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.

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This study produces alteration mineral maps based on WorldView-3 (WV-3) data and field shortwave-infrared (SWIR) spectroscopy. It is supported by conventional analytical methods such as X-ray diffraction, X-ray fluorescence, and electron probe X-ray micro analyzer as an initial step for mineral exploration in eastern Tsogttsetsii, Mongolia, where access is limited. Distributions of advanced argillic minerals (alunite, dickite, and kaolinite), illite/smectite (illite, smectite, and mixed-layered illite-smectite), and ammonium minerals (buddingtonite and NH4-illite) were mapped using the decorrelation stretch, band math, and mixture-tuned-matched filter (MTMF) techniques. The accuracy assessment of the WV-3 MTMF map using field SWIR data showed good WV-3 SWIR data accuracy for spectrally predominant alteration minerals such as alunite, kaolinite, buddingtonite, and NH4-illite. The combination of WV-3 SWIR mineral mapping and a drone photogrammetric-derived digital elevation model contributed to an understanding of the structural development of the hydrothermal system through visualization of the topographic and spatial distribution of surface alteration minerals. Field SWIR spectroscopy provided further detailed information regarding alteration minerals such as chemical variations of alunite, crystallinity of kaolinite, and aluminum abundance of illite that was unavailable in WV-3 SWIR data. Combining WV-3 SWIR data and field SWIR spectroscopy with conventional exploration methods can narrow the selection between deposit models and facilitate mineral exploration.

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