SENTINEL-1A ANALYSIS FOR DAMAGE ASSESSMENT: A CASE STUDY OF KUMAMOTO EARTHQUAKE IN 2016
DOI:
https://doi.org/10.20319/mijst.2019.51.2335Keywords:
Damage Assessment, Sentinel-1A, Interferometric Coherence, Kumamoto EarthquakeAbstract
The powerful earthquakes occurred in Kumamoto at the highest magnitude 7.3 hitting this prefecture on April 16, 2016 at 01.25 A.M. local time. This study proposed a method to utilize Sentinel-1A images to detect earthquake damaged areas. There were two images being used. One was before-earthquake images (on March 3, 2016 and March, 27 2016) and another was an after-earthquake image (on April 20, 2016). The method operated on interferometric coherence of sentinel-1A image. In order to estimate the damaged areas, the coherence change between the before-earthquake and during-earthquake pairs were processed. The damage map was produced by concerning into different classes comprised of the urban damaged areas, none and less damaged of the urban areas, and non-urban areas. This approach was compared with the ground-truth data, which provides high overall accuracy at 88% (kappa coefficient is 0.82). Consequently, Sentinel-1A could contribute the insightful geospatial information of the earthquake situation and support the disaster management.
References
Ansal, A. (2014). Perspectives on European Earthquake Engineering and Seismology: Volume 1. Geotechnical, Geological and Earthquake Engineering (Vol. 34). https://doi.org/10.1007/978-3-319-07118-3
Arciniegas, G. A., Bijker, W., Kerle, N., & Tolpekin, V. A. (2007). Coherence- and amplitude-based analysis of seismogenic damage in Bam, Iran, using ENVISAT ASAR data. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1571–1581. https://doi.org/10.1109/TGRS.2006.883149
Eguchi, R. T., Huyck, K., Adams, B. J., Mansouri, B., Houshmand, B., & Shinozuka, M. (2001). Resilient Disaster Response : Using Remote Sensing Technologies for Post-Earthquake Damage Detection. Earthquake Engineering to Extreme Events (MCEER), Research Progress and Accomplishments, 125–137.
ESA. (2012). Sentinel data policy and access to data. Retrieved January 3, 2019, from http://ec.europa.eu/DocsRoom/documents/648/attachments/1/translations/en/renditions/native
Hoffmann, J. (2007). Mapping damage during the Bam (Iran) earthquake using interferometric coherence. International Journal of Remote Sensing, 28(6), 1199–1216. https://doi.org/10.1080/01431160600928567
Ishitsuka, K., Tsuji, T., & Matsuoka, T. (2013). Detection and mapping of soil liquefaction in the 2011 Tohoku earthquake using SAR interferometry. Earth, Planets and Space, 64(12), 1267–1276. https://doi.org/10.5047/eps.2012.11.002
Konishi, T., & Suga, Y. (2018). Landslide detection using COSMO-SkyMed images : a case study of a landslide event on Kii Peninsula , Japan. European Journal of Remote Sensing, 51(1), 205–221. https://doi.org/10.1080/22797254.2017.1418185
M. Ventayen, R. J. (2017). Multilingual Detection and Mapping of Emergency and Disaster-Related Tweets. MATTER: International Journal of Science and Technology, 3(2), 240–249. https://doi.org/10.20319/mijst.2017.32.240249
Matsuoka, M., & Yamazaki, F. (2005). Building damage mapping of the 2003 Bam, Iran, earthquake using Envisat/ASAR intensity imagery. Earthquake Spectra, 21(SUPPL. 1), 1–5. https://doi.org/10.1193/1.2101027
Miyazaki, H., Nagai, M., & Shibasaki, R. (2015). Reviews of Geospatial Information Technology and Collaborative Data Delivery for Disaster Risk Management. ISPRS International Journal of Geo-Information, 4(4), 1936–1964. https://doi.org/10.3390/ijgi4041936
Mondini, A. C. (2017). Measures of spatial autocorrelation changes in multitemporal SAR images for event landslides detection. Remote Sensing, 9(6). https://doi.org/10.3390/rs9060554
Plank, S. (2014). Rapid Damage Assessment by Means of Multi-Temporal SAR — A Comprehensive Review and Outlook to Sentinel-1. Remote Sensing, 6(6), 4870–4906. https://doi.org/10.3390/rs6064870
Raucoules, D., Ristori, B., de Michele, M., & Briole, P. (2010). Surface displacement of the Mw 7 Machaze earthquake (Mozambique): Complementary use of multiband InSAR and radar amplitude image correlation with elastic modelling. Remote Sensing of Environment, 114(10), 2211–2218. https://doi.org/10.1016/j.rse.2010.04.023
Sandwell, D., Mellors, R., Tong, X., Wei, M., & Wessel, P. (2011). GMTSAR: An InSAR Processing System Based on Generic Mapping Tools. Retrieved May 15, 2014, from https://doi.org/10.2172/1090004
Sandwell, D., Mellors, R., Tong, X., Xu, X., Wei, M., & Wessel, P. (2016). GMTSAR: An InSAR Processing System Based on Generic Mapping Tools (Second Edition), 1–120.
Sandwell, D. T., Mellors, R., Xiaopeng, T., Matt, W., & Wessel, P. (2011). Open Radar Interferometry Software for Mapping Surface Deformation. Eos, 92(2), 2011. https://doi.org/10.1029/2011EO280002
Stramondo, S., Moro, M., Tolomei, C., Cinti, F. R., & Doumaz, F. (2005). InSAR surface displacement field and fault modelling for the 2003 Bam earthquake (southeastern Iran). Journal of Geodynamics, 40(2–3), 347–353. https://doi.org/10.1016/j.jog.2005.07.013
Suresh, D., & Yarrakula, K. (2018). InSAR based deformation mapping of earthquake using Sentinel 1A imagery. Geocarto International, 1–4. https://doi.org/10.1080/10106049.2018.1544289
Tamkuan, N., & Nagai, M. (2017). Fusion of Multi-Temporal Interferometric Coherence and Optical Image Data for the 2016 Kumamoto Earthquake Damage Assessment. ISPRS International Journal of Geo-Information, 6(7), 188. https://doi.org/10.3390/ijgi6070188
Twele, A., Cao, W., Plank, S., & Martinis, S. (2016). Sentinel-1-based flood mapping: a fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990–3004. https://doi.org/10.1080/01431161.2016.1192304
Usman Kaoje, I., & Ishiaku, I. (2017). Urban Flood Vulnerability Mapping of Lagos, Nigeria. MATTER: International Journal of Science and Technology, 3(1), 224–236. https://doi.org/10.20319/mijst.2017.s31.224236
Watanabe, M., Thapa, R. B., Ohsumi, T., Fujiwara, H., Yonezawa, C., Tomii, N., & Suzuki, S. (2016). Detection of damaged urban areas using interferometric SAR coherence change with PALSAR-2. Earth, Planets and Space, 68(1), 131. https://doi.org/10.1186/s40623-016-0513-2
Yamazaki, F., & Matsuoka, M. (2007). Remote Sensing Technologies in Post-Disaster Damage Assessment. Journal of Earthquake and Tsunami, 01(03), 193–210. https://doi.org/10.1142/S1793431107000122
Downloads
Published
How to Cite
Issue
Section
License
Copyright of Published Articles
Author(s) retain the article copyright and publishing rights without any restrictions.
All published work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
