Earthquake Damage Visualization (EDV) Technique for the Rapid Detection of Earthquake-Induced Damages Using SAR Data

Abstract

The damage of buildings and manmade structures, where most of human activities occur, is the major cause of casualties of from earthquakes. In this paper, an improved technique, Earthquake Damage Visualization (EDV) is presented for the rapid detection of earthquake damage using the Synthetic Aperture Radar (SAR) data. The EDV is based on the pre-seismic and co-seismic coherence change method. The normalized difference between the pre-seismic and co-seismic coherences, and vice versa, are used to calculate the forward (from pre-seismic to co-seismic) and backward (from co-seismic to pre-seismic) change parameters, respectively. The backward change parameter is added to visualize the retrospective changes caused by factors other than the earthquake. The third change-free parameter uses the average values of the pre-seismic and co-seismic coherence maps. These three change parameters were ultimately merged into the EDV as an RGB (Red, Green, and Blue) composite imagery. The EDV could visualize the earthquake damage efficiently using Horizontal transmit and Horizontal receive (HH), and Horizontal transmit and Vertical receive (HV) polarizations data from the Advanced Land Observing Satellite-2 (ALOS-2). Its performance was evaluated in the Kathmandu Valley, which was hit severely by the 2015 Nepal Earthquake. The cross-validation results showed that the EDV is more sensitive to the damaged buildings than the existing method. The EDV could be used for building damage detection in other earthquakes as well.

Keywords: 2015 Nepal Earthquake; ALOS-2; EDV; SAR; buildings; coherence; cross-validation; earthquake damage; visualization.

Figure 1

Location map of the study area, the Kathmandu Valley (red polygon) displayed with the shake areas.

Figure 2

Flow chart showing creation of the Earthquake Damage Visualization (EDV).

Figure 3

Distribution of the ground truth polygons in the Kathmandu Valley displayed over the line of sight displacement image resulted from differential interferometric processing in the research.

Figure 4

Earthquake Damage Visualization (EDV) imagery showing forward (red), backward (green), and change-free (blue) components: (a) Google map imagery of the Kathmandu Valley dated 3 May 2015; (b) the corresponding EDV imagery.

Figure 5

Performance of the EDV in different locations (a–d) that were highly damaged by the earthquake. The blocks of highly damaged (lethal) buildings are delineated by yellow polygons in each image. The left and middle columns show pre-seismic and post-seismic Google Earth images, whereas the right column shows the EDV image. The date of the Google Earth image is labeled in each image. The amount of redness in the EDV indicates severity of the building damage.

Figure 6

Performance of the EDV in different locations (a–d) that were highly damaged by the earthquake. The blocks of highly damaged (lethal) buildings are delineated by yellow polygons in each image. The left and middle columns show pre-seismic and post-seismic Google Earth images, whereas the right column shows the EDV image. The date of the Google Earth image is labeled in each image. The amount of redness in the EDV indicates severity of the building damage.

Figure 7

The NASA Damage Proxy Map (NDPM) in a number of highly damaged locations overlaid on the Google Earth imagery dated 3 May 2015 (post-seismic). The labels 5a to 5d and 6a to 6d in this figure denote the corresponding locations described in Figure 5 and Figure 6, respectively. The amount of redness in the NDPM imagery indicates severity of building damage.