In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
Figure 5. Vertical roughness map and classification scheme for 
a detail of the test site, and aerial image for comparison (D). 
In fact five classes out of the 36 categories stand out each 
featuring more than 5% and in total covering about 60% of the 
test site. 22 of the remaining classes just account for less than 
2% each. The by far most dominant class (25%) is ‘2-0-1’ (light 
green in Figure 5) featuring an overall height between 10 m and 
25 m, smooth surface and no echo records regarding 
undergrowth. In general nearly 65% of the total forest area 
shows no backscatter records in the mid-vegetation level (i.e. 
the ‘h-0-y’ category group). In case some kind of undergrowth 
is present, most of the times it spans the whole vertical range 
between 0.2 m and 3.0 m above ground, i.e. echoes recorded in 
both TR I and TRII as well as ‘rough’ surface conditions (‘h-1- 
2’: 18%). When looking at the ‘h-l-y’ type, thus ignoring 
surface variation, these regions even cover 27.5% of the total 
study area. Classes with TR information exclusively recorded in 
the higher level of understory vegetation between 1.0 m and 
3.0 m (i.e. the ‘h-2-y’ category) occur just very sparsely 
covering a total of less than 10% of the study area. Pixels 
assigned to these classes can mainly be attributed to large trees 
with branches reaching down to the 3.0 m range but not all the 
way down to the ground. 
Extensive photo documentation was an essential part of the data 
collection allowing capturing a certain neighborhood around 
fixed reference point locations. A total of 24 points was 
collected in the course of the field survey whereas the focus 
was on getting a representative point set featuring different 
types of vegetation including scrubs and brushwood. 
For this paper two selected reference points of varying 
characteristics will be presented in detail serving as examples 
for cross-validating ALS derived roughness and reality 
conditions. Figure 6 shows the vertical roughness map (VRM C ) 
for the examined area with the aerial image included for 
comparison and orientation reasons. For each of the two 
reference points (no. 463 and 464) a vertical-bar graph is 
presented illustrating the VRM e pattern (class ratios) within a 
10 m neighborhood (marked with red circles in the map). To 
illustrate reality conditions pictures from the field survey are 
shown in the bottom part of the figure (463: PI, 464: P2). 
First, spatial roughness patterns as illustrated in the raster map 
and class ratio numbers are analyzed. For reference point 463 a 
good portion of its immediate neighborhood (63%) is classified 
as ‘1-1-2’ (light yellow in Figure 6), i.e. overall rather low 
vegetation height <10 m, recorded echoes in both terrain layers 
(TR I and TRII), along with ‘rough surface’. With respect to 
the entire study area this is a very untypical picture with ‘1-1-2’ 
just covering less than 3% in total. These ALS-based ratio 
values shown in the vertical-bar graph are confirmed by looking 
at the collected reference data, with photo PI showing dense 
low-level deciduous vegetation with branches and leaves 
throughout the vertical range. Also in an additionally available 
reference data set (provided by the Research and Training 
Centre for Forests, Natural Hazards and Landscape, BFW) this 
region is qualitatively described as ‘young and very dense 
deciduous forest including undergrowth’. 21% are classified as 
‘1-2-y’ (light green) while the remaining 16% are assigned to 
‘0-2-y’ (orange), i.e. no recorded echoes in TR I at slightly 
varying overall tree height. As surface information is available 
anyhow (either smooth or rough) it is unlikely that too dense 
top-level vegetation prevented the laser beam from ‘seeing’ the 
lower level. As during the field campaign indeed some parts of 
this specific forest patch were observed not featuring any low- 
level brushwood the classified VRM e information can be 
expected to be correct and significant. 
Reference point 464 shows a completely different picture. 
Photo P2 in Figure 6 displays mixed and rather loose vegetation 
including e.g. large coniferous trees, small broadleaf bushes and 
leaf-covered surface. This is confirmed and even emphasized by 
the ALS based VRM e . Looking at the class ratio values shown 
in the vertical-bar graph and at the raster layer no predominant 
category can be detected, but rather a heterogeneous mix of 
various roughness classes in the immediate neighborhood of the 
reference point. From bare soil (‘0-0-1’) to the largest trees (3- 
x-y) the entire range of roughness categories is present. 
5. VALIDATION 
For validating the results of the ALS based vertical roughness 
mapping in situ reference data was collected. The field survey 
was done on April 16, 2009 taking Hafibach (village in the 
southwest of the test site, see Figure 1) as starting point. 
Locations of reference points were stored using a Garmin eTrex 
GPS handheld model. 
Photo documentation not necessarily delivers representative 
results, also because of directivity. However, during data 
collection attention was paid to that issue. As the field survey 
was carried out about two years after the ALS data had been 
recorded, data mismatches can be due to that temporal 
variation. Summing it up results are very encouraging and it 
seems that advanced vertical roughness mapping is possible at a 
certain spatial level of detail based on ALS information.