zijing 2008 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi XXXVII. Part B4. Beijing 2008 
363 
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Figure 4. Problems with wharfs or undeep water: many points 
are classified as terrain 
The control of the GRASS DSM was performed with a 
comparison with Sardinia DSM. The areas in which the control 
was performed are always the same already used in the previous 
analysis. 
To create a DSM with GRASS it is sufficient to interpolate the 
points classified as first pulses. In this case we used bicubic 
splines with resolution of 4 m and we choose a resolution of the 
DSM always equal to 2 m. 
The differences were calculated with the procedure already used 
for the DTM and are shown in table 5. 
area 
mean 
a 
•< 1 
<N 
v 
VI 
CO 
V 
VI 
<N 
•>3 
B 
0,03 
0,84 
87,70% 
8,17% 
2,66% 
1,47% 
C 
0,04 
0,52 
94,91% 
3,63% 
0,96% 
0,50% 
Table 5. Distribution of the DSM error (units in m) 
The mean of the difference is almost zero but the standard 
deviation has a larger value than in the DTM analysis. Anyway 
the error is at least at 87 % lesser than 1 m and is due mainly to 
the usage of two different interpolation methods. 
4. DTM/DSM ABSOLUTE CONTROL 
4.1 Dataset and procedure 
The controls previously shown were performed with the raw 
LiDAR data, starting from the filtering up to the interpolation 
on a regular grid. They were based on the comparison with 
another DTM/DSM. This analysis demonstrated a good 
correspondence between GRASS and Sardinia results, but it is 
not sufficient to check the absolute precision of the 
implemented algorithm. 
The goal of this paragraph is to show the results related to the 
difference between gridded GRASS products and 218 points 
measured with a GPS. The precision of the RTK GPS survey 
was ±0.03 m, while raw LiDAR data have a precision larger 
than ±0.15 m. This implicates that GPS coordinates become 
useful to test the absolute accuracy of GRASS DTM and DSM. 
The procedure to check GPS and GRASS data was based on 
raster differences. Also in this case a GPS raster file was created. 
After an analysis on the distances between each GPS point we 
choose a resolution of the GPS raster map equal to 0.5 m, which 
avoided overlapping between the GPS raster points. The map is 
composed of only 218 full cells, and the remaining part is 
completely empty. 
The next step was to calculate the differences between the GPS 
maps and the DTMs. The control was performed in 3 areas (2 
areas have been already used during the relative control, in 
addition we choose another area). To complete the tests also the 
original Sardinia’s DTM was used in this comparison. The 
results are shown in table 6. 
num 
mean (m) 
a(m) 
max (m) 
min (m) 
GPS-GRASS 
area 16 
88 
0,21 
0,17 
0,47 
-0,48 
area 15 
72 
-0,01 
0,14 
0,26 
-0,45 
area 14 
58 
0,36 
0,16 
0,73 
-0,19 
GPS-Sardinia 
area 16 
88 
0,24 
0,08 
0,50 
0,11 
area 15 
72 
0,21 
0,07 
0,39 
0,08 
area 14 
58 
0,27 
0,14 
0,57 
-0,38 
Table 6. Difference between GPS measures and grid products 
The results using GRASS and Sardinia DTMs are close enough 
but Sardinia DTM always presents a lower discrepancy. In any 
case, considering the spatial resolution of 2 meter and the 
complicated morphology of the chosen areas both results can be 
accepted (we remember that Sardinia’s procedure is semi 
automatic). 
The last control regards the DSM precision. Also in this case the 
control was based on the differences, but the points were 
measured with a Total Station to obtain information also about 
the point that lie on roofs (figure 5). 
The main occurred problem was the position of the point used 
to perform the control, because they are generally located next 
to the building edge, where the DSM has a significant variation. 
The roofs in the areas have typically pitched faces, and it is 
obvious to forecast larger discrepancies than in the DTM 
control, because an optimal and representative control must be 
carried out in flat areas, where the spatial resolution of 2 m 
becomes irrelevant.