DEM is surface data model to interpret the
spatial point data into surface. The data point data will be changed a
lot depending on the methods. It has great potential to create very big
errors if the methods are not selected properly. Some of surface
may have extreme big values out of the point boundary. Some of
techniques have to be used at this time to reclassify or exclude the
any points out of boundary.
2. Excluding the Errors
From Original Data
During
creating the factor surface layers, it is important to set some
boundary. For example, soil moisture
values are very lower because some of data were measured near
water and riverside. These groups of the data have no relation with the
elevation. After some of these soil moisture data points were take off,
the relation between soil moisture and elevation becomes clear.
The relation between
temperature and elevation is not clear in terms of linear or nonlinear
in the beginning.
The temperature at the snow linear is always closer to zero. The snow
line is up elevation boundary. When this point data
was added into data sets, it makes the linear relation clear and
reducing the
errors in the spatial analysis.