Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15799
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dc.contributorDepartment of Applied Social Sciences-
dc.creatorSu, G-
dc.creatorQi, W-
dc.creatorZhang, S-
dc.creatorSim, T-
dc.creatorLiu, X-
dc.creatorSun, R-
dc.creatorSun, L-
dc.creatorJin, Y-
dc.date.accessioned2015-07-13T10:35:04Z-
dc.date.available2015-07-13T10:35:04Z-
dc.identifier.urihttp://hdl.handle.net/10397/15799-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Su, G., Qi, W., Zhang, S., Sim, T., Liu, X., Sun, R., … Jin, Y. (2015). An integrated method combining remote sensing data and local knowledge for the large-scale estimation of seismic loss risks to buildings in the context of rapid socioeconomic growth : a case study in Tangshan, China. Remote Sensing, 7(3), (Suppl. ), 2543-2601 is available athttps://dx.doi.org/10.3390/rs70302543en_US
dc.subjectBuilding-relevant local knowledge (Br-LK)en_US
dc.subjectChinaen_US
dc.subjectHigh-resolution optical remote sensing image (Hr-ORSI)en_US
dc.subjectLarge-scale estimation of risken_US
dc.subjectRapid socioeconomic growthen_US
dc.subjectSeismic loss risk to buildingsen_US
dc.subjectSimulation of the impacts of the 1976 Ms 7.8 Tangshan earthquakeen_US
dc.subjectTangshanen_US
dc.titleAn integrated method combining remote sensing data and local knowledge for the large-scale estimation of seismic loss risks to buildings in the context of rapid socioeconomic growth : a case study in Tangshan, Chinaen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2543en_US
dc.identifier.epage2601en_US
dc.identifier.volume7en_US
dc.identifier.issue3en_US
dc.identifier.doi10.3390/rs70302543en_US
dcterms.abstractRapid socioeconomic development in earthquake-prone areas can cause rapid changes in seismic loss risks. These changes make it difficult to ensure that risk reduction strategies are realistic, practical and effective over time. To overcome this difficulty, ongoing changes in risk should be captured timely, definitively, and accurately and then specific and well-timed adjustments of the relevant strategies should be made. However, methods for rapidly characterizing such seismic disaster risks over a large area have not been sufficiently developed. By focusing on building loss risks, this paper presents the development of an integrated method that combines remote sensing data and local knowledge to resolve this problem. This method includes two key interdependent steps. (1) To extract the heights and footprint areas of a large number of buildings accurately and quickly from single high-resolution optical remote sensing images; (2) To estimate the floor areas, identify structural types, develop damage probability matrixes, and determine economic parameters for calculating monetary losses due to seismic damage to the buildings by reviewing building-relevant local knowledge based on these two parameters (i.e., the building heights and footprint areas). This method is demonstrated in the Tangshan area of China. Based on the integrated method, the total floor area of the residential and public office buildings in central Tangshan in 2009 was 3.99% lower than the corresponding area number obtained by a conventional earthquake loss estimation project. Our field-based verification indicated that the mean relative error of the method for estimating the floor areas of the assessed buildings was 2.99%. A simulation of the impacts of the 1976 Ms 7.8 Tangshan earthquake using this method indicated that the total damaged floor area of the residential and public office buildings and the associated direct monetary loses in the study area could have been 8.00 and 28.73 times greater, respectively, than in 1976 if this earthquake had recurred in 2009, which is a strong warning to the local people regarding the increasing challenges they may face.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationRemote sensing, Mar. 2015, v. 7, no. 3, p. 2543-2601-
dcterms.isPartOfRemote sensing-
dcterms.issued2015-03-04-
dc.identifier.scopus2-s2.0-84926379871-
dc.identifier.eissn2072-4292en_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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