Remote sensing and spatial multi-criteria analysis for tsunami vulnerability assessment

被引:9
|
作者
Sambah, Abu Bakar [1 ,2 ]
Miura, Fusanori [1 ]
机构
[1] Yamaguchi Univ, Dept Environm Sci & Engn, Yamaguchi, Japan
[2] Brawijaya Univ, Fac Fisheries & Marine Sci, Malang, Indonesia
关键词
Vulnerability; Disasters; Remote sensing; Tsunami; Spatial multi-criteria; DECISION-MAKING; HAZARD; BUILDINGS; RISK;
D O I
10.1108/DPM-05-2013-0082
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Purpose - The purpose of this paper is to assess the vulnerability of the Kesennuma area in Japan to a tsunami disaster and to map the area of inundation. Design/methodology/approach - Digital elevation model (DEM) data and ALOS image were used to create maps of the parameters of this study area: elevation, slope, coastal proximity, river, and land use. An analytical hierarchy process was used to assign weights to each parameter and a spatial multi-criteria analysis was applied through cell-based modelling for vulnerability mapping. Findings - The vulnerability map shows that 17.679 km(2) of the area could be inundated by a tsunami. High vulnerability areas were mostly found in coastal areas with a sloping coast and a cape area. A low elevation and the presence of rivers or water channels are factors that increase the impact of tsunamis. Inundation areas were predicted to spread in areas identified as having either high vulnerability or slightly high vulnerability. Research limitations/implications - Because of the limited geospatial data, the authors encourage further studies using DEM data with a high spatial resolution. Practical implications - The results of this research can be used as basic information for disaster mitigation and urban planning in coastal areas. Originality/value - This research creates a new approach for assessing which areas could be inundated by tsunamis, based on the vulnerability map generated through remote sensing and spatial multi-criteria analysis. Moreover, the parameters used are very close to those of actual inundation maps.
引用
收藏
页码:271 / 295
页数:25
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