Matthias Mück

Assessing building vulnerability to earthquake and tsunami hazard using remotely sensed data

Quantification of building vulnerability to earthquake and tsunami hazards is a key component for the implementation of structural mitigation strategies fostering the essential shift from post-disaster crisis reaction to preventive measures. Facing accelerating urban sprawl and rapid structural change in modern urban agglomerations in areas of high seismic and tsunami risk, the synergetic use of remote sensing and civil engineering methods offers a great potential to assess building structures up-to-date and area-wide. This paper provides a new methodology contextualizing key components in quantifying building vulnerability with regard to sequenced effects of seismic and tsunami impact. The study was carried out in Cilacap, a coastal City in Central Java, Indonesia. Central is the identification of significant correlations between building characteristics, easily detectable by remote sensing techniques, and detailed in situ measurements stating precise building vulnerability information. As a result, potential vertical evacuation shelters in the study area are detected and a realistic vulnerability assessment of the exposed building stock is given. These findings obtained allow for prioritization of intervention measures such as awareness and preparedness strategies and can be implemented in local disaster management.

A Method for Detecting Buildings Destroyed by the 2011 Tohoku Earthquake and Tsunami Using Multitemporal TerraSAR-X Data

In this letter, a new approach is proposed to classify tsunami-induced building damage into multiple classes using pre- and post-event high-resolution radar (TerraSAR-X) data. Buildings affected by the 2011 Tohoku earthquake and tsunami were the focus in developing this method. In synthetic aperture radar (SAR) data, buildings exhibit high backscattering caused by double-bounce reflection and layover. However, if the buildings are completely washed away or structurally destroyed by the tsunami, then this high backscattering might be reduced, and the post-event SAR data will show a lower sigma nought value than the pre-event SAR data. To exploit these relationships, a rapid method for classifying tsunami-induced building damage into multiple classes was developed by analyzing the statistical relationship between the change ratios in areas with high backscattering and in areas with building damage. The method was developed for the affected city of Sendai, Japan, based on the decision tree application of a machine learning algorithm. The results provided an overall accuracy of 67.4% and a kappa statistic of 0.47. To validate its transferability, the method was applied to the town of Watari, and an overall accuracy of 58.7% and a kappa statistic of 0.38 were obtained.

Contribution of earth observation and modelling to disaster response management: Methodological developments and recent examples

The paper outlines new research findings and hereof generated products in the field of earth observation and modeling technologies to support emergency response measures. Based on the recent earthquake and tsunami disaster in Japan (March 2011) examples will be given for new methodological developments and products to support emergency response strategies more effectively.

Validation of the DLR Global Urban Footprint in rural areas: A case study for Burkina Faso

Earth observation from space has often been used for the mapping of settlements on a global scale. The recently developed Global Urban Footprint (GUF - 2011/2013) aims at a global settlement classification with an improved geometric resolution based on data from the TerraSAR-X/TanDEM-X missions. Due to manifold challenges the settlement classifications are subject to distinct accuracy variations across global landscapes. This study presents a systematic accuracy assessment of the GUF product for the case study of Burkina Faso, an African country characterized by mainly rural, small-scale and fragmented settlement structures. As a first step, we conduct a relative, non site-specific comparison of available national and global human settlement layers (HSL) on national scale by the quantification of total settlement areas and derived density measures. In a second step, we aim at an absolute accuracy assessment on local scale using appropriate reference data. Therefore we use absolute accuracy measures based on the error matrix as well as pattern-based evaluation techniques with regard to physical settlement variations such as settlement size. Results clearly show the enhanced mapping capabilities of new high resolution global settlement products such as the GUF, especially for rural areas.