Environmental Earth Sciences

, Volume 65, Issue 1, pp 173–182

Social vulnerability assessment of natural hazards on county-scale using high spatial resolution satellite imagery: a case study in the Luogang district of Guangzhou, South China

Authors

  • J. Zeng
    • Key Laboratory of Marginal Sea GeologyGuangzhou Institute of Geochemistry, CAS
    • Key Laboratory of Marginal Sea GeologyGuangzhou Institute of Geochemistry, CAS
  • J. L. Zhang
    • Key Laboratory of Marginal Sea GeologyGuangzhou Institute of Geochemistry, CAS
  • T. P. Ouyang
    • Key Laboratory of Marginal Sea GeologyGuangzhou Institute of Geochemistry, CAS
  • S. F. Qiu
    • Key Laboratory of Marginal Sea GeologyGuangzhou Institute of Geochemistry, CAS
  • Y. Zou
    • Key Laboratory of Marginal Sea GeologyGuangzhou Institute of Geochemistry, CAS
  • T. Zeng
    • Key Laboratory of Marginal Sea GeologyGuangzhou Institute of Geochemistry, CAS
Original Article

DOI: 10.1007/s12665-011-1079-8

Cite this article as:
Zeng, J., Zhu, Z.Y., Zhang, J.L. et al. Environ Earth Sci (2012) 65: 173. doi:10.1007/s12665-011-1079-8

Abstract

Social vulnerability assessment of natural hazards aims to identify vulnerable populations and provide decision makers with scientific basis for their disaster prevention and mitigation decisions. A new method based on remote sensing is presented here to establish a model of social vulnerability for county-scale regions that lack of relative data. To calculate population density, which is the most important indicator in social vulnerability assessment, first, a statistical model is established to estimate the population on village level. Then a new concept defined as “population density based on land use” is created to replace the arithmetic population density. The former has taken the dynamic human distribution related to land use into account; thus, it can map the population distribution more realistically. The other two indicators are age structure and distance to hospital. The application of this method to the Luogang District of Guangzhou, South China demonstrated its capability of providing high spatial resolution and reasonable social vulnerability for social vulnerability assessment of natural hazards.

Keywords

Social vulnerability SPOT Land use Statistical model

Copyright information

© Springer-Verlag 2011