In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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Figure 1. Overview of the study area: a) digital surface model of the city of Vienna, b) normalized digital surface model (nDSM) of 
the study area in the Vienna Woods. In the subareas 1-4 additional analyses are done (see Sect. 3.3). 
2. STUDY AREA AND DATA 
The study area is located in the western part of Vienna (Fig. 1), 
in the so-called Vienna Woods, and covers about 200 hectares 
forest and a small part of an urban area. The study area is part 
of the UNESCO biosphere reserve Wienerwald 1 , which was 
installed by the provinces of Lower Austria and Vienna in 
2005. Since the 19 th century the Wienerwald region has been 
used as a traditional recreation area for Viennese and 
inhabitants of Lower Austria, which are living in the immediate 
vicinity. In general, the landscape of the Vienna wood is 
characterized by individual deciduous forests, which are closely 
interlinked with meadows while the forests are dominated by 
oak and hornbeam, beech at higher altitudes, and ash in the 
peak region of north-facing slopes. Within the investigated area 
the dominating tree species are red beech (.Fagus sylvatica) with 
~51%, oaks (Quercus robur, Quercus petraea) with ~23% and 
hornbeam (Carpinus betulus) with ~16%. The remaining areas 
are covered with ~6% larch (Larix decidua), 2% clearings and 
other deciduous and coniferous tree species. The forests are 
characterized by a balanced age class distribution (tree ages 
vary between 5 and 180 years) and with tree heights varying 
between 2.0 m and 45.0 m. The clearings are partly covered by 
very dense brushwood i.e. blackberry. Furthermore, some 
young forest stands covered with very dense red beech {Fagus 
sylvatica) are available. 
For the study area full-waveform airborne laser scanning (FWF- 
ALS) data was provided by the city of Vienna (MA41 
Stadtvermessung) and was acquired in the framework of a 
commercial terrain mapping project, fully covering the city of 
Vienna. The FWF-ALS data was acquired with a Riegl LMS- 
Q560 by the company Diamond Airborne Sensing 2 in 
cooperation with AREA Vermessung ZT GmbH 3 in January 
2007. The average point density is greater than 30 echoes per 
square meter. The pre-processing of the FWF-ALS data was 
done from the company AREA Vermessung ZT GmbH using 
the Riegl software packages 4 . For the current study the geo- 
referenced 3D echo points and the determined attributes for 
each echo, i.e. the echo width and the amplitude, serve as input 
for the following analyses. 
1 http://www.biosphaerenpark-wienerwald.org 
2 http://www.diamond-air.at/airbomesensing.html 
3 http://www.area-vermessung.at/ 
4 http://www.riegl.com/nc/products/airbome-scanning/ 
The digital terrain model (DTM) was calculated using the 
SCOP++ (2010) software. For the determination of the digital 
surface model (DSM) the approach described in Hollaus et al. 
(2010) was applied using the OPALS (2010) software. Finally, 
the normalized digital surface model (nDSM) was calculated by 
subtracting the DTM from the DSM (cf. Fig. lb). The derived 
topographic models (DTM, DSM, nDSM) have a spatial 
resolution of 0.5 m. The DTM is used for normalizing the 3D 
echo points (dz) and therefore, for the selection of terrain and 
near-terrain echoes (cf. Sect. 3.1 and 3.2). The nDSM is used 
for visualization purposes only. 
3. TERRAIN ROUGHNESS PARAMETERS FROM 
FWF-ALS DATA 
Looking at roughness from the ALS sensing technique point of 
view (Fig. 2), the spatial scale of observation plays a crucial 
role. Roughness with a spatial scale of up to a few decimeters 
cannot be determined directly by using the ALS range 
measurements (i.e. XYZ of point cloud) because of the range 
accuracy (Kraus, 2007), which generally lies far above the 
given laser wavelength of a few micrometers. Hence, micro 
structures can only be determined indirectly, for example, by 
using the strength of reflection of a laser echo (i.e. amplitude), 
which is correlated with target reflectivity but also surface 
roughness (Wagner et al., 2004). 
Macro structure > d d> Meso structure » X X> Mici o structure 
different roof shapes different elevated small roughness of the 
objects surface and materials 
d... footprint diameter [dm]; X ... wavelength [pm] 
Figure 2. Scales of roughness in terms of the ALS sensing 
technique (modified from Jutzi and Stilla, 2005).