文章基本信息

标题：Urban Social Vulnerability Assessment Using Object-Oriented Analysis of Remote Sensing and GIS Data. A Case Study for Tegucigalpa, Honduras
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作者：A. Ebert ; N. Kerle
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B7
页码：1307-1312
出版社：Copernicus Publications
摘要：This paper deals with the assessment of social vulnerability (SV) as a critical component of comprehensive disaster risk assessment. Indicators for SV relate to aspects on different scales. Individual characteristics, such as gender, age and education level, have to be assessed on a very local (individual) scale, whereas indicators such as living conditions, economic development and location of the household can be assessed on the scale of a building, building block, an administrative neighbourhood or city district. In turn, measures to reduce SV and thereby the disaster risk are taken on different levels. Information on SV is notoriously difficult to obtain, and traditionally either detailed field studies or census data have been used.This research, which was done in Tegucigalpa, Honduras, is not focused on individual people, but on the level of buildings and administrative neighbourhoods in the city, with the intention to analyse SV for a central area of 3*3 km as a potential starting point for more detailed analysis if needed. The central novelty is the use of image-based contextual, object-oriented analysis and the focus on physical proxies as indicators for SV, whereby we focus on landslide and flood hazards.Very high resolution remote sensing data, as well as GIS data and city maps were applied to delineate proxy variables with the goal to analyse four indicators for social vulnerability: (i) socio-economic status, (ii) commercial and industrial development of a neighbourhood, (iii) abundance of infrastructure/lifelines, (iv) and distance to those. The validation of the results was done using a Social Vulnerability Index (SVI) created based on census data. A subsequent stepwise regression analysis showed that eight out of 47 proxy variables were significant and could explain almost 60 % of the variation of the SVI, whereby the slope position (i.e. location of a building) and the proportion of built-up area in a neighbourhood (i.e. neighbourhood composition) were found to be the most valuable proxies. To make the approach transferable to other study areas with different data availability we also indicate where data can potentially be substituted with lower quality information than applied in this study. This work shows that contextual segmentation-based analysis of geospatial data can substantially aid in SV assessment, and, when combined with field- based information, leads to an optimisation of the assessment in terms of assessment frequency and costs
关键词：Social vulnerability assessment; Remote sensing; Object-oriented analysis; Proxy variables; Tegucigalpa