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RSS FeedsRemote Sensing, Vol. 11, Pages 2184: Detection of Liquefaction Phenomena from the 2017 Pohang (Korea) Earthquake Using Remote Sensing Data (Remote Sensing)

 
 

20 september 2019 00:03:05

 
Remote Sensing, Vol. 11, Pages 2184: Detection of Liquefaction Phenomena from the 2017 Pohang (Korea) Earthquake Using Remote Sensing Data (Remote Sensing)
 


On 15 November 2017, liquefaction phenomena were observed around the epicenter after a 5.4 magnitude earthquake occurred in Pohang in southeast Korea. In this study, we attempted to detect areas of sudden water content increase by using SAR (synthetic aperture radar) and optical satellite images. We analyzed coherence changes using Sentinel-1 SAR coseismic image pairs and analyzed NDWI (normalized difference water index) changes using Landsat 8 and Sentinel-2 optical satellite images from before and after the earthquake. Coherence analysis showed no liquefaction-induced surface changes. The NDWI time series analysis models using Landsat 8 and Sentinel-2 optical images confirmed liquefaction phenomena close to the epicenter but could not detect liquefaction phenomena far from the epicenter. We proposed and evaluated the TDLI (temporal difference liquefaction index), which uses only one SWIR (short-wave infrared) band at 2200 nm, which is sensitive to soil moisture content. The Sentinel-2 TDLI was most consistent with field observations where sand blow from liquefaction was confirmed. We found that Sentinel-2, with its relatively shorter revisit period compared to that of Landsat 8 (5 days vs. 16 days), was more effective for detecting traces of short-lived liquefaction phenomena on the surface. The Sentinel-2 TDLI could help facilitate rapid investigations and responses to liquefaction damage.


 
186 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 2185: The Interannual Calibration and Global Nighttime Light Fluctuation Assessment Based on Pixel-Level Linear Regression Analysis (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2182: Using Canopy Height Model Obtained with Dense Image Matching of Archival Photogrammetric Datasets in Area Analysis of Secondary Succession (Remote Sensing)
 
 
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