The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
The SRTM Digital Elevation Model was processed and 
maintained by the Consultative Group for International 
Agriculture Research Consortium for Spatial Information 
(CGIAR-CSI). In the form compiled and maintained by 
CGIAR-CSI, the SRTM elevation data have a spatial resolution 
of 90m. This data set is seamless with all voids filled using a 
methodology based on spatial filtering (Gorokhovich and 
Voustianiouk, 2006). The CGIAR-CSI SRTM 90m digital 
elevation data sets are provided to the general public in 5° X 5° 
tiles in computer-compatible raster formats (GeoTiff and 
ARC/INFO ASCII Grid). The data set is in LatLon coordinate 
system projected on the WGS 84 Ellipsoid. For the purpose of 
our study, one of the tiles covering the chosen study site was 
downloaded from the CGIAR-CSI Web site at http://srtm.csi- 
cgiar.org. 
One topographic map sheet at the scale of 1/50,000 covering the 
chosen study site was selected for use as a reference for 
analyzing the SRTM elevation data. The map sheet was 
digitized into layers (hypsometry, hydrography, transportation 
and built-up) as part of the input data sets within the 
framework of an on-going state-wide topographical mapping 
project undertaken by the Ondo State Government of Nigeria. 
In this accuracy assessment study, only the hypsometric and 
hydrographic layers were obtained from the project consultants. 
The selected layers were digitized from an existing paper map 
sheet compiled in 1965. The map was based on the UTM 
projection (Zone 31) on Clarke 1880 Ellipsoid and had a 
contour interval of 50 feet. 
GPS elevation data used in this study were acquired during a 
field truthing mission organized by the Ondo State 
Topographical Mapping project consultants as part of activities 
within the framework of the state-wide 1/25,000 topographical 
mapping project. During the GPS survey exercise, GPS 
measurements were made with a vertical accuracy of ± lm at 
randomly visited points. Since the GPS observations were 
meant to validate contours interpolated from the existing 
topographical maps, they were originally transformed into the 
coordinate system of the maps (UTM projection on Clarke 1880 
Ellipsoid). 
3.2 Materials 
Three major software packages were employed for the 
processing of the data and the visualization and analysis of the 
results. These included the royalty-free, open-source Integrated 
Land and Water Information System (ILWIS 3.4), ArcGIS 9.2 
(proprietary) and Microsoft Excel. In addition, we developed a 
number of in-house programs in Visual Basic 6.0 for 
performing some specialized functions such as coordinate 
transformation, terrain profiling and elevation data extraction 
from ASCII raster data sets. 
3.3 Methodology 
The methodology adopted in this study was in keeping with the 
main objectives of the study as stated in Section 1 of this paper. 
3.3.1 Data preparation: The data sets employed in this study 
emanated from disparate sources based on different formats, 
coordinate systems and projections. The first step in the 
exploitation of the data sets was therefore the transformation of 
all the data sets into a common system. Since the CGIAR-CSI 
SRTM 90m digital elevation data sets were in LatLon WGS 84 
system, the topographic map layers (contours and rivers) and 
the GPS elevation data in UTM Clarke 1880 system were 
transformed into the LatLon WGS84 system using tools 
available in ILWIS 3.4 software. To restrict the test to the 
chosen study site, it was expedient to extract only the GPS 
points that fell within the extents of the study area. To perform 
this operation, we implemented a small program in Visual Basic 
6.0 to clip the GPS point set using the extents of the study site. 
Using our program, the hypsometric layer with contour values 
in feet was metricated by transforming it into a new layer with 
all the contour values multiplied with a Z-factor of 0.3048 
Since our study also involved the accuracy tests of contour 
interpolation from the 1:50,000 topographical map, it was 
necessary to process the source 1:50,000 topographic map into 
a form appropriate for the test. To satisfy this requirement, we 
created a grid-based digital elevation model with a resolution of 
90m (corresponding to the resolution of the CGIAR-CSI SRTM 
DEM) from the metricated 1:50,000 topographic map using the 
contour interpolation function available in ILWIS 3.4. This 
involved first rasterizing the contour map layer and then 
interpolating between the isolines using the method described in 
Gorte, B.G.H. and Koolhoven W. (1990). 
3.3.2 Determination of the vertical accuracy of Topo DEM 
and SRTM DEM: Several publications on the accuracy of 
CGIAR-CSI SRTM 90m elevation data report that its absolute 
vertical accuracy is in the order of ± 16m (Koch, A. and 
Lohmann, P., 2000; Miliaresis, G. and Paraschou, C. V. E., 
2005; Muller, J. P., 2005). This accuracy value has been 
extensively tested in different regions under different terrain 
characteristics by many researchers (Giacomo F. et al, 2005; 
Brown, C. G. et al, 2005). Results of such tests showed that the 
absolute vertical accuracy of the SRTM elevation data depends 
on terrain characteristics. In Gorokhovich and Voustianiouk 
(2006) for example, it was shown that two topographic 
derivatives, slope and aspect, significantly influence the 
absolute vertical accuracy value. The study showed that steeper 
slopes recorded higher vertical errors than gentler slopes, while 
SRTM data underestimated elevations with North West aspect 
and overestimated elevations with South East aspect. In our 
study, emphasis was placed only on the absolute vertical 
accuracy of Topo DEM and SRTM data covering our study site. 
Determining the absolute vertical accuracy of SRTM data 
basically involves computing the standard deviation statistic of 
the elevation differences between the SRTM data and a 
reference data set such as GPS point measurements. This 
requires overlaying the GPS points on the SRTM and extracting 
the heights from the two data sets at their position of 
coincidence and using these values to compute the accuracy 
statistic. Gorokhovich and Voustianiouk (2006) described a 
method for performing the overlay. Their approach involved 
first converting the SRTM raster data set into a vector-based 
GIS layer containing as many polygons as there were grid cells 
in the SRTM data and then performing a spatial join of the 
point data and the new polygonal layer to extract the height data 
for the statistical analysis. This method may prove to be highly 
demanding in computer memory and may turn out to be 
computationally-intensive to handle especially where the point 
data set is large. In our study, we adopted a simpler method of 
performing the spatial join. Our approach involved projecting 
the X, Y Cartesian coordinates of the point data into their 
equivalent grid image space (rows and columns). These rows 
and columns were then used to access the value stored at the 
corresponding grid cell location. We implemented a small 
Visual Basic program (using the MapWindow programmable