Tsunami Risk and Vulnerability

The research focuses on providing reliable spatial information in
support of tsunami risk and vulnerability assessment within the
framework of the German-Indonesian Tsunami Early Warning System
(GITEWS) project. It contributes to three major components of the
project: (1) the provision of spatial information on surface
roughness as an important parameter for tsunami inundation modeling
and hazard assessment; (2) the modeling of population distribution,
which is an essential factor in tsunami vulnerability assessment
and local disaster management activities; and (3) the settlement
detection and classification from remote sensing radar imagery to
support the population distribution research. Regarding the surface
roughness determination, research analyses on surface roughness
classes and their coefficients have been conducted. This included
the development of remote sensing classification techniques to
derive surface roughness classes, and integration of the thus
derived spatial information on surface roughness conditions to
tsunami inundation modeling. This research determined 12 classes of
surface roughness and their respective coefficients based on
analyses of published values. The developed method for surface
roughness classification of remote sensing data considered density
and neighborhood conditions, and resulted in more than 90%
accuracy. The classification method consists of two steps: main
land use classification and density and neighborhood analysis.
First, the main land uses were defined and a classification was
performed applying decision tree modeling. Texture parameters
played an important role in increasing the classification accuracy.
The density and neighborhood analysis further substantiated the
classification result towards identifying surface roughness
classes. Different classes such as residential areas and trees were
combined to new surface roughness classes, as “residential areas
with trees”. The density and neighborhood analysis led to an
appropriate representation of real surface roughness conditions.
This was used as an important input for tsunami inundation
modeling. By using Tohoku University’s Analysis Model for
Investigation Near-field Tsunami Number 3 (TUNAMI N3), the
spatially distributed surface roughness information was integrated
in tsunami inundation modeling and compared to the modeling results
applying a uniform surface roughness condition. An uncertainty
analysis of tsunami inundation modeling based on the variation of
surface roughness coefficients in the Cilacap study area was also
undertaken. It was demonstrated that the inundation modeling
results applying uniform and spatially distributed surface
roughness resulted in high differences of inundation lengths,
especially in areas far from the coastline. This result showed the
important role of surface roughness conditions in resisting tsunami
flow, which must be considered in tsunami inundation modeling. With
respect to the second research focus, the population distribution,
a concept of population distribution modeling was developed. Within
the modeling process, weighting factor determination, multi-scale
disaggregation and a comparative study to other methods were
conducted. The basis of the developed method was a combination of
census and land use data, which led to an improved spatial
resolution and accuracy of the population distribution.
Socio-economic data were used to derive weighting factors to
distributing people to land use classes. Moreover, in case of
missing input data, an approach was developed that allows for the
determination of generalized weighting factors. The approach to use
specific weightings, where possible and generalized ones, where
necessary, led to a flexible methodology with respect to the
achievable accuracy and availability of data. A comparative study
was performed by comparing this new model with previously developed
population distribution models. The newly developed model showed a
higher accuracy. The detailed population distribution information
was a valuable input for the vulnerability assessment being the
main data source for human exposure assessment and an important
contribution to evacuation time modeling. In support of the
population distribution research, settlement classification using
TerraSAR-X imagery was conducted. A current classification method
of speckle divergence analysis on SAR imagery was further developed
and improved by including the neighborhood concept. The settlement
classification provided highly accurate results in dense urban
areas, whereas the method needs to be further developed and
improved for rural settlement areas. Finally, it has been shown how
the results of this research can be applied. These applications
cover the integration of surface roughness conditions into the
tsunami inundation modeling and hazard mapping. The contributions
to tsunami vulnerability assessment and evacuation planning were
shown. Additionally, the results were integrated into the decision
support system of the Tsunami Early Warning Center in Jakarta.