We use TanDEM-X and Sentinel-2 observations to disaggregate earthquake risk-related exposure data. We use the refined exposure data and model earthquake loss. Results for the city of Santiago de Chile show that earthquake risk has been underestimated before due to aggregated exposure data

Aravena Pelizari, Patrick

Geiß, Christian

Gómez Zapata, Camilo

Langbein, Michael

Pittore, Massimiliano

Priesmeier, Peter

Riedlinger, Torsten

Santa Maria, Hernan

Schoepfer, Elisabeth

Soto Calderon, Angela

Taubenböck, Hannes

Institute of Transport Research:Publications

   Work title: Earth observation-based disaggregation of exposure data for earthquake loss modeling Authors: Christian Geiß1, Patrick Aravena Pelizari1, Peter Priesmeier2, Angélica Rocio Soto Calderon3, Elisabeth Schoepfer1, Michael Langbein1, Torsten Riedlinger1, Hernán Santa María4,5, Juan Camilo Gómez Zapata6, Massimiliano Pittore7, and Hannes Taubenböck1 1 German Aerospace Center (DLR), German Remote Sensing Data Center (DFD) 2 TH Köln – University of Applied Sciences, Cologne, Germany  3 Technical University of Munich, Munich, Germany 4 National Research Center for Integrated Natural Disaster Management (CIGIDEN), Santiago de     Chile, Chile 5 Department of Structural and Geotechnical Engineering, Pontifical Catholic University of Chile, Santiago de Chile, Chile 6 GFZ German Research Centre for Geosciences, Seismic Hazard and Risk Dynamics, Potsdam, Germany 7 EURAC Research, Institute for Earth Observation, Bolzano, Italy Project Image:     Earth observation-based disaggregation of exposure data for earthquake loss modelling  Short project summary (max 250 characters): We use TanDEM-X and Sentinel-2 observations to disaggregate earthquake risk-related exposure data. We use the refined exposure data and model earthquake loss. Results for the city of Santiago de Chile show that earthquake risk has been underestimated before due to aggregated exposure data.    Keywords:  TanDEM-X, Sentinel-2, Urban Morphology, Natural Hazard Exposure, Earthquake Loss Estimation   1. Introduction: Exposure is an essential component of risk models and describes elements that are endangered by a hazard and susceptible to damage. The affiliated vulnerability characterizes the likelihood to experience damage regarding a certain level of hazard intensity [1]. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) of risk assessment procedures [2]. 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.  2. Methods: 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 [3]. To derive built-up density and height we follow an     automated workflow. First, “built-up” and “non-built-up” areas are distinguished by deploying the so-called Global Urban Footprint layer [4]. This information is deployed within a tailored filtering procedure for the TDM digital surface model data [5] to extract elevation information for built-up areas [6]. Subsequently, the intra-urban land cover is mapped under consideration of multispectral Sentinel-2 imagery. Thereby, intra-urban vegetation is pruned and the residual areas, i.e., elevated built-up areas, serve as basis to compute built-up densities and heights. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we develop both absolute and relative disaggregation techniques to spatially disaggregate and eventually constrain existing exposure data from administrative entities based on the derived morphologic properties of the built environment. Finally, we align vulnerability information, i.e., fragility functions, to the exposure data and establish scenario-based seismic loss estimations.    3. Results: Results are presented for the city of Santiago de Chile, Chile, which is prone to natural hazards such as earthquakes. We present earthquake loss estimations and corresponding sensitivity regarding the resolution properties of the exposure data used in the model. Results show how loss estimations vary substantially and that aggregated exposure information underestimates losses in our scenarios.  4. Conclusions: This study underlines the benefits of deploying modern earth observation technologies, that deliver data on a global scale, for refined exposure mapping and related earthquake loss estimation.          References: [1] Geiß C, Taubenböck H (2013) Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap. Natural Hazards 68:7-48. https://doi.org/10.1007/s11069-012-0322-2 [2] Geiß C, Aravena Pelizari P, Marconcini M, Sengara W, Edwards M, Lakes T, Taubenböck H (2015) Estimation of seismic buildings structural types using multi-sensor remote sensing and machine learning techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 104:175–188. https://doi.org/10.1016/j.isprsjprs.2014.07.016 [3] Geiß C, Leichtle T, Wurm M, Aravena Pelizari P, Standfuß I, Zhu X X, So E, Siedentop S, Esch T, Taubenböck H (2019) Large-Area Characterization of Urban Morphology – Mapping Built-Up Height and Density with the TanDEM-X Mission and Sentinel-2. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 12(8):2912–2927. https://doi.org/10.1109/JSTARS.2019.2917755 [4] Esch T, Heldens W, Hirner A, Keil M, Marconcini M, Roth A, Zeidler J, Dech S, Strano E (2017) Breaking new ground in human settlements from space—The Global Urban Footprint. ISPRS J. Photogramm. Remote Sens. 134:30–42. https://doi.org/10.1016/j.isprsjprs.2017.10.012 [5] Geiß C, Wurm M, Breunig M, Felbier A, Taubenböck H (2015) Normalization of TanDEM-X DSM Data in Urban Environments with Morphological Filters. IEEE Transactions on Geoscience and Remote Sensing, 53(8):4348–4362. https://doi.org/10.1109/TGRS.2015.2396195 [6] Geiß C, Aravena Pelizari P, Bauer S, Schmitt A, Taubenböck H (2020) Automatic Training Set Compilation with Multisource Geodata for DTM Generation from the TanDEM-X DSM. IEEE Geoscience and Remote Sensing Letters, 17(3):456–460. https://doi.org/10.1109/LGRS.2019.2921600           

Earth observation-based disaggregation of exposure data for earthquake loss modeling

We use TanDEM-X and Sentinel-2 observations to disaggregate earthquake risk-related exposure data. We use the refined exposure data and model  earthquake loss. Results for the city of Santiago de Chile show that earthquake  risk has been underestimated before due to aggregated exposure data

Soto, Angelica

Gomez Zapata, Camilo

Earth observation-based disaggregation of exposure data for earthquake loss modelling

https://elib.dlr.de/188407/1/Gei%C3%9F%20et%20al%202022%20Earth%20observation-based%20disaggregation%20of%20exposure%20data%20for%20earthquake%20loss%20modeling%20-JURSE2022.pdf

Earth observation-based disaggregation of exposure data for earthquake loss modeling

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