**7. References**


features during inundation, such as inundation depth, current velocity and hydrodynamic force can be simulated by the numerical model. The tsunami fragility function can be constructed by combining the inspected damage data and simulated tsunami features using a statistical approach. The developed tsunami fragility curves for each location could be important tools for tsunami risk assessment against potential future tsunamis. However, applying tsunami fragility for future risk evaluation should be performed with care. The structural characteristics and behaviour of housing and buildings differ by country (Fig. 24). For example, an RC-frame building with brick walls is common in Southeast Asian countries. However, wooden walls are commonly used in Japan because of their light weight for reducing damage from earthquakes. These differences cause the tsunami damage

 Fig. 25. Examples of building damage in the case of the 2004 Indian Ocean tsunami in

QuickBird images are owned by DigitalGlobe, Inc. and IKONOS images are operated by GeoEye. ASTER and PALSAR images are owned by METI/NASA and METI/JAXA, respectively, and both are processed by GEO Grid, AIST. JERS-1/SAR image is also owned by METI/JAXA. TerraSAR-X image is the property of Infoterra GmbH and distributed by PASCO Corporation. We express our deep appreciation to the Industrial Technology Research Grant Program in 2008 (Project ID: 08E52010a) from the New Energy and Industrial Technology Development Organization (NEDO), the Willis Research Network (WRN) under the Pan-Asian/Oceanian tsunami risk modelling and mapping project and the Ministry of Education, Culture, Sports, Science and Technology (MEXT) for the financial

Aburaya, T. & Imamura, F. (2002). The proposal of a tsunami run–up simulation using

Gokon, H. & Koshimura, S. (2011). Mapping of buildings damage of the 2011 Tohoku

combined equivalent roughness, *Proceedings of the Coastal Engineering Conference* 

earthquake tsunami, *Proceedings of the 8th International Workshop on Remote Sensing for Post Disaster Response*, Standford University, California, United States, 15-16

characteristics to be different (Suppasri et al., 2011b).

Thailand and the 2011 Tohoku tsunami in Japan

*(JSCE)*, 49, 276–280 (in Japanese)

September 2011

**6. Acknowledgment** 

support for this study.

**7. References** 


**Part 3** 

**Sensors and Systems** 


**Part 3** 

**Sensors and Systems** 

168 Remote Sensing of Planet Earth

Matsuoka, M. & Yamazaki, F. (2004). Use of satellite SAR intensity imagery for detecting

Matsuoka, M. & Koshimura, S. (2010). Tsunami damage area estimation for the 2010 Maule,

Murosaki, Y. (1994). Great fire in Okushiri in case of the 1993 Hokkaido Nansei–Oki

Shuto, N. (2007). Damage and Reconstruction at Okushiri Town Caused by the 1993

Suppasri, A.; Koshimura, S. & Imamura, F. (2011a). Developing tsunami fragility curves

Suppasri, A.; Koshimura, S. & Imamura, F. (2011b). Tsunami risk assessment for building

*Conference (JSCE)*, Morioka, International session, 9-11 November 2011

Tsunami and Damages, Report No. 05306012, pp.161–170 (in Japanese) Nojima, N.; Matsuoka, M.; Sugito, M. & Esaki, K. (2006). Quantitative estimation of building

Engineers, 62(4), 808–821 (in Japanese with English abstract)

pp.975–994

November 2010

No.1, pp.44–49

173–189

building areas damaged due to earthquakes, *Earthquake Spectra*, Vol. 20, No. 3,

Chile earthquake using ASTER DEM and PALSAR images with the GEO grid system, *Proceedings of the 8th International Workshop on Remote Sensing for Post Disaster Response*, Tokyo Institute of Technology, Tokyo, Japan, 31 October - 1

earthquake, in Survey and Research on the 1993 Hokkaido Nansei–Oki Earthquake,

damage based on data integratoin of seismic intensities and satellite SAR imagery, *Journal of Structural Mechanics and Earthquake Engineering*, Japan Society of Civil

Hokkaido Nansei–oki Earthquake Tsunami, *Journal of Disaster Research*, Vol. 2,

based on the satellite remote sensing and the numerical modeling of the 2004 Indian Ocean tsunami in Thailand, *Natural Hazards and Earth System Sciences*, 11,

using numerical model and fragility curves, *Proceedings of the Coastal Engineering* 

**8** 

*Italy* 

**GNSS Signals: A Powerful Source for** 

*1Politecnico di Torino, Electronics Dept. 2Università di Perugia, Electronics and* 

*Information Engineering Dept.* 

**Atmosphere and Earth's Surface Monitoring** 

It is well known that Global Navigation Satellite Systems signals (which include for example the U.S. GPS and its modernization, the Russian GLONASS, the future European Galileo, the Chinese COMPASS), commonly processed for navigation purposes, can also be used to characterize media where they propagate in. In the last decade, GNSS atmospheric and Earth's surface remote sensing become more and more important, thanks to technical improvements applied to the processing of such "free-of-charge", everywhere available and

For example, remote sensing of wet part of troposphere is possible "extracting" the atmospheric delays from GNSS observations. These delays are associated to water vapour and are accumulated by the signal along its propagation path. In the double difference phase observation adjustment (a standard GNSS signal pre-processing) it is possible and quite easy to estimate the wet contribution to atmospheric total delay mapped into the zenith direction, the so-called Zenith Wet Delay. From one side the estimate of propagation delays is essential to improve the accuracy of the height determination in the geodetic positioning framework (Kleijer, 2004). From the remote sensing point of view, Zenith Wet Delay may be then transformed into the so-called Integrated Precipitable Water Vapour (IPWV). Therefore, the knowledge of the temporal behaviour of IPWV above a GPS receiver network allows meteorologists to know the evolution of total water vapour content in atmosphere, which is one of the variable operatively used in Numerical Weather Prediction

A second important application allows to add vertical variability information to the atmospheric parameter distribution with respect to the previous one, which represents an "integrated" quantity. The amplitude and phase variations experienced by GNSS signal crossing the atmospheric "limb" and received on-board a Low Earth Orbit satellite, can be used to infer temperature and water vapor profiles, thanks to the GNSS Radio Occultation technique (Melbourne et al., 1994; Ware et al., 1996; Kursinski et al., 1997; Hajj, 2002). Even if aspects related to such very important Remote Sensing technique are not treated in the present chapter (a comprehensive tutorial can be found in Liou et al (2010), while review of results

**1. Introduction** 

weather insensitive signals.

Models. These aspects are described in section 2.

Riccardo Notarpietro1, Manuela Cucca1 and Stefania Bonafoni2
