Tsunami risk and vulnerability [Elektronische Ressource] : remote sensing and GIS approaches for surface roughness determination, settlement mapping and population distribution modeling / eingereicht Muhammad Rokhis Khomarudin

Khomarudin, Muhammad Rokhis

Informations

• Publié par: ludwig-maximilians-universitat_munchen
• Publié le: 01 janvier 2010
• Nombre de lectures: 133
• Langue: English
• Poids de l'ouvrage: 10 Mo

Extrait

TSUNAMI RISK AND VULNERABILITY:
REMOTE SENSING AND GIS APPROACHES FOR SURFACE
ROUGHNESS DETERMINATION, SETTLEMENT MAPPING
AND POPULATION DISTRIBUTION MODELING



Dissertation
der Fakultät für Geowissenschaften
der Ludwig-Maximilians-Universität München












eingereicht von

Muhammad Rokhis Khomarudin

im Juli 2010










































1. Gutachter: Prof. Dr. Ralf Ludwig
2. Gutachter: Prof. Dr. Günter Strunz

Datum der Disputation: 7. Dezember 2010
IIACKNOWLEDGMENTS

First, I would like to sincerely thank my supervisors, Prof. Dr. Ralf Ludwig and
Prof. Dr. Günter Strunz, for their support, guidance and care in finalizing this
PHD thesis. Special thanks go to Dr. Kai Zoßeder, Dr. Joachim Post, Dr. Torsten
Riedlinger, Matthias Mück and Stephanie Wegscheider for three and a half years
of intensive discussions — I learned a great deal from all of you. My
appreciation also goes to other United Nations University, Institute for
Environmental and Human Security (UNU-EHS) PhD students, Sumaryono,
Widjo Kongko, and Widodo S. Pranowo for fruitful collaboration in carrying out
the research. Thanks also to Evalyne Katabaro from UNU-EHS for the invaluable
help on administrative matters.
I am very fortunate to have my wife, Retno Kustiyah, who always gives me
support, encouragement and delicious Indonesian food during my stay in
Germany. Thanks for your patience and love. My lovely son, Fathan Muhammad
Alif, you are my inspiration to keep my spirits up. To my mother, my mother-
in-law and all my family in Pekalongan and Batu, Indonesia, thank you for
providing me every day ‘doa’ for our success.
I would also like to thank Prof. Stefan Dech and Dr. Harald Mehl for having
facilitated my work at DLR and also to Ibu Ratih Dewanti, Ibu Komala and Pak
Agus Hidayat from LAPAN for providing their permission for me to study in
Germany. It has been a great opportunity for me to study and learn about the
science and culture. Thanks also go to all my colleagues, too many to mention
— to whom I would just like to say, vielen dank.
This research work has been performed in the framework of the German
Indonesian Tsunami Early Warning System (GITEWS project), which is carried
out by a large group of scientists and engineers from the German Research
Centre for Geosciences (GFZ, consortium leader) and its partners from the
Alfred Wegener Institute for Polar and Marine Research (AWI), the German
Aerospace Center (DLR), the GKSS Research Centre, the German Marine
Research Consortium (KDM), the Leibniz Institute for Marine Sciences (IFM-
GEOMAR), the United Nations University (UNU), the Federal Institute for
Geosciences and Natural Resources (BGR), the German Agency for Technical
Cooperation (GTZ), as well as Indonesian and other international partners.
Funding is provided by the German Federal Ministry for Education and Research
(BMBF), Grant 03TSU01. This research has been carried out under a PhD scholarship provided by UNU-
EHS, and with support from the German Aerospace Center (DLR) and the
National Institute of Aeronautics and Space (LAPAN) of Indonesia.


Summary
SUMMARY

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
I Summary
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.


II

CONTENTS

Summary ............................................................................................... I
Contents................................................................................................ III
List of Figures...........................................................................