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Remote Sens. 2015, 7(3), 2543-2601; doi:10.3390/rs70302543

An Integrated Method Combining Remote Sensing Data and Local Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China

1
Institute of Geology, China Earthquake Administration, Beijing 100029, China
2
China Earthquake Networks Center, China Earthquake Administration, Beijing 100045, China
3
Department of Applied Social Sciences, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong 999077, China
4
School of Geography, Beijing Normal University, Beijing 100875, China
5
State Key Laboratory of Remote Sensing Science, Co-Sponsored by Beijing Normal University and RADI, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editors: George P. Petropoulos and Prasad S. Thenkabail
Received: 25 August 2014 / Revised: 4 February 2015 / Accepted: 17 February 2015 / Published: 4 March 2015

Abstract

Rapid socioeconomic development in earthquake-prone areas can cause rapid changes in seismic loss risks. These changes make it difficult to ensure that risk reduction strategies are realistic, practical and effective over time. To overcome this difficulty, ongoing changes in risk should be captured timely, definitively, and accurately and then specific and well-timed adjustments of the relevant strategies should be made. However, methods for rapidly characterizing such seismic disaster risks over a large area have not been sufficiently developed. By focusing on building loss risks, this paper presents the development of an integrated method that combines remote sensing data and local knowledge to resolve this problem. This method includes two key interdependent steps. (1) To extract the heights and footprint areas of a large number of buildings accurately and quickly from single high-resolution optical remote sensing images; (2) To estimate the floor areas, identify structural types, develop damage probability matrixes, and determine economic parameters for calculating monetary losses due to seismic damage to the buildings by reviewing building-relevant local knowledge based on these two parameters (i.e., the building heights and footprint areas). This method is demonstrated in the Tangshan area of China. Based on the integrated method, the total floor area of the residential and public office buildings in central Tangshan in 2009 was 3.99% lower than the corresponding area number obtained by a conventional earthquake loss estimation project. Our field-based verification indicated that the mean relative error of the method for estimating the floor areas of the assessed buildings was 2.99%. A simulation of the impacts of the 1976 Ms 7.8 Tangshan earthquake using this method indicated that the total damaged floor area of the residential and public office buildings and the associated direct monetary loses in the study area could have been 8.00 and 28.73 times greater, respectively, than in 1976 if this earthquake had recurred in 2009, which is a strong warning to the local people regarding the increasing challenges they may face. View Full-Text
Keywords: rapid socioeconomic growth; high-resolution optical remote sensing image (Hr-ORSI); building-relevant local knowledge (Br-LK); large-scale estimation of risk; seismic loss risk to buildings; Tangshan; China; simulation of the impacts of the 1976 Ms 7.8 Tangshan earthquake rapid socioeconomic growth; high-resolution optical remote sensing image (Hr-ORSI); building-relevant local knowledge (Br-LK); large-scale estimation of risk; seismic loss risk to buildings; Tangshan; China; simulation of the impacts of the 1976 Ms 7.8 Tangshan earthquake
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Su, G.; Qi, W.; Zhang, S.; Sim, T.; Liu, X.; Sun, R.; Sun, L.; Jin, Y. An Integrated Method Combining Remote Sensing Data and Local Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China. Remote Sens. 2015, 7, 2543-2601.

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