This paper presents a procedure for assessing the quality of a digital
elevation model (DEM) which has been applied to the output of a
normalized cross correlation based stereomatching algorithm. Using
semimetric photography of natural gravel river bed surfaces acquired in
the field, digital photogrammetry was used to extract DEMs
automatically for Else in characterizing surface roughness properties.
The procedure for assessing DEM quality involves examination of (i)
ortho-images, to provide a qualitative check on stereomatching
performance; (ii) DEM collection statistics which quantify the
percentage of correctly matched pixels as a function of those
interpolated; and (iii) height differences between check points,
measured using independent field survey and corresponding DEM points.
The concepts of precision, accuracy and reliability are defined in the
context of DEM quality assessment and methods are outlined which can be
used to assess these variables. The assessment is conducted for two
adjacent stereopairs with similar characteristics, considering the
effects of both DEM collection parameters and different lens models
upon DEM quality.
Results show that digital photogrammetry, in conjunction with
independent field survey can be used successfully for extracting high
resolution, small scale DEMs from natural gravel surfaces. Components
(i) and (ii) of the quality assessment suggest the need to optimize DEM
collection parameters, although the effects of not using a properly
specified lens model were minimal at this scale. Method (iii) showed
that increasing stereomatching success does not necessarily lead to
more accurately estimated DEM points. However, the use of method (iii)
remained difficult because of the scale of the photogrammetric
application being used; check point positioning within the
photogrammetric co-ordinate system was only possible to +/- 10 mm
which, for a gravel bed surface, was associated with elevation variance
of a similar, sometimes greater magnitude. The next stage of this
research will require the use of higher quality check data, possibly
from laser profiling.