135
Structural information may be explicit or implicit. They
denote meaning of the data elements, relations between
them, and significance of relations. Such relations are first
of all topological and in addition also morphological,
hydrological or derived.
Continuous functions are used for approximation of the
modeled terrain surface. DTM is generally considered as
a 2.5 D surface with only one elevation attribute. There
are many possibilities for generating different functions,
which are based on interpolation of structured data
elements.
Quality information depends on semantic perception of
structure of the real Earth surface. This is the nominal
ground or desired level that is tried to reach with the
highest level quality of captured and modeled data.
Methods for implicit functions analyses are partly
connected with structural information data. But they are
more generally connected with methods for analyses in
GIS.
Figure 1: Flowchart of the DTM / DEM 25 modeling.
On the basis of this introduction the difference between
DEM and DTM can be made. DEM includes only elevation
data (look to data elements) that are generally not
considered as terrain surface. In most cases DEM is grid
data with elevation attributes, which is suitable to use for
analyses in raster GIS. Term DTM includes more general
information than DEM. DTM is a modeled surface
structure which contains also other data of terrain as
following: ridgelines, peak points etc. With simplification,
the term DTM may be used in general.
3. PREPARATION FOR INTERPOLATION
3.1 Interpolation draft
DTM interpolation from many data sources - as in our
case study - demands many data preparing and
managing steps. Main modeling steps are shown by
flowchart at figure 1 and described bellow.
3.2 Selection of test regions
Relief morphology of Slovenia is quite heterogeneous and
so it is not easy for terrain modeling. It can be roughly
classified to alpine, karst, hilly and flat surface regions.
Case study for DTM modeling bases on a test data which
has been chosen with respect of the mentioned
morphological classes. Test regions were optimized to
have as much as possible relief characteristic on relative
small areas. In the selected areas we were also trying to
include relevant quantity of available input data with the
elevation attribute.
Figure 2: Test areas in Slovenia: Krsko (1), Alpe (2) and
Kras (3).
On figure 2 we can see three test areas for DTM
modeling. The first (1) is hilly and flat surface which has
dimension of 11,250 x 18,000 m. The other two are alpine
-mountainous (2) and karst (3) regions with dimensions of
4,500 x 3,000 m.
4. DATA FOR DTM MODELLING
4.1 Description of potential data
We decided to use for case study only data that is
available at Surveying and Mapping Authority of the
Republic of Slovenia. The following are potential input
data:
1) raster data;
digital elevation model with 100 m raster (DEM
100),