The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
ILWIS 3.4 and profiles plotted. Figure 5 shows an example of 
the terrain profile along one of the transects. 
Transect A-B: DEM Elevation X Cumulative Distance 
Figure 5. Terrain profile along Transect A-B 
3.3.5 Assessment of the cartographic quality of the SRTM 
DEM: One major requirement for deploying contours 
interpolated from SRTM elevation data in 1/25,000 mapping is 
that the contours must be of good cartographic quality. 
However, SRTM DEMs have been shown to suffer from a 
number of gross, systematic and random errors propagated from 
the Synthethic Aperture Radar (SAR) imaging system. As 
demonstrated by Koch, A. and Lohmann, P., (2000), SAR 
imaging system is affected by errors due to baseline tilt angle, 
baseline length, platform position, phase and slant range. These 
errors are known to affect the accuracy and the quality of 
SRTM DEM and its derivatives such as slope, aspect and 
contours. In contours interpolated from the SRTM DEMs for 
example, such errors may manifest as artefacts such as short 
pieces of unclosed contour lines, self-intersecting contour lines 
and contour lines intersecting other contour lines with different 
contour values. The issue of correcting errors in digital 
elevation models has received the attention of many researchers. 
For example Ping Wang (1998) employed 2-D Kalman filtering 
approach to generate optimal estimates of terrain variables from 
a noisy DEM. The approach comprised of using a 2-D Kalman 
processor, a function for outlier detection and a two-step 
filtering procedure. More recently, Emanuel Mahler (2001) 
implemented the wavelet transform model for scale-dependent 
filtering of high resolution digital terrain models. In our study, 
we investigated the cartographic quality of contours 
interpolated directly from SRTM DEM. The objective was to 
determine the suitability of using such a product in 1/25,000 
topographical mapping and to propose, employ and evaluate a 
methodological approach in order to determine its effectiveness 
in improving the cartographic quality of contours derived from 
SRTM DEMs. The steps involved in this accuracy assessment 
method were: 
(1) interpolating contours directly from the SRTM 90m 
DEM and visually inspecting the quality of the 
contours 
(2) re-sampling the SRTM 90m DEM to twice the 
resolution (45m) and visually analyzing contours 
derived from the new DEM; 
(3) subjecting the SRTM 90m DEM to further 
processing by re-interpolating the surface from its 
elevation data and visually analyzing the quality of 
contours derived from the new product; 
(4) visually comparing the results of the above 
procedures. 
The goal of the first step above was to visually assess the 
cartographic quality of contours interpolated from the CGIAR- 
1351 
CSI 90-m resolution SRTM DEM without subjecting it to 
further processing. To achieve this goal, the contour 
interpolation function available in ArcGIS 9.2 GIS was used to 
create a vector contour map with a vertical interval of 5m (a 
vertical interval value recommended for a 1/25,000 topographic 
mapping). A small window extracted from the resulting contour 
map is presented in Figure 6(a). From this map extract, it can 
be seen that direct interpolation of contours from the 90-m 
resolution SRTM DEM produces artefacts as highlighted in the 
encircled areas of the map. 
The second step in the assessment of the cartographic quality of 
the SRTM data was executed to visualize the impact of further 
processing of the data set on its cartographic quality. This 
procedure involved simply applying a pixel densification 
operation by a process of raster re-sampling. In this study, we 
executed this process by performing a cubic interpolation of the 
90-m resolution SRTM DEM to re-sample the data from 90m to 
45m. The result of this operation is presented in Figure 6(b) as a 
map extract using the same window coordinates as in the first 
step. 
The third step in the assessment of the cartographic quality of 
the SRTM data involved re-interpolating the SRTM DEM 
surface from a point raster map derived from the SRTM DEM 
using the Inverse Distance Weighted (IDW) point interpolation 
function. The principle of this interpolation technique, also 
called the moving surface function, is well documented in the 
literature (see, for instance, ILWIS, 2001). This technique 
involves determining a new output value for a given cell by 
fitting a polynomial surface through all points weighted by their 
individual weight factors considering only points that fall 
within a certain limiting distance (search radius) towards this 
pixel. The weight functions ensure that points close to an 
output pixel obtain a larger weight value than points which are 
farther away from an output pixel. In this study, we executed 
this interpolation operation by adopting a methodological 
approach as follows. First a point map was created by running a 
small Visual Basic 6.0 program developed in-house. The 
program created a point map in which the points corresponded 
to the centres of the cells in the SRTM 90m grid-based map. 
The point map (with SRTM DEM heights as attributes) was 
then imported into the ILWIS 3.4 environment where its 
variogram surface was calculated. The calculated variogram 
surface enabled an optimal search distance for the moving 
surface interpolation to be established. For the point data set 
covering our study site, this value was found to be 2.5 times the 
grid cell size. The actual interpolation was executed in ArcGIS 
9.2 environment using the IDW interpolation function. This 
operation was executed twice (for 90-m and 45-m resolutions) 
each time setting the search distance of 2.5 times the cell size 
and a weight exponent of 2. The resulting raster grid map was 
then passed to a contour interpolation function to create a 5-m 
vertical interval contour map. The resulting contour maps are 
presented in Figures 6 (c) and (d). 
To further visualize the spatial relationships between the three 
resulting contour maps above, we extracted a small portion of 
these maps and overlaid them in the ArcGIS 9.2 GIS 
environment as shown in Figure 6(e). To investigate the degree 
of fitness of the hydrography with the derived contour maps, we 
super-imposed the hydrographic network as shown in Figure 
6(f).