A quantitative method for storm surge vulnerability assessment - a case study of Weihai city

In traditional vulnerability assessments, a synthetic index method is usually used to select all types of social and economic indexes so that more aspects can be covered; however, the requisite social and economic data are not always available or are not highly relevant to the studied geographical s...

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Veröffentlicht in:International journal of digital earth 2017-05, Vol.10 (5), p.539-559
Hauptverfasser: Liu, Jin, Gong, Jian-Hua, Liang, Jian-Ming, Li, Yi, Kang, Lin-Chong, Song, Li-Li, Shi, Sui-Xiang
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Sprache:eng
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Zusammenfassung:In traditional vulnerability assessments, a synthetic index method is usually used to select all types of social and economic indexes so that more aspects can be covered; however, the requisite social and economic data are not always available or are not highly relevant to the studied geographical space, which makes it difficult to conduct quantitative calculations. In this paper, a spatial value density assessment method was developed to improve the hazard of place model. First, a three-dimensional (3D) model of a coastal city was obtained using oblique airborne photogrammetry and image-based 3D reconstruction and then, architecture footprints were employed to extract the geometric information of each individual building. Additionally, a vulnerability assessment system was established to quantitatively account for the aggregate economic value of a selected set of urban surface features. Using geographic information system (GIS) techniques, the aggregate value of these urban features within each geographic unit can be accurately calculated to quantify the exposure and vulnerability of coastal cities to storm surge. A vulnerability assessment was conducted using Weihai city as an example. The study shows that vulnerability assessment accuracy was greatly improved by downscaling the assessment granularity from county-level administrative districts to a 1-km grid.
ISSN:1753-8947
1753-8955
DOI:10.1080/17538947.2016.1229052