The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
0 (due north) to 360 (again due north). The value of each 
location in an aspect dataset indicates the direction the surface 
slope faces. The slope is presented in Figure 4 and the aspect 
regions defined from the reference CDED level 1 are by 
geographic directions. 
Figure 4: Slope classes of the reference CDED level 1 
3.3 Land cover classes 
Claudia C. Carabajal and David J.Harding. (2005) were among 
the first to evaluate the influence of the vegetation on digital 
elevation models. Canada’s forests are vast-nearly 50% of the 
total landmass of the country (Natural Resources Canada, 2001). 
The EOSD data used are represented by many classes. All the 
classes were not used. The main classes were considered. Those 
are: Coniferous, broadleaf, mixedwood and herbs. The 
distribution of the species, dominated by coniferous-open 
justifies the adoption of such classes. We combined coniferous- 
sparse, coniferous open and coniferous dense to form the class 
of coniferous. The same idea was used to create the broadleaf 
and mixedwood classes. The herbs class was maintained. 
3.4 Error Statistics 
Statistics are computed for the differences between CDED level 
1 and SRTM model per each segmented terrain classes as for 
the study area. Several descriptive statistic measures were 
employed, among which the mean, the standard deviation for 
the both the error and the absolute value. We determined also 
the root-mean-square error (RMSE). If RMSE is normally 
distributed then, we can compute the linear error (LE) at 95% 
confidence level (Maune et al. 2001). This indicates that the 
95% of CDED level 1 points represent the true value with |error| 
< LE. LE = 1.96 * RMSE. Also from Maune et al. (2001), the 
contour interval (Cl) is related to RMSE by the relation: C.I = 
3.2898 * RMSE. After normalizing the distribution of all the 
differences by filtering, on the base of three times the standard 
deviation we, respectively computed the LE and Cl. All the 
results obtained are presented in table 1. The same statistics 
were made for the four species of the land cover (Table 2). The 
influence of the slope was removed since we considered only 
the first slope classe (Slope < 5°); we filtered the other slope 
classes. 
Statistics 
Land cover classes 
Herbs 
Broadleaf 
Coniferous 
Mixedwood 
Accuracy 
RMSE 
9,0 
12,7 
10,7 
12,0 
LE 
17,7 
24,8 
20,9 
23,6 
Cl 
29,7 
41,7 
35,0 
39,5 
No of points 
25 
4412 
33527 
3460 
Percentage 
100% 
99,10% 
99,30 % 
99,10% 
Error 
Minimum 
-13,0 
-37,0 
-35,0 
-37,0 
Maximum 
22,0 
42,0 
31,0 
39,0 
Mean 
-1,5 
2,8 
-1,8 
1,4 
S.D 
9,2 
13,0 
10,8 
12,1 
|Error| 
Mean 
7,7 
10,3 
8,2 
9,4 
Tableau 2: Differences in function of land cover in meters 
between CDED and SRTM 
Concerning statistics on ICESat’s points/profiles, the skewness 
which characterizes the degree of asymmetry of a distribution 
around its mean and the kurtosis which describes the relative 
peakedness or flatness of a distribution compare with the 
normal distribution were added to the statistics presented in 
table 2. ICESat data were filtered to remove samples that might 
have been contaminated by cloud cover or other atmospheric 
interference. Figure 5 below presents that correlation between 
CDED level 1 and ICESat. 
CDED Elevation (m) 
Figure 5: Correlation between CDED level 1 and ICESat data 
3.4.1 Altitude Error distribution 
The altitude errors (differences) between CDED level 1 and 
SRTM model are respectively in the range - 50 to 47 m, while 
the standard deviation of the altitude is 15.6 m. In the following, 
it is examined if the difference in mean error is statistically 
significant for CDED level 1 versus SRTM model. For this, the 
first task is to determine the equality of sample variances. We 
used the R software; R. Version 2.6.2 (2008-02-08). The 
observed F-Statistic equals to 0.9968 for CDED level 1 versus 
SRTM model. With a p-value of 0.5541 at 95% confidence 
interval, the true ratio of variances is not equal to 1. In this case 
it was not possible to presume the equality of variances. This 
indicates that the means errors of both DEM are significantly 
different. 
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