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| Titel |
Potential of remote sensing techniques for tsunami hazard and vulnerability analysis – a case study from Phang-Nga province, Thailand |
| VerfasserIn |
H. Römer, P. Willroth, G. Kaiser, A. T. Vafeidis, R. Ludwig, H. Sterr, J. Revilla Diez |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 12, no. 6 ; Nr. 12, no. 6 (2012-06-28), S.2103-2126 |
| Datensatznummer |
250010937
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| Publikation (Nr.) |
 copernicus.org/nhess-12-2103-2012.pdf |
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| Zusammenfassung |
| Recent tsunami disasters, such as the 2004 Indian Ocean tsunami or the 2011
Japan earthquake and tsunami, have highlighted the need for effective risk
management. Remote sensing is a relatively new method for risk analysis,
which shows significant potential in conducting spatially explicit risk and
vulnerability assessments. In order to explore and discuss the potential and
limitations of remote sensing techniques, this paper presents a case study
from the tsunami-affected Andaman Sea coast of Thailand. It focuses on a
local assessment of tsunami hazard and vulnerability, including the
socio-economic and ecological components. High resolution optical data,
including IKONOS data and aerial imagery (MFC-3 camera) as well as different
digital elevation models, were employed to create basic geo-data including
land use and land cover (LULC), building polygons and topographic data sets
and to provide input data for the hazard and vulnerability assessment.
Results show that the main potential of applying remote sensing techniques
and data derives from a synergistic combination with other types of data. In
the case of hazard analysis, detailed LULC information and the correction of
digital surface models (DSMs) significantly improved the results of
inundation modeling. The vulnerability assessment showed that remote sensing
can be used to spatially extrapolate field data on socio-economic or
ecological vulnerability collected in the field, to regionalize exposure
elements and assets and to predict vulnerable areas. Limitations and
inaccuracies became evident regarding the assessment of ecological resilience
and the statistical prediction of vulnerability components, based on
variables derived from remote sensing data. |
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