Risk management in urban planning is of increasing importance to mitigate the growing amount of damage and the increasing number of casualties caused by natural disasters. Risk assessment to support management requires knowledge about present and future hazards, elements at risk and different types of vulnerability. This article deals with the assessment of social vulnerability (SV). In the past this has frequently been neglected due to lack of data and assessment difficulties. Existing approaches for SV assessment, primarily based on community-based methods or on census data, have limited efficiency and transferability. In this article a new method based on contextual analysis of image and GIS data is presented. An approach based on proxy variables that were derived from highresolution optical and laser scanning data was applied, in combination with elevation information and existing hazard data. Object-oriented image analysis was applied for the definition and estimation of those variables, focusing on SV indicators with physical characteristics. A reference Social Vulnerability Index (SVI) was created from census data available for the study area on a neighbourhood level and tested for parts of Tegucigalpa, Honduras. For the evaluation of the proxy-variables, a stepwise regression model to select the best explanatory variables for changes in the SVI was applied. Eight out of 47 variables explained almost 60% of the variance, whereby the slope position and the proportion of built-up area in a neighbourhood were found to be the most valuable proxies. 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 optimization in terms of assessment frequency and cost.
Due to continuous world-wide urbanization, especially in developing and emerging countries, urban environment represents one of the most dynamic landscapes on earth. The role of human settlements is becoming more and more important in land management, and the various structures of urban areas differ tremendously depending on social and historical backgrounds. So, beyond land-use and land-cover information, it is the urban composition that explains for social disparities, vulnerability to natural hazards, and deficits in urban and regional planning. In this study, first steps are presented towards and urban structural analysis using a multi-sensoral approach. With very high spatial resolution imageries, remote sensing offers techniques wellapplicable to measure and monitor intra-urban heterogeneties. Imageries from Quickbird sensor and TerraSAR-X device, Spotlight mode, are taken to delineate field-mapped and welldefined urban structure types (UST). A robust rule base will be built which allows for accurate classifications and interpretation of typical-man-made structures. The most typical structural characteristics of urban regions are alignment and type of buildings, open and green spaces, and transportation network.
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