In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
VERTICAL ROUGHNESS MAPPING - 
ALS BASED CLASSIFICATION OF THE VERTICAL VEGETATION STRUCTURE 
IN FORESTED AREAS 
C. Aubrecht 3 ’ *, B. Hofle b , M. Hollaus c , M. Kostl a , K. Steinnocher a , W. Wagner 0 
a AIT Austrian Institute of Technology GmbH, Donau-City-Str. 1, A-1220 Vienna, Austria - 
(christoph.aubrecht, mario.koestl, klaus.steinnocher)@ait.ac.at 
b Department of Geography, University of Heidelberg, Berliner StraBe 48, D-69120 Heidelberg, Germany - 
hoefle@uni-heidelberg.de 
c Inst, of Photogrammetry & Remote Sensing, Vienna U. of Technology, GuBhausstr. 27-29, A-1040 Vienna, Austria - 
(mh, ww)@ipf.tuwien.ac.at 
KEY WORDS: Forestry, Hazards, Mapping, Vegetation, Classification, Laser scanning 
ABSTRACT: 
In this paper we describe an approach to classify forested areas based on their vertical vegetation structure using Airborne Laser 
Scanning (ALS) data. Surface and terrain roughness are essential input parameters for modeling of natural hazards such as 
avalanches and floods whereas it is basically assumed that flow velocities decrease with increasing roughness. Seeing roughness as a 
multi-scale level concept (i.e. ranging from fine-scale soil characteristics to description of understory and lower tree level) various 
roughness raster products were derived from the original ALS point cloud considering specified point-distance neighborhood 
operators and plane fitting residuals. Aiming at simplifying the data structure for use in a standard GIS environment and providing 
new options for ALS data classification these raster layers describe different vertical ranges of the understory and ground vegetation 
(up to 3 m from ground level) in terms of overall roughness or smoothness. In a predefined 3D neighborhood the standard deviation 
of the detrended z-coordinates of all ALS echoes in the corresponding vertical range was computed. Output grid cell size is 1 m in 
order to provide consistency for further integration of high-resolution optical imagery. The roughness layers were then jointly 
analyzed together with a likewise ALS-based normalized Digital Surface Model (nDSM) showing the height of objects (i.e. trees) 
above ground. This approach, in the following described as ‘vertical roughness mapping’, enables classification of forested areas in 
patches of different vegetation structure (e.g. varying soil roughness, understory, density of natural cover). For validation purposes 
in situ reference data were collected and cross-checked with the classification results, positively confirming the general feasibility of 
the proposed vertical roughness mapping concept. Results can be valuable input for forest mapping and monitoring in particular with 
regard to natural hazard modeling (e.g. floods, avalanches). 
1. INTRODUCTION 
Surface and terrain roughness is an essential parameter for 
assessment and modeling of natural hazards such as avalanches 
and floods (Margreth & Funk 1999, Werner et al. 2005, 
Schumann et al. 2007). Basically it can be assumed that flow 
velocities decrease with increasing roughness (Gomez & 
Nearing 2005). Roughness can be seen in various scale levels, 
ranging from fine-scale soil characteristics to terrain features. 
On the micro-level soil roughness is described in a range of 
millimeters to centimeters. Relevant parameters in that context 
are land cover types such as herbaceous and grass vegetation. 
Relevant meso-level roughness features include objects and 
vegetation in a range of decimeters to meters, such as shrubs 
and boulders. The macro-level is determined by topography and 
terrain features, whereas the scale ranges from one to hundred 
meters (Jutzi & Stilla 2005). 
In state-of-the-art avalanche modeling approaches empirically 
developed roughness schemes based on a set of varying land 
cover types are implemented (McClung 2001, Ghinoi & Chung 
2005). Such land cover classification can e.g. consist of (1) 
screes and boulders, (2) shrubs or mountain pines, (3) 
herbaceous and grass vegetation including low bushes, and (4) 
compact grassland or solid rock. Depending on the exposition 
various skid factors are derived from these surface types (see 
Table 1). Surface roughness is relevant for glide avalanches on 
micro-level as well as for snow slabs on meso- and macro-level. 
The estimated skid factors are introduced in snow gliding and 
snow pressure modeling (Holler et al. 2009). In the field of 
hydrology surface roughness is introduced in runoff models for 
detecting superficial flow velocities (Lavee et al. 1995, Rai et 
al. 2010). Assessment of roughness is thereby based on a coarse 
surface and vegetation classification. Markart et al. (2004) 
identified six classes ranging from very flat to very rough. 
Different types of vegetation can span several roughness 
classes. In particular this applies to forest locations, where 
surface roughness is depending on specific low-vegetation 
cover. Accordingly further parameters are needed for 
classification. E.g. for virgin soils the dominance of migrating 
plants is relevant. For grassland land use strongly affects 
roughness characteristics (pasturing, ski slopes, hay meadows). 
In moist locations the moss rate is crucial, while for areas with 
bushes particularly the type of plant cover is relevant. 
Corresponding author.