1 Spatial Modelling of the Terrestrial Environment - Georeferencial

Remotely Sensed Topographic Data for River Channel Research 133
Table 6.5
The effects of ground control point discrepancy removal upon DEM elevations
Pre-discrepacy Pre-discrepacy Post-discrepancy Post-discrepancy
removal: removal: standard removal: removal: Standard
DEM mean error (m) deviation of error (m) mean error (m) deviation of error (m)
February 1999 0.174 2.113 0.084 0.261
March 1999 0.154 0.260 0.010 0.261
February 2000 0.110 0.141 0.088 0.131
Table 6.6 Check data, photogrammetrically acquired points for elevations that were not
inundated, corrected photogrammetrically acquired points for elevations that were not inundated,
and the associated error statistics
Number of Raw Corrected Theoretical
check points standard Corrected standard standard
available Raw mean deviation mean deviation of deviation of
DEM (raw data) error (m) of error (m) error (m) error (m) error (m)
February 3700 0.542 ±2.113 0.084 0.261 ±0.070
1999
March 241 0.252 ±0.883 0.010 0.261 ±0.070
1999
February 1661 0.064 ±0.926 0.088 0.131 ±0.056
2000
all the ground control points used in its determination. However, if multiple stereo-pairs
are being used in a common bundle adjustment, there is a growing possibility that there
will be discrepancies between the derived DEM elevations and the correct ground control
point elevations. This should provide a useful means of detecting systematic DEM errors
in situations where there is banding, provided the random contribution to those errors is
small. To assess this, discrepancy maps were contoured and an example is shown in Plate 6.
for February 2000. Although this was a DEM for which little banding was thought to exist,
Plate 6 shows that there are quite considerable discrepancies for some, but not all, of the
photocontrol data points. These discrepancy maps were then used to remove the effects
of banding by subtracting the discrepancies from the stitched DEMs. This resulted in a
statistically significant reduction of ME in all three cases, with, as expected, negligible
changes in SDE (Table 6.5). This demonstrated that this aspect of post-processing resulted
in a significant reduction in systematic error.
6.6 Summary of Data Quality
Table 6.6 shows the results of the correction methods developed for and applied to this
dataset. In all cases, the SDE has been reduced significantly. In the two cases where there
was a serious ME, this has also been reduced. This demonstrates the potential of automated
data correction methods for improving surface quality to levels that are more acceptable.
Lane et al. (2003) propagated the standard deviations of error shown in Table 6.6 through
to surfaces of difference to identify where there had been statistically significant change