i think the interpolation method for dem changes to everybody for area of usage.
Not only interpolation method, but also creating dem method can change to usage. For example if you want to work more complex areas you should use TINs instead of altitude matricez structure to create dem. there is lots of information about interpolation but i want to give you sth. as following text.
This is from -- Bill Huber ,
Quantitative Decisions (http://www.quantdec.com )
Resampling an aspect map will either be (a) crude, if you use
nearest-neighbor resampling, or (b) meaningless, if you use
interpolation (bilinear or cubic convolution are available). This is
because the interpolation methods compute an average. For instance,
aspects in a northward-sloping region will all be near zero or 360
degrees, for an average of about 180 degrees: due south! Thus, the
answer for an aspect map is clear: recompute the aspect of the
resampled DEM; do not resample the original aspect map.
For slopes, the answer depends on what you use the slopes for. A
resampled slope should be generated using an interpolator, rather than
nearest neighbors. It therefore represents an average slope within
each cell. The average can sometimes be much greater than the apparent
slope at the new scale. Consider, for instance, an area around a small
incised stream on a mountainside. At the 10m resolution, you might see
a wide variety of quite extreme slopes. At the 50m resolution, perhaps
all you see is the mountainside. The average of the 10m slopes can be
much higher than the apparent slope at 50m resolution.
Unless you want the slope map to reflect this average of slopes over
smaller regions, you might as well assure consistency among the
elevations, slopes, and aspects by recomputing slope and aspect from
the resampled DEM. It would be best to use cubic convolution for the
resampling of the elevations. This ought to produce a DEM that has no
spurious NoData values in its aspect grid.
And definition of interpolation according to Arcgis help:
Inverse Distance Weighted Interpolation (IDW)
" IDW interpolation explicitly implements the assumption that things
that are close to one another are more alike than those that are
farther apart. To predict a value for any unmeasured location, IDW
will use the measured values surrounding the prediction location.
Those measured values closest to the prediction location will have
more influence on the predicted value than those farther away. Thus,
IDW assumes that each measured point has a local influence that
diminishes with distance. It weights the points closer to the
prediction location greater than those farther away, hence the name
inverse distance weighted."
"Like IDW interpolation, Kriging forms weights from surrounding
measured values to predict values at unmeasured locations. As with IDW
interpolation, the closest measured values usually have the most
influence. However, the kriging weights for the surrounding measured
points are more sophisticated than those of IDW. IDW uses a simple
algorithm based on distance, but kriging weights come from a
semivariogram that was developed by looking at the spatial structure
of the data. To create a continuous surface or map of the phenomenon,
predictions are made for locations in the study area based on the
semivariogram and the spatial arrangement of measured values that are
For more information about interpolation on surfaces pls read Interpolating Surfaces in ArcGIS Spatial Analyst which is a great information to understand interpolation with graphs. And after this i think you will find which is proper for your needs.
And beside arcgis, you dont have to use only arcgis for getting dem data. You can use gdal_grid to get correct dem from your data. it has lots of option as interpolation algorithms.
Example usage :
gdal_grid -zfield "Elevation" -a invdist:power=2.0:smoothing=1.0 -txe 85000 89000 -tye 894000 890000 -outsize 400 400 -of GTiff -ot Float64 -l dem dem.vrt dem.tiff
Beside this you can use classify_terrain.py too...
i hope it helps you...