Fakultät für Geowissenschaften - Digitale Hochschulschriften der LMU Archiv

This thesis deals with adsorption, self-assembly, and surface reactions of organic molecules on solid substrates, with the aim to fabricate higher hierarchical twodimensional (2D) structures. It is of genuine interest in materials science to develop strategies and methods for reproducible growth of extended molecular assemblies with specific and desired chemical, physical and functional properties. The experimental technique used was Scanning Tunneling Microscopy (STM) - an outstanding method to gain real space information of the atomic-scale realm of adsorbates on crystalline surfaces. The investigated systems are characterized by a complex interplay between adsorbateadsorbate interactions and adsorbate-substrate interactions. In one series of experiments this could be illustrated through self-assembly of hydrogen bonded heteromeric molecular networks on a chemically relatively inert graphite substrate. In this case, van-der-Waals forces between adsorbate and substrate have to be balanced with intermolecular hydrogen bonds in concert with weaker van-der-Waals forces. Since the magnitude of van-der-Waals forces between adsorbates and substrates correlates with the contact area, this type of interaction becomes more dominant for larger molecules. By stronger interactions which do not depend on molecule size, it was also possible to grow isotopological molecular networks, i.e. networks following a similar building plan. By varying for instance the length of aliphatic spacers, supramolecular structures with tuneable lattice parameter could be formed. Studies of organic molecules on chemically more active metal substrates show that more complex processes can be involved. In particular the concept of reactivity and surface-catalyzed reactions are discussed and illustrated by an intuitive example. It is demonstrated that strong molecule-substrate interaction can induce unimolecular reactions such as deprotonation of molecules or more generally dissociation of intramolecular bonds. This interaction strength, thus substrate reactivity is highly influenced by a variety of factors which include material, crystallographic surface orientation, and temperature. Further more the importance of so-called active sites on crystal surfaces, i.e. special sites with significantly increased interaction strength, is taken into account and exemplified with experimental results. Exploiting these fundamental principles, C-Br bond scission of brominated aromatic compounds was demonstrated upon adsorption on reactive substrates and followed by successful incorporation in covalently bonded networks. However, irreversibility of covalent bonds prevents similar control and error correction mechanisms over the system as compared to hydrogen bonded networks. A high defect density and a low degree of ordering is the consequence for the resulting 2D structures. In a final set of experiments aromatic thiol molecules could be assembled into highly or dered structures via metal-coordination bonds. The 2D gas of freely diffusing adatoms of a copper surface was thermally excited to finally transform a trithiolate precursor structure into metal-coordination networks via Cu-S metal coordination bonds. Two different coordination geometries were observed giving rise to the formation of two morphologically distinct phases. These studies revealed the impact of the adatom gas for surface reactivity and chemistry of metals.

After introducing the Discontinuous Galerkin (DG) method a detailed misfit analysis on its numerical approximation is performed. We investigate the accuracy of the scheme, the element type (tetrahedrons and hexahedrons), the spatial sampling of the computational domain and the number of propagated wavelengths. As the error norm we chose a time-frequency representation, which illustrates the time evolution of the spectral content. The results of this analysis are confirmed by a multi-institutional code validation project. In order to improve efficiency, we expand the computer code to non-conforming, hybrid meshes. In 2 dimensions, riangulars and quadrilaterals can be combined within one computational domain. Several convergence tests are carried out and the newly invented scheme is applied to different test cases including thin layers and variable material. Furthermore, as absorbing boundaries suffer from spurious reflections at artificial boundaries of the computational domain, we introduce a convolutional perfectly matched layer (CPML) to the scheme. Due to the loss of definite stability, we accomplish several test cases in order to examine the scheme’s behavior. A switchoff criterion for the CPML is suggested. Considering topographic effects on seismic waves, we perform a systematic study of different parameterizations involving the wave type and frequency of the input signal, the dataset resolution and various amplification factors of real topography in the region of Grenoble, France. Special events are simulated at Mt. Hochstaufen, Southern Bavaria, and compared to real recordings.

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.

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.

This thesis is an outcome of the project “Flood and damage assessment using very high resolution SAR data” (SAR-HQ), which is embedded in the interdisciplinary oriented RIMAX (Risk Management of Extreme Flood Events) programme, funded by the Federal Ministry of Education and Research (BMBF). It comprises the results of three scientific papers on automatic near real-time flood detection in high resolution X-band synthetic aperture radar (SAR) satellite data for operational rapid mapping activities in terms of disaster and crisis-management support. Flood situations seem to become more frequent and destructive in many regions of the world. A rising awareness of the availability of satellite based cartographic information has led to an increase in requests to corresponding mapping services to support civil-protection and relief organizations with disaster-related mapping and analysis activities. Due to the rising number of satellite systems with high revisit frequencies, a strengthened pool of SAR data is available during operational flood mapping activities. This offers the possibility to observe the whole extent of even large-scale flood events and their spatio-temporal evolution, but also calls for computationally efficient and automatic flood detection methods, which should drastically reduce the user input required by an active image interpreter. This thesis provides solutions for the near real-time derivation of detailed flood parameters such as flood extent, flood-related backscatter changes as well as flood classification probabilities from the new generation of high resolution X-band SAR satellite imagery in a completely unsupervised way. These data are, in comparison to images from conventional medium-resolution SAR sensors, characterized by an increased intra-class and decreased inter-class variability due to the reduced mixed pixel phenomenon. This problem is addressed by utilizing multi-contextual models on irregular hierarchical graphs, which consider that semantic image information is less represented in single pixels but in homogeneous image objects and their mutual relation. A hybrid Markov random field (MRF) model is developed, which integrates scale-dependent as well as spatio-temporal contextual information into the classification process by combining hierarchical causal Markov image modeling on automatically generated irregular hierarchical graphs with noncausal Markov modeling related to planar MRFs. This model is initialized in an unsupervised manner by an automatic tile-based thresholding approach, which solves the flood detection problem in large-size SAR data with small a priori class probabilities by statistical parameterization of local bi-modal class-conditional density functions in a time efficient manner. Experiments performed on TerraSAR-X StripMap data of Southwest England and ScanSAR data of north-eastern Namibia during large-scale flooding show the effectiveness of the proposed methods in terms of classification accuracy, computational performance, and transferability. It is further demonstrated that hierarchical causal Markov models such as hierarchical maximum a posteriori (HMAP) and hierarchical marginal posterior mode (HMPM) estimation can be effectively used for modeling the inter-spatial context of X-band SAR data in terms of flood and change detection purposes. Although the HMPM estimator is computationally more demanding than the HMAP estimator, it is found to be more suitable in terms of classification accuracy. Further, it offers the possibility to compute marginal posterior entropy-based confidence maps, which are used for the generation of flood possibility maps that express that the uncertainty in labeling of each image element. The supplementary integration of intra-spatial and, optionally, temporal contextual information into the Markov model results in a reduction of classification errors. It is observed that the application of the hybrid multi-contextual Markov model on irregular graphs is able to enhance classification results in comparison to modeling on regular structures of quadtrees, which is the hierarchical representation of images usually used in MRF-based image analysis. X-band SAR systems are generally not suited for detecting flooding under dense vegetation canopies such as forests due to the low capability of the X-band signal to penetrate into media. Within this thesis a method is proposed for the automatic derivation of flood areas beneath shrubs and grasses from TerraSAR-X data. Furthermore, an approach is developed, which combines high resolution topographic information with multi-scale image segmentation to enhance the mapping accuracy in areas consisting of flooded vegetation and anthropogenic objects as well as to remove non-water look-alike areas.