The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
urban areas. In the field of SV research, on one level this opens 
up new possibilities. Even though SV tends to relate to personal 
and individual characteristics, physical indicators and spatial 
and contextual relationships that relate to the “physical and 
social world” of the individual (Clark et al., 1998) have 
previously been identified (Wu et al., 2002; Rashed & Weeks, 
2003) and may well be observable in RS data. In Figure 2, 
indicators for SV assessment on two scales, the 
individual/household and the neighbourhood scale, are 
presented with their best-suited assessment methods. 
Besides a generally accepted definition a more efficient and 
comprehensive approach for the assessment of SV is needed. 
Data availability and low efficiency have been pointed out as 
two of the most important constraints in current SV assessment 
(Birkmann, 2005). 
4. METHODOLOGY: OBJECT-ORIENTED ANALYSIS 
OF REMOTE SENSING DATA 
So far, studies on vulnerability assessment using satellite data 
have mainly been focusing on the assessment of physical 
vulnerability (Müller et al., 2006). But by applying object- 
oriented image analysis, a wide range of additional, contextual 
information that is relevant for SV assessment can be extracted 
from remote sensing images. Very high resolution satellite data 
(0.6 m ground resolution from Quickbird satellite) have been 
used in combination with various digital data sets to extract 
important semantic information, e.g. the position of a building 
in relation to the hazard zone. This, for example, is a critical 
feature as the position of each image object in its natural and 
man-made environment partly determines its SV (Rashed & 
Weeks, 2003; Clark et al., 1998). 
Given that SV is more of a non-tangible concept it has to be 
evaluated which criteria for its assessment potentially have a 
spatial expression in an image (Table 2). The selection of 
indicators for this study was based on the list of generally 
accepted criteria according to Cutter et al. (2003, Ebert et al., 
2007). Selected according to assessment capabilities with 
remote sensing data and relevance for this study, four main 
indicators (Table 2) remained. Meaningful proxy variables 
(parent proxy, second column Table 2) have been found to 
describe the content of the indicators. These in turn had to be 
translated into proxy variables that can be directly delineated 
from remote sensing data and other available information (GIS 
data, city maps) using object-oriented analysis (supporting 
proxy, third column Table 2). 
Abundance of 
lifelines 
Number of 
lifelines 
Number of lifelines 
(1) 
Distance to 
lifelines 
Distance to 
lifelines 
Distance measures 
13} 
Table 2. Physical proxies identified for the original SV 
indicators. Numbers in parentheses indicate the actual number 
of proxy variables for each parent proxy. 
A land use/land cover classification was performed in two 
modes: with and without the usage of an additional normalized 
Digital Surface Model (nDSM) that was available for a part of 
the study area. In both modes, spectral as well as spatial and 
contextual information have been used for the class description. 
The classification accuracy for the classification with and 
without the nDSM was 89.1 % and 84.3 %, respectively. Based 
on the classification results, and by using additional GIS data 
and city maps, the supporting proxy variables were delineated. 
The proportion of built-up and vegetated area was calculated for 
each neighbourhood, using spatial metrics (Herold et al., 2003). 
Similarly, the percentage of paved roads compared to the total 
road network per neighborhood was calculated. Seven different 
roof types and their neighbourhood proportion were also 
classified from the Quickbird image, which can give 
information about the socioeconomic status of the residents. 
However, field data would be necessary to associate actual roof 
materials with the roof types classified, as Quickbird’s spectral 
characteristics are insufficient for such determination. The 
available service infrastructure was digitized from city maps 
and quantified for each neighbourhood. Image texture measures 
were used to describe the distribution of grey values in an image 
and thus to characterize the heterogenic structures of a city 
(Tuceryan & Jain, 1998). To describe the topographic location, 
the slope position was calculated from a digital terrain model 
(DTM, 1.5 m resolution), to which each image object was then 
associated. Twelve slope classes in 5° intervals were delineated. 
All buildings in a hazard zone (flood and landslide) were further 
masked out, and the percentage compared to all buildings in the 
neighbourhood calculated. Building heights were further 
delineated from the lidar data set for a smaller part of the study 
area, and used to characterize commercial development. Finally, 
three possible distances to lifelines (less than 100 m, 100 to 250 
m, more than 250 m) were defined that refer to the position of 
each segment in the image. A total of 47 supporting proxy 
variables were thus delineated (Table 2). 
Original 
indicator 
Socio-economic 
status 
Commercial and 
industrial 
development 
Parent proxy 
Supporting proxy 
Settlement 
type 
Topographic 
location 
Commercial 
development 
Proportion of built- 
up and vegetated 
area (4 proxies); 
Road conditions (1); 
Roof type (7); 
Texture (10) 
Slope position (12); 
Proportion of 
buildings in hazard 
zone (2) 
Building heights (7) 
5. RESULTS 
The goal of the evaluation was to investigate if the proxy 
variables can be used to extract information from the satellite 
data that are relevant for the assessment of SV. We thus tested 
for a relation between the remote sensing derivatives (proxy 
variables) and a reference SV index (SVI, modified from Haki 
et al., 2004) based on census data from 2000 census in 
Tegucigalpa. The set consist of seven variables (gender, literacy, 
wall material, roof material, water availability, waste disposal, 
building use) to calculate the SV for each neighbourhood. The 
resulting values for each neighbourhood are shown in Figure 3. 
We used a stepwise regression model to determine which of the 
47 proxy variables (explanatory variables) can best explain the 
variation of the vulnerability score V (dependent variable) (Jain, 
2005). The main observations were the following: