In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
38 
Figure 3. Terrain roughness - TR I. 
Looking at the TR raster layers in more detail (details in 
Figures 3 and 4) local fine-scale roughness variations become 
visible. The spatial distribution of available information is very 
similar. The slightly higher data density in TR II can be an 
indicator of (1) vegetation being very dense in this story thus 
again preventing the laser beam to reach lower levels or (2) 
higher vegetation with branches starting somewhere around eye 
level, but not having any undergrowth beneath. 
4. VERTICAL ROUGHNESS MAPPING - 
INTEGRATION OF MULTI STORY BACKSCATTER 
INFORMATION 
After computing the ALS point cloud based roughness raster 
layers as described above they were jointly analyzed and 
combined, whereas a novel roughness classification scheme was 
developed, further referred to as ‘vertical roughness'. This 
novel roughness mapping concept incorporates information 
from various vegetation height layers using the capability of 
full waveform ALS, i.e. recording the entire backscatter 
spectrum from treetop to ground. It thus not only gives an 
indication of surface roughness patterns (limited to a very small 
height threshold above ground), but also includes information 
on the variance of brushwood (such as bushes and shrubs) and 
understory vegetation (up to 3.0 m). Results of the roughness 
classification were finally validated with in situ data from a 
field survey conducted in April 2009. The following paragraphs 
are dedicated to advanced classification and analysis of the 
ALS-derived roughness raster products. First, SR and TR layers 
were jointly analyzed with regard to identification of significant 
spatial patterns in terms of intensity and accumulation of 
roughness echoes. Adding yet another dimension to this 
integrated classification process - absolute vegetation height as 
illustrated by a nDSM - rounds off a novel approach of 
mapping roughness in wooded areas on multiple vertical levels, 
from now on called ‘vertical roughness mapping (VRM)'. 
According to the basic objective of distinguishing smooth and 
rough surfaces, the SR raster was binary recoded with the 
threshold defined at SR = 0.05 m plus one additional ‘no data’ 
category. Grid cells featuring SR values larger than 0.05 were 
thus considered rough, while all values below that threshold 
were considered smooth. 
Figure 4. Terrain roughness - TR II. 
Regarding the structural undergrowth information inherent in 
the two TR raster layers a slightly different, but yet binary 
classification approach was chosen. One class of pixels depicts 
areas where echoes are recorded in both the lower vegetation 
level TR I (very low brushwood or undergrowth up to 1.0 m) 
and the level of understory vegetation up to 3.0 m TR II. The 
second category includes regions where echoes were just 
recorded at the level of TR II, but no data exist on the lower 
level of TR I. Again an additional class for ‘no data’ cells was 
appended. 
The combination of these reclassified SR and TR products ‘x-y’ 
(x ... TR rec i, y ... SR rec i) with each layer featuring three value 
facets (1, 2, no data) resulted in a set of nine possible new 
classes (3*3 categories) describing different multi-level 
roughness characteristics. Most frequent classes are the 
categories with x=0 (0-0, 0-1, 0-2), i.e. having no TR data 
records, whereas 0-1 particularly stands out. This class covering 
about one third of the study area (33.4%) delineates areas with 
smooth surface and no recorded echoes in both levels of 
understory vegetation (TR I 0.2 m to 1.0 m, and TR II 0.2 m to 
3.0 m). 
In the previous steps information on the vertical distribution of 
recorded echoes within a range of 0.2 m to 3.0 m above ground 
was considered for VRM. In order to get an overall picture of 
the vertical vegetation structure another dimension was added 
by integrating the nDSM as third input variable, i.e. absolute 
height information classified in 4 story layers. The first 
category (‘0-x-y’) is defined as ‘vegetation up to 3.0 m’ 
covering about 9% of the total area. As this class boundary 
coincides with the upper boundary of TR II, the nDSM 
integration does not expand the vertical roughness information 
content in these areas. The biggest part of the test site’s wooded 
area (65.8%) is covered by trees which are between 10 m and 
25 m high (category ‘2-x-y’). Integrating an additional 
information layer with 4 possible values to the concept results 
in 36 (3 * 3 * 4) classes eventually describing the full vertical 
vegetation structure. Figure 5 shows the new extended 
classification scheme - ‘vertical roughness mapping extended 
(VRM*)' - applied to a detail of the study area. The aerial 
image is given for comparison in order to provide an impression 
of the heterogeneous forest structure, which is already visible 
without even knowing how it looks like beneath the tree crown.