The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
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(i) A high proportion of built-up areas and of buildings
on gentle slopes corresponds to low SV.
(ii) A high proportion of buildings on medium slopes,
buildings exposed to landslide hazard, and the
abundance of two specific roof types, corresponds to
high SV values.
(iii) A high amount of infrastructure corresponds to high
SV, although at low significance. This is somewhat
surprising and most likely this variable provides a
correction on the other variables, compensating some
of the variation in the variables with a higher
significance, such as buildings at landslide hazard. It
may also be the case that the amount of service
infrastructure and lifelines is higher around buildings
exposed to landslide hazard because of planning
considerations by the city developers in the past.
(iv) No significant influence was found for the selected
texture measures, the amount of buildings at flood
risk and the distance to infrastructure (e.g. lifelines).
Legend
Pansharpened QB intage
0.6 m ree., from 12/2000
Legend
Social Vulnerabitrty
Score
ШШ frie - 1.x
ШШ 1-31 - 1.41
Figure 3. Scores from the social vulnerability index per
neighborhood (bottom) with the pansharpened Quickbird image
as reference (top).
6. VALIDATION, CONCLUSION, DISCUSSION AND
OUTLOOK
on a larger scale (e.g. using data from house-to-house surveys),
the proxy variables could have been delineated on a smaller and
more detailed scale as well. To validate the results obtained in
this study and to quantify their contribution to risk assessment
better, the validation could be improved by the comparison with
results from detailed traditional methods, e.g. from house-to-
house surveys in the same area. Unfortunately, they were not
available for this research.
Nevertheless, the potential of the method becomes obvious:
relevant information, consisting to a large proportion of
valuable contextual information, can be delineated from remote
sensing data in combination with other digital data sets (Table
1). The information that can be extracted ranges from building-
specific information (location, roof material, etc.) to
neighbourhood information (neighbourhood composition, living
standards, etc.).
In terms of transferability, the delineated proxies can be adapted
and evaluated to fit different places and different hazards. The
main precondition of this approach is the initial understanding
of SV in a particular place, mainly based on the specific local
conditions and the understanding of those factors. For example,
if it is not the case that poor people live in areas with
unfavourable terrain, the consequent evaluation of this proxy
variable can be changed accordingly or combined with other
information such as neighbourhood composition.
For the assessment of physical vulnerability, it is important to
refer to a certain natural hazard (e.g. flood, earthquake,
landslide, storm, etc.) as physical characteristics of buildings
imply different degrees of vulnerability to different hazards. As
discussed in the literature (e.g. Cobum et al., 1994) indicators
for SV do not necessarily relate to a certain hazard, but do
express an intrinsic incapability and lack of resilience to cope
with natural hazards in a more general way. This enables the
application of this method for SV assessment for hazard-
specific studies as well as for multi-hazard studies.
Some indicators, e.g. construction material, are used for both
the assessment of physical and social vulnerability. On the one
hand, it can be used to express the resilience or the lack thereof
to withstand the power of a hazardous event (physical
vulnerability). On the other hand, it is being used to describe
indirectly the social standard and wealth of a household and
thereby their vulnerability and coping capacities in case of a
disastrous event (social vulnerability).
Another advantage is that medium scale approaches such as
ours consider spatial relations of objects and their environment,
and thereby provide a spatial framework in which information
can be interpreted, whereas individual, very small scale
information lacks this context and topology information. Thus,
a combination of approaches at different levels is needed.
In terms of data interchangeability, previous studies showed that
the use of lidar data for the extraction of building heights can be
substituted by the use of stereo- or orthophotos (Fraser et al.,
2002).
Eight out of 47 variables explained almost 60 % of the variance
of the SVI. While this is from the statistic perspective a
satisfactory result, it has to be noted that the applied SVI was
calculated based on a small amount of variables from census
data not ideally suited for SV assessment. The data only include
a limited amount of indicators relevant for SV assessment, and
also allow validation only at the level of a census unit
(neighbourhood). If the validation could have been performed
In this paper we present a new method for SV assessment based
on contextual analysis of remote sensing and GIS data. An
approach based on physical proxy variables and spatial metrics
that were derived from high resolution optical and laser
scanning data, in combination with elevation information and
existing hazard data. Object-oriented image analysis was
applied for the definition and estimation of proxy variables that
describe the living environment and living standards, such as