The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
neighbourhood composition, construction material and building
location.
Efficiency, transferability and repetition rate are important
points that need to be considered, i.e. methods are needed that
allow the assessment of SV in a sufficiently comprehensive way
and that can be broadly applied in a sustainable fashion. A
solution could be the combination of different traditional
approaches with new methods, such as the analysis of remote
sensing data.
REFERENCES
Angel, S., Bartley, K., Derr, M., et al.,2004. Rapid Urbanization
in Tegucigalpa, Honduras. Preparing for the doubling of the
City's Population in the next twenty-five years. Woodrow
Wilson School of Public and International Affairs, Princeton
University.
Azar, D., Rain, D.,2007. Identifying population vulnerable to
hydrological hazards in San Juan, Puerto Rico. GeoJournal. 69:
23-43.
Birkmann, J.,2005. Measuring Vulnerability. Report on the 1st
meeting of the expert working group "Measuring Vulnerability"
of the United Nations University Institute for Environment and
Human Security (UNU-EHS). Bonn.
Clark, G.E. et al.,1998. Assessing the vulnerability of coastal
communities to extreme storms: the case of Revere, MA., USA.
Mitigation and Adaptation Strategies for Global Change, 3(1):
59-82.
Cutter, S. L., Boruff, B. J., Shirley, W. L.,2003. Social
Vulnerability to Environmental Hazards. Social Science
Quarterly 82 (2): 242-260.
Dwyer, A., Zoppou, C., Nielsen, O., Day, S., Roberts, S.,2004.
Quantifying Social Vulnerability: A methodology for
identifying those at risk to natural hazards. Geoscience
Australia.
Ebert, A., Kerle, N. Stein, A.,2007. Remote sensing based
assessment of social vulnerability. In: Proceedings of the 5th
international workshop on remote sensing applications to
natural hazards, 10-11 September 2007, Washington, D.C.
Washington, D.C. : George Washington University : The Space
Policy Institute, 2007. art. 12. 7 p.
Fraser, C., Baltsavias, E., Gruen, A.,2002. Processing of Ikonos
images for submetre 3D positioning and building extraction.
ISPRS Journal of Photogrammetry & Remote Sensing, 56, 177—
194.
Haki, Z., Akyuerek, Z., Duezguen, S.,2004. Assessment of
Social Vulnerability Using Geographic Information Systems:
Pendik, Istanbul Case Study. In Proceedings of the 7th AGILE
Conference on Geographic Information Science. Heraklion,
Greece.
Herold, M., Liu, X., Clarke, K. C.,2003. Spatial Metrics and
Image Texture for Mapping Urban Land Use. Photogrammetric
Engineering & Remote Sensing 69 (9): 991-1001.
Jain, S.,2005. System evolution using high resolution satellite
data for urban regimes. Indian Institute of Technology Roorkee,
Department of Architecture and Planning. PhD thesis.
Kienberger, S., Steinbruch, F.2005. P-GIS and disaster risk
management: Assessing vulnerability with P-GIS methods -
Experiences from Büzi, Mozambique. International Conference
on Participatory Spatial Information Management and
Communication. PGIS '05.
Mueller, M., Segl, K., Heiden, U., Kaufmann, H.,2006.
Potential of High-Resolution Satellite Data in the Context of
Vulnerability of Buildings. Natural Hazards 38: 247-258.
Palmiano-Reganit, M.,2005. Analysis of Community's Coping
Mechanisms in Relation to Floods: A Case Study in Naga City,
Philippines. International Institute for Geo-Information Science
and Earth Observation. MSc thesis.
Rashed, T., & Weeks, J.,2003b. Exploring the spatial
association between measures from satellite imagery and
patterns of urban vulnerability to earthquake hazards.
International Archives of the Photogrammetry, Remote Sensing
and Spatial Information Sciences, XXXIV-7/W9, 144-152.
Saaty, T. L.,1980. The analytic hierarchy process. McGraw-Hill
International Book Company.
Tuceryan, M., Jain, A. K.,1998. The Handbook of Pattern
Recognition and Computer Vision. Chen, C.H., Pau, L. F.,
Wang, P. S. P. (ed.), World Scientific Publishing Co. 207-248.
Wu, S.Y., Yamal, B., and Fisher, A.,2002, Vulnerability of
coastal communities to sea-level rise: a case study of Cape May
County, New Jersey, USA: Climate Research, 22: 255-270.