Citation

Fan X, Nie G, Deng Y, An J, Zhou J, Xia C, Pang X. Int. J. Disaster Risk Reduct. 2019; 33: 275-283.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.ijdrr.2018.10.013

PMID

unavailable

Abstract

To identify earthquake-damage areas after an earthquake occurs, a method based on Landsat-8 operational land imager (OLI) surface reflectance (SR) products is proposed. The SR pixels with the influences of clouds, cloud shadows, heavy aerosols, or instrument saturation are eliminated. Then, the mean and standard deviation (STD) images of SR products for two years before an earthquake are acquired; the mean images of the first three post-earthquake images are also obtained. The differences between post- and pre-earthquake mean SR images are taken to reflect the effects of the earthquake on SR. As we focus mainly on the impact of earthquakes on populated areas such as cities or villages, the pixels with Visible Infrared Imaging Radiometer Suite (VIIRS) day and night band annual average nighttime light products greater than 0 or Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data labeled 'urban and built-up' are taken as the study area for this work. Next, the areas with post- and pre-earthquake SR mean image differences greater than 0.1 and greater than 2 times the local STD are taken to represent earthquake-damaged areas. Finally, this method is applied to the 2015 Nepal moment magnitude (Mw) 7.8 earthquake. Compared with the actual damage data provided by the Nepal Earthquake 2015: Disaster Recovery and Reconstruction Information Platform (NDRRIP), the results show that the coefficient of determination between NDRRIP fully damaged buildings and Landsat-8 OLI SR data estimated damage pixels is 0.80 for the 69 Nepal districts not heavily influenced by snows.

Language: en

Keywords

Earthquake damage; Land surface reflectance; Landsat-8 OLI