Assessing Building Vulnerability to Tsunami Hazard Using Integrative Remote Sensing and GIS Approaches

Risk and vulnerability assessment for natural hazards is of high
interest. Various methods focusing on building vulnerability
assessment have been developed ranging from simple approaches to
sophisticated ones depending on the objectives of the study, the
availability of data and technology. In-situ assessment methods
have been widely used to measure building vulnerability to various
types of hazards while remote sensing methods, specifically
developed for assessing building vulnerability to tsunami hazard,
are still very limited. The combination of remote sensing
approaches with in-situ methods offers unique opportunities to
overcome limitations of in-situ assessments. The main objective of
this research is to develop remote sensing techniques in assessing
building vulnerability to tsunami hazard as one of the key elements
of risk assessment. The research work has been performed in the
framework of the GITEWS (German-Indonesian Tsunami Early Warning
System) project. This research contributes to two major components
of tsunami risk assessment: (1) the provision of infrastructure
vulnerability information as an important element in the exposure
assessment; (2) tsunami evacuation modelling which is a critical
element for assessing immediate response and capability to evacuate
as part of the coping capacity analysis. The newly developed
methodology is based on the combination of in-situ measurements and
remote sensing techniques in a so-called “bottom-up remote sensing
approach”. Within this approach, basic information was acquired by
in-situ data collection (bottom level), which was then used as
input for further analysis in the remote sensing approach (upper
level). The results of this research show that a combined in-situ
measurement and remote sensing approach can be successfully
employed to assess and classify buildings into 4 classes based on
their level of vulnerability to tsunami hazard with an accuracy of
more than 80 percent. Statistical analysis successfully revealed
key spatial parameters which were regarded to link parameters
between in-situ and remote sensing approach such as size, height,
shape, regularity, orientation, and accessibility. The key spatial
parameters and their specified threshold values were implemented in
a decision tree algorithm for developing a remote sensing rule-set
of building vulnerability classification. A big number of buildings
in the study area (Cilacap city, Indonesia) were successfully
classified into the building vulnerability classes. The
categorization ranges from high to low vulnerable buildings (A to
C) and includes also a category of buildings which are potentially
suitable for tsunami vertical evacuation (VE). A multi-criteria
analysis was developed that incorporates three main components for
vulnerability assessment: stability, tsunami resistance and
accessibility. All the defined components were configured in a
decision tree algorithm by applying weighting, scoring and
threshold definition based on the building sample data. Stability
components consist of structure parameters, which are closely
related to the building stability against earthquake energy.
Building stability needs to be analyzed because most of tsunami
events in Indonesia are preceded by major earthquakes. Stability
components analysis was applied in the first step of the newly
developed decision tree algorithm to evaluate the building
stability when earthquake strikes. Buildings with total scores
below the defined threshold of stability were classified as the
most vulnerable class A. Such the buildings have a high probability
of being damaged after earthquake events. The remaining buildings
with total scores above the defined threshold of stability were
further analyzed using tsunami components and accessibility
components to classify them into the vulnerability classes B, C and
VE respectively. This research is based on very high spatial
resolution satellite images (QuickBird) and object-based image
analysis. Object-based image analysis is was chosen, because it
allows the formulation of rule-sets based on image objects instead
of pixels, which has significant advantages especially for the
analysis of very high resolution satellite images. In the
pre-processing stage, three image processing steps were performed:
geometric correction, pan-sharpening and filtering. Adaptive Local
Sigma and Morphological Opening filter techniques were applied as
basis for the subsequent building edge detection. The data
pre-processing significantly increased the accuracy of the
following steps of image classification. In the next step image
segmentation was developed to extract adequate image objects to be
used for further classification. Image classification was carried
out by grouping resulting objects into desired classes based on the
derived object features. A single object was assigned by its
feature characteristics calculated in the segmentation process. The
characteristic features of an object - which were grouped into
spectral signature, shape, size, texture, and neighbouring
relations - were analysed, selected and semantically modelled to
classify objects into object classes. Fuzzy logic algorithm and
object feature separation analysis was performed to set the
member¬ship values of objects that were grouped into particular
classes. Finally this approach successfully detected and mapped
building objects in the study area with their spatial attributes
which provide base information for building vulnerability
classification. A building vulnerability classification rule-set
has been developed in this research and successfully applied to
categorize building vulnerability classes. The developed approach
was applied for Cilacap city, Indonesia. In order to analyze the
transferability of this newly developed approach, the algorithm was
also applied to Padang City, Indonesia. The results showed that the
developed methodology is in general transferable. However, it
requires some adaptations (e.g. thresholds) to provide accurate
results. The results of this research show that Cilacap City is
very vulnerable to tsunami hazard. Class A (very vulnerable)
buildings cover the biggest portion of area in Cilacap City (63%),
followed by class C (28%), class VE (6%) and class B (3%).
Preventive measures should be carried out for the purpose of
disaster risk reduction, especially for people living in such the
most vulnerable buildings. Finally, the results were applied for
tsunami evacuation modeling. The buildings, which were categorized
as potential candidates for vertical evacuation, were selected and
a GIS approach was applied to model evacuation time and evacuation
routes. The results of this analysis provide important inputs to
the disaster management authorities for future evacuation planning
and disaster mitigation.