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Benefits of global earth observation missions for disaggregation of exposure data and earthquake loss modeling: evidence from Santiago de Chile

Author

Listed:
  • Christian Geiß

    (German Remote Sensing Data Center (DFD))

  • Peter Priesmeier

    (TH Köln – University of Applied Sciences)

  • Patrick Aravena Pelizari

    (German Remote Sensing Data Center (DFD))

  • Angélica Rocio Soto Calderon

    (Technical University of Munich)

  • Elisabeth Schoepfer

    (German Remote Sensing Data Center (DFD))

  • Torsten Riedlinger

    (German Remote Sensing Data Center (DFD))

  • Mabé Villar Vega

    (UME School, IUSS)

  • Hernán Santa María

    (National Research Center for Integrated Natural Disaster Management (CIGIDEN)
    Pontifical Catholic University of Chile)

  • Juan Camilo Gómez Zapata

    (GFZ German Research Centre for Geosciences
    University of Potsdam)

  • Massimiliano Pittore

    (GFZ German Research Centre for Geosciences
    EURAC Research, Institute for Earth Observation)

  • Emily So

    (University of Cambridge)

  • Alexander Fekete

    (TH Köln – University of Applied Sciences)

  • Hannes Taubenböck

    (German Remote Sensing Data Center (DFD))

Abstract

Exposure is an essential component of risk models and describes elements that are endangered by a hazard and susceptible to damage. The associated vulnerability characterizes the likelihood of experiencing damage (which can translate into losses) at a certain level of hazard intensity. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) of risk assessment procedures. Existing models often describe exposure in an aggregated manner, e.g., by relying on statistical/census data for given administrative entities. Nowadays, earth observation techniques allow the collection of spatially continuous information for large geographic areas while enabling a high geometric and temporal resolution. Consequently, we exploit measurements from the earth observation missions TanDEM-X and Sentinel-2, which collect data on a global scale, to characterize the built environment in terms of constituting morphologic properties, namely built-up density and height. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we establish dasymetric methods for disaggregation. The results are presented for the city of Santiago de Chile, which is prone to natural hazards such as earthquakes. We present loss estimations due to seismic ground shaking and corresponding sensitivity as a function of the resolution properties of the exposure data used in the model. The experimental results underline the benefits of deploying modern earth observation technologies for refined exposure mapping and related earthquake loss estimation with enhanced accuracy properties.

Suggested Citation

  • Christian Geiß & Peter Priesmeier & Patrick Aravena Pelizari & Angélica Rocio Soto Calderon & Elisabeth Schoepfer & Torsten Riedlinger & Mabé Villar Vega & Hernán Santa María & Juan Camilo Gómez Zapat, 2023. "Benefits of global earth observation missions for disaggregation of exposure data and earthquake loss modeling: evidence from Santiago de Chile," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(2), pages 779-804, November.
  • Handle: RePEc:spr:nathaz:v:119:y:2023:i:2:d:10.1007_s11069-022-05672-6
    DOI: 10.1007/s11069-022-05672-6
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    References listed on IDEAS

    as
    1. Christian Geiß & Hannes Taubenböck, 2013. "Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(1), pages 7-48, August.
    2. Christoph Aubrecht & Dilek Özceylan & Klaus Steinnocher & Sérgio Freire, 2013. "Multi-level geospatial modeling of human exposure patterns and vulnerability indicators," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(1), pages 147-163, August.
    3. Marina Mueller & Karl Segl & Uta Heiden & Hermann Kaufmann, 2006. "Potential of High-Resolution Satellite Data in the Context of Vulnerability of Buildings," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 38(1), pages 247-258, May.
    4. Hernán Santa María & Matías A. Hube & Felipe Rivera & Catalina Yepes-Estrada & Jairo A. Valcárcel, 2017. "Development of national and local exposure models of residential structures in Chile," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(1), pages 55-79, March.
    5. Massimiliano Pittore & Marc Wieland & Kevin Fleming, 2017. "Perspectives on global dynamic exposure modelling for geo-risk assessment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(1), pages 7-30, March.
    6. Daniele Ehrlich & Patrizia Tenerelli, 2013. "Optical satellite imagery for quantifying spatio-temporal dimension of physical exposure in disaster risk assessments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(3), pages 1271-1289, September.
    Full references (including those not matched with items on IDEAS)

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