In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
In: Wagr 
Land cover class 
Range 
Skid factor 
1 
Screes and boulders 
>30 cm 
1.2- 1.3 
2 
Shrubs or mountain pines 
Mounds w. veg. cover 
Cattle treading 
Screes 
>1 m 
> 50 cm 
10 - 30 cm 
1.6- 1.8 
3 
Grass veg. inch low bushes 
Fine debris mixed with veg. 
Small mounds w. veg. cover 
Grass veg. inch superficial 
cattle treading 
< 1 m 
< 10 cm 
< 50 cm 
2.0 - 2.4 
4 
Compact grassland 
Solid rock 
Fine debris mixed with soil 
Moist sinks 
2.6-3.2 
Table 1. Skid factors assigned to land cover types featuring 
varying roughness (Margreth, 2007). 
The current standard way of assessing surface and/or terrain 
roughness is using empirical methods in the field. Taking the 
macro-level as example, terrain features are described 
approximately via wavelength and amplitude of sinusoids. 
Roughness assessment on meso- and micro-level can be carried 
out by fitting ductile slats to the surface. All these methods 
require on-site inspections which gets extremely time- 
consuming and costly for large-area assessments. Remote 
sensing offers the advantage of an area-wide standardized 
survey and is expected to deliver roughness assessments in 
comparable accuracy. 
In this paper we describe an approach to classify forested areas 
based on their vertical vegetation structure using ALS data. We 
see roughness as a multi-scale level concept, i.e. ranging from 
fine-scale soil characteristics to description of understory and 
lower tree level. Results of our ‘vertical roughness mapping’ 
concept can be valuable input for forest monitoring in particular 
with regard to natural hazard modeling. 
2. STUDY AREA AND DATA 
The study area covering approximately 10 square kilometers is 
located in the ‘Bucklige Welt’, a hilly region in the south 
eastern part of Lower Austria (about 70 km south of the Vienna 
basin) also known as ‘land of the 1,000 hills’ (see figure 1). 
Widely dominated by forest of varying characteristics (i.e. 
deciduous, coniferous, and mixed forest) this is a typical rural 
area interrupted by a few small settlements (e.g. HaBbach, 
Kirchau, Kulm) and patches of agricultural land. In line with 
the overall characteristics of the ‘Bucklige Welt’ region the 
study site which belongs to the municipal area of Warth 
features hilly terrain conditions with maximal 300 meters 
elevation difference. 
Employing a full waveform Airborne Laser Scanning (FWF- 
ALS) system ALS data were acquired in the framework of a 
commercial terrain mapping project covering the entire Federal 
State of Lower Austria (acquisition period: spring 2007). In 
spring favorable leaf-off conditions without snow cover could 
be guaranteed. For the presented research project 3D point 
clouds organized in tiles and consisting of XYZ coordinate 
triples (ASCII XYZ format) were delivered. Originally ALS- 
inherent information about scan geometry and radiometric 
information was not available for further analysis. 
Figure 1. Study area ‘Bucklige Welt’, Lower Austria. 
3. ALS BASED ROUGHNESS DESCRIPTION 
This paper concentrates on products based on parameters that 
can be derived directly from the ALS point cloud. Only by 
using the 3D point cloud maximum information content is 
guaranteed, while preserving the highest data density and not 
introducing any biasing decisions on suitable target raster 
resolution, filter or aggregation strategies (Hbfle, 2007). On the 
end-user side however, it is much more convenient and 
applicable to deal with pre-processed ‘roughness images’, i.e. 
featuring substantially reduced amount of data and simple raster 
data structure, which can be dealt with easily in standard GIS 
and remote sensing software packages. Computation of 
additional point cloud attributes and subsequent generalized 
raster layers requires a sophisticated software implementation, 
including both the mathematical definitions and intelligent 
management of the large amount of data which arises when 
working with high density laser point data. 
In the following paragraphs different roughness parameters 
calculated on the basis of the initial ALS point cloud are 
described and resulting raster layer products are illustrated. In 
the definition of surface roughness in this context all ALS 
terrain points within a 0.2 m range to the ground are considered. 
The terrain roughness concept on the other hand just comprises 
objects (i.e. point clusters) close but explicitly above the terrain 
(>0.2 m), whereas two different vegetation story layers were 
analyzed for this paper: (1) very low brushwood or undergrowth 
from 0.2 m to 1.0 m such as bushes and shrubs, and (2) 
understory vegetation from 0.2 m to 3.0 m, e.g. being indicative 
for different tree types. 
As the objective of the presented approach was to ‘look through 
the forest canopy’ and map the entire vertical vegetation 
structure (i.e. ‘roughness’ on various levels inside the woods) 
non-forested regions were masked out using a previously 
derived forest mask. This mask had been produced 
implementing an integrated analysis approach considering 
aerial imagery and ALS data (i.e. Object-based Image Analysis, 
OBIA). 
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