization DTM, was used. The main advantage of 
this software is ability to use data with different accuracy 
in the interpolation process, what was our very important 
preliminary condition. Module SCOP.TRI includes 
powerful tool for enhancement of contour line data with 
characteristic points and lines. Method for robust 
estimation in module SCOP.DTM can be useful for 
correction of gross errors in input data (Ecker, 1999). And 
not the least, the SCOP produces DTM with relevant 
structure. 
6.1 Interpolation methodology 
Interpolation method used is known as “least squares 
interpolation” or “linear prediction”. In geostatistics the 
method is known as “kriging” (Kraus, 1998). Method 
bases on interpolation with least squares which requires 
the search for the minimal variance. 
Practically and shortly, this local interpolation method 
works with so-called computing units. It is attempting to 
find suitable function (theoretical surface) regarding to 
influence of the particular points, to which filter value 
(variance) has to be assigned. Filter values also control a 
degree of smoothing the surface. 
The data for interpolation was divided to particular classes 
with regard to type and accuracy. For each class different 
filter values were used for interpolation: 
1) bulk points; 
DEM 100, 
DEM 40 from TIN, 
2) spot heights; 
geodetic points (in this case used only as 
reference), 
characteristic topographic points, 
3) form line points; 
contour lines 
characteristic topographic lines, 
4) break lines (we haven’t any data for them). 
The lowest filter values were assigned to spot heights and 
the highest to bulk points. Geodetic points were used only 
as reference points for testing of input data. 
6.2 Results of DTM modeling 
The results of modeling the DTM / DTM 25 are very 
promising. Table 2 shows difference between accuracy of 
the vector contour lines and produced DTM 25. 
Parameters indicate improvement for all morphological 
classes, especially for Alpine areas. Implication of 
characteristic points above all in interpolation, caused also 
reduction of average distance according to reference 
points, except at flat areas where these points usually 
aren’t present. 
Morph, classes 
Contour lines 
DTM / DEM 25 
Flat surface (1) 
Hills (1) 
Mountainous (2) 
Karst region (3) 
1.5 m / 0.3 m 
5.0 m / 2.5 m 
10-40 m / 3.0 m 
4.0 m / 2.0 m 
1.2 m / 0.3 m 
4.0 m / 2.0 m 
10.0 m / 2.7 m 
3.0 m / 0.5 m 
Table 2: Morphological classes from three test regions 
(1-3) with parameters: RMS error/ average deviation from 
the reference points. 
Figure 8 shows evidently improvement of interpolated 
DTM 25 (the right picture) at the areas without contour 
lines. There are not noticed large triangles. For the other 
three test regions improvements are better statistically 
than visually perceived.