Ibérica na região de Trás-os-Montes (NE Portugal) - Universidade ...

CAD. LAB. XEOL. LAXE 26 (2001) Geostatistical interpolation 189
Figure 9a. The results of the interpolation, the
digital elevation model (DEM) of the catchment.
the interpolation, 21,500 m 2 has an interpolation
error between three quarter of a
meter and one meter. This sounds reasonable,
but in a low-relief landscape like this
catchment, this can have large influences
on notably the runoff direction.
Furthermore, kriging is an interpolation
method that generates a ‘smoothed’
interpolation surface, without the natural
discontinuities or abrupt changes that in
reality may be present in the landscape.
An alternative method such as Monte
Carlo simulation simulates a more natural
‘rough’ surface on basis of a given variogram
and the (estimated) population standard
deviation.
However, if the results are compared
with another method of DEM creation,
like digitising and rasterisation of analogue
maps and remote sensing techniques,
then it emerges that this method is relatively
precise and determinate. Besides, the
errors are known and this is not the case in
Figure 9b. The kriging standard deviations
after the interpolation.
several other methods of DEM elaboration.
The knowledge of these errors helps
in the evaluation of the outcomes of
models that use the derivatives of DEM’s.
CONCLUSIONS
Of the above discussion and presentation
of the results, the following may be
concluded:
• An interpolation with an anisotropic
Gaussian variogram with the search radius
adjusted to the anisotropy, applying
Universal kriging with a first order trend
surface, was the best kriging action possible
in this case study.
• The quality of the interpolation is
rather satisfactory when compared to
other methods of DEM creation, but
interpolation errors still may influence the
derivatives of the DEM and so the subsequent
outcome of the models that use
these derivatives.