Potential of remote sensing techniques for tsunami hazard and vulnerability analysis-a case study from Phang-Nga province, Thailand

Show simple item record

dc.identifier.uri http://dx.doi.org/10.15488/643
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/667
dc.contributor.author Römer, H.
dc.contributor.author Willroth, Philipp
dc.contributor.author Kaiser, G.
dc.contributor.author Vafeidis, A.T.
dc.contributor.author Ludwig, R.
dc.contributor.author Sterr, H.
dc.contributor.author Revilla Diez, Javier
dc.date.accessioned 2016-11-03T09:29:57Z
dc.date.available 2016-11-03T09:29:57Z
dc.date.issued 2012
dc.identifier.citation Römer, H.; Willroth, P.; Kaiser, G.; Vafeidis, A.T.; Ludwig, R. et al.: Potential of remote sensing techniques for tsunami hazard and vulnerability analysis-a case study from Phang-Nga province, Thailand. In: Natural Hazards and Earth System Science 12 (2012), Nr. 6, S. 2103-2126. DOI: http://dx.doi.org/10.5194/nhess-12-2103-2012
dc.description.abstract 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. eng
dc.description.sponsorship DFG
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartofseries Natural Hazards and Earth System Science 12 (2012), Nr. 6
dc.rights CC BY 3.0 Unported
dc.rights.uri http://creativecommons.org/licenses/by/3.0/
dc.subject digital elevation model eng
dc.subject ecological impact eng
dc.subject hazard assessment eng
dc.subject IKONOS eng
dc.subject remote sensing eng
dc.subject socioeconomic impact eng
dc.subject tsunami eng
dc.subject vulnerability eng
dc.subject Andaman Sea eng
dc.subject Indian Ocean eng
dc.subject Phangnga eng
dc.subject Southern Region eng
dc.subject Thailand eng
dc.subject.ddc 500 | Naturwissenschaften ger
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Potential of remote sensing techniques for tsunami hazard and vulnerability analysis-a case study from Phang-Nga province, Thailand
dc.type article
dc.type Text
dc.relation.issn 1561-8633
dc.relation.doi http://dx.doi.org/10.5194/nhess-12-2103-2012
dc.bibliographicCitation.issue 6
dc.bibliographicCitation.volume 12
dc.bibliographicCitation.firstPage 2103
dc.bibliographicCitation.lastPage 2126
dc.description.version publishedVersion
tib.accessRights frei zug�nglich

Files in this item

This item appears in the following Collection(s):

Show simple item record


Search the repository


My Account

Usage Statistics