In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
University 
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Figure 1. Study area located in the city centre of Vienna, 
Austria. Diversity in tree species and structure are shown in 
detailed photographs (B1-B3) 
The full-waveform airborne LiDAR data were obtained in the 
framework of the city-wide laser scanning campaign in the 
winter season 2006/2007 under leaf-off conditions. Data 
acquisition was performed with a Riegl LMS-Q560 system, 
which uses near-infrared (1500 nm) laser pulses with a pulse 
width of 4 ns. Further settings are a pulse repetition frequency 
of 200 kHz, scan angle range of ±22.5° and a beam divergence 
of 0.5 mrad. Full-waveform recording can be done with 1 ns 
temporal resolution. Decomposition of the waveforms was 
performed by using the Riegl software RiANALYZE. The 
number of echoes is not limited by the full-waveform recording 
sensor system, and therefore, the number of detected echoes per 
laser shot can be higher as with traditional discrete echo 
recording, particularly in high vegetation. The average echo 
density of the LiDAR dataset covering the study area is 50 
echoes per m 2 with about 6.8% first echoes, 1% intermediate 
echoes (e.g. 2 nd , 3 rd echo), 6.8% last and 85.4% single echoes 
(i.e. only one reflection per shot). A Digital Terrain Model 
(DTM) with 0.5 m resolution was produced using the software 
SCOP++ (SCOP++, 2010). For evaluation and comparison a 
reference dataset including single tree positions of alley trees as 
well as the official land cover map of Vienna are available: 
Mehrzweckkarte (MZK), Flachenmehrzweckkarte 1 (FMZK). 
3. METHODS 
The very high point density of the LiDAR dataset, i.e. about 
50-10 6 echoes per km 2 , does not allow a pure point cloud based 
vegetation detection procedure for large areas (400 km 2 for 
entire Vienna) in an operational manner. Therefore, a combined 
object-based image and point cloud approach is introduced, 
taking advantage of both, fast raster processing and detailed 
("interpolation-free") point cloud based information extraction 
including full-waveform laser point attributes. Furthermore, the 
high point density of the point cloud allows the derivation of 
high-resolution derivatives (e.g. DTM with 0.5 m cell size), 
providing a sufficient planimetrie accuracy of the final results. 
1 http://www.wien.gv.at/stadtentwicklung/stadtvenriessung/geod 
aten/fmzk/produkt.html (last accessed 31.5.2010) 
The developed workflow is implemented in the GRASS GIS 
environment (GRASS Development Team, 2010) and 
comprises the steps shown in Fig. 2. 
Full-waveform Point Cloud 
X,Y,Z, amplitude, echo width, echo n 
total no. echoes 
|~t g™ I 
Building Polygons I—I 
Data Preparation 
?.g. calculation of DSM, nDSM 
Segmentation 
raster-based 
Segment Feature Calculation 
geometric and topological features 
.^mstcr^vcctor^n^omt^cloud^bascd^ 
Existing Tree Positions 
Figure 2. Workflow of urban vegetation detection using raster 
and full-waveform point cloud input data 
3.1 Data preparation 
The data preparation of the input point cloud ASCII file 
includes a classification of the laser points into first, last, single 
and intermediate echoes. Each laser point has the attributes 
echo number (EN) and total number (TN) of echoes of the 
corresponding laser shot, with e.g. single echoes: EN=TN=1; 
first echoes: EN=1 && TN>1; intermediate echoes: EN>1 && 
EN!=TN. Using the maximum height per 0.5 m cell a Digital 
Surface Model (DSM) is derived and finally normalized with 
the input DTM, in order to get a normalized DSM (nDSM). 
Empty cells get a normalized height of 0 m. In urban areas 
generally complete and high quality cadastral data layers are 
available, which can be used as mask for the subsequent steps. 
In our study the buildings can be extracted from the official 
land cover map and could be used optionally to exclude these 
areas from further investigation. In our study the building 
polygons are used for evaluation and thus are not considered as 
input. 
3.2 Segmentation 
An object-based raster analysis using an edge-based 
segmentation procedure is applied to the high-resolution nDSM. 
The procedure has already been tested in densely forested areas 
where no buildings are present (Hofle et al., 2008) and is 
transferred to the densely built-up urban area of Vienna in this 
study. The segmentation aims at delineating convex objects in 
the nDSM by finding concave edges in between the objects. 
Additionally, constraints on normalized height (nDSM>1.0m) 
and the occurrence of multiple reflections are included. 
Multiple reflections are parametrized by an echo ratio (ER) 
defined as (Eq. 1): 
ER [ /0] — (/ifirst ^intermediate) / (^last E ^single) 100 0) 
where n is the respective number of echoes per cell. If no echo 
is within a cell, ER is set to zero and if no last and single echoes 
are found within a cell, the echo ratio is set to 100, exhibiting a 
high vertical extension and transparency of the object. High ER 
values are assumed to indicate vegetation, which tends to have 
a high number of first and intermediate echoes compared to 
other elevated objects in urban areas (e.g. buildings; Fig. 3b).