{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T19:14:34Z","timestamp":1791054874925,"version":"4.1.0"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2017,6,22]],"date-time":"2017-06-22T00:00:00Z","timestamp":1498089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Earthquakes are one of the most devastating types of natural disasters, and happen with little to no warning. This study combined Landsat-8 and interferometric ALOS-2 coherence data without training area techniques by classifying the remote sensing ratios of specific features for damage assessment. Waterbodies and highly vegetated areas were extracted by the modified normalized difference water index (MNDWI) and normalized difference vegetation index (NDVI), respectively, from after-earthquake images in order to improve the accuracy of damage maps. Urban areas were classified from pre-event interferometric coherence data. The affected areas from the earthquake were detected with the normalized difference (ND) between the pre- and co-event interferometric coherence. The results presented three damage types; namely, damage to buildings caused by ground motion, liquefaction, and landslides. The overall accuracy (94%) of the confusion matrix was excellent. Results for urban areas were divided into three damage levels (e.g., none\u2013slight, slight\u2013heavy, heavy\u2013destructive) at a high (90%) overall accuracy level. Moreover, data on buildings damaged by liquefaction and landslides were in good agreement with field survey information. Overall, this study illustrates an effective damage assessment mapping approach that can support post-earthquake management activities for future events, especially in areas where geographical data are sparse.<\/jats:p>","DOI":"10.3390\/ijgi6070188","type":"journal-article","created":{"date-parts":[[2017,6,22]],"date-time":"2017-06-22T10:11:35Z","timestamp":1498126295000},"page":"188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Fusion of Multi-Temporal Interferometric Coherence and Optical Image Data for the 2016 Kumamoto Earthquake Damage Assessment"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8982-5361","authenticated-orcid":false,"given":"Nopphawan","family":"Tamkuan","sequence":"first","affiliation":[{"name":"Remote Sensing and GIS FoS, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiko","family":"Nagai","sequence":"additional","affiliation":[{"name":"Remote Sensing and GIS FoS, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand"},{"name":"Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 2-16-1, Tokiwadai, Ube, Yamaguchi 755-8611, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ansal, A. (2014). Perspectives on European Earthquake Engineering and Seismology, Springer.","DOI":"10.1007\/978-3-319-07118-3"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Boccardo, P., and Tonolo, F.G. (2015). Remote sensing role in emergency mapping for disaster response. 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