A geospatial approach for vulnerability analysis on Moscow, Russia

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Abstract

Vulnerability is an assessment of priorities for implementation in a city. It is imperative to determine vulnerable regions in the city to identify priority areas that may require immediate intervention. Nowadays, several methods used for national, international and local level vulnerability assessment are based on remote sensing and GIS technology. In this research aim to determine the vulnerability of Moscow city using a geospatial based vulnerability index for sustainable development of the city. We use urbanization and vulnerability concept for the development of the city policy measures. We assessed the city vulnerability using a hypothetic diagram composed of exposure, sensitivity and adaptive capacity. Exposure, we considered the elevation, watershed, roads, railways and airport thematic layers. Sensitivity, the built-up area, industry, manages system such as farmland and LULC map from GIS data were used. To examine the adaptive capacity, we addressed the natural vegetation layer, economic points and infrastructure. Results shows, the center part of the city are highly and extremely vulnerable due to industry and high socio-economic activities when compared with the southern part of the city. We divided the whole city into 5 types of vulnerability: low vulnerability 5.44%, reasonable vulnerability 20.10%, moderate vulnerability 56.91%, high vulnerability 11.70% and extreme vulnerability 5.83%, in terms of the city area percentage. All this information is very inevitable and can be used to address management issues, such as resource prioritization and optimization. Keywords: Vulnerability, Geospatial, Landsat data, Remote Sensing, GIS.

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APA

Choudhary, K., Boori, M. S., & Kupriyanov, A. (2017). A geospatial approach for vulnerability analysis on Moscow, Russia. In International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM (Vol. 17, pp. 293–300). International Multidisciplinary Scientific Geoconference. https://doi.org/10.5593/sgem2017H/43/S19.037

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