1 Spatial Modelling of the Terrestrial Environment - Georeferencial

122 Spatial Modelling of the Terrestrial Environment
(a) Raw DEM test area (b) Dry, non-veg., matched data (c) Surface interpolated from (b)
(d) Local σ, radius = 5 m (e) Local σ, radius = 10 m (f) Local σ, radius = 15 m
(g) Local σ, radius = 20 m (h) Local σ, radius 25 m (i) after correction
Figure 6.4 Diagrams to illustrate application of standard deviation-based filtering of error
(approach 1) to the test area. Part (b) shows the data points that were matched, in non-inundated
and non-vegetated areas and part (c) the surface interpolated from these. Parts (d) through
(h) show the effects of calculating point standard deviation for different search radii. Part (i)
shows the final accepted combination of search radius (20 m) and filter tolerance (0.5 m),
demonstrating that many of the spikes and pits in part (c) have now been removed
neighbouring and including the pixel (i.e., a 3 × 3 matrix), σ z is the SD of those elevations
used to calculate, and m is a user-specified value. This is a local topography-based filter
that will clearly be effective in the removal of isolated spikes and dips. However, it will break
down where there are clusters of points in error, reflected in the retention of significant error
in Figure 6.2(a). The effect of equation (6) was explored for a test area (Figure 6.4(a)),
which contains isolated points in error (Figure 6.4(c)) after filtered out of unmatched, dry
and vegetated points (Figure 6.4(b)). This was applied in a manner similar to Felicísimo
(1994) for grid-based data but used in this case with point data. A local SD was calculated
for circles of varying search radii. A point was eliminated if the calculated SD centred on a
point was greater than a SD tolerance. Note that if the method had been applied to the fullgridded
DEM, then it would have been ineffective because point errors would have been