{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T17:16:39Z","timestamp":1776446199225,"version":"3.51.2"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T00:00:00Z","timestamp":1612915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["17H02066"],"award-info":[{"award-number":["17H02066"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Typhoon Hagibis passed through Japan on October 12, 2019, bringing heavy rainfall over half of Japan. Twelve banks of seven state-managed rivers collapsed, flooding a wide area. Quick and accurate damage proximity maps are helpful for emergency responses and relief activities after such disasters. In this study, we propose a quick analysis procedure to estimate inundations due to Typhoon Hagibis using multi-temporal Sentinel-1 SAR intensity images. The study area was Ibaraki Prefecture, Japan, including two flooded state-managed rivers, Naka and Kuji. First, the completely flooded areas were detected by two traditional methods, the change detection and the thresholding methods. By comparing the results in a part of the affected area with our field survey, the change detection was adopted due to its higher recall accuracy. Then, a new index combining the average value and the standard deviation of the differences was proposed for extracting partially flooded built-up areas. Finally, inundation maps were created by merging the completely and partially flooded areas. The final inundation map was evaluated via comparison with the flooding boundary produced by the Geospatial Information Authority (GSI) and the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) of Japan. As a result, 74% of the inundated areas were able to be identified successfully using the proposed quick procedure.<\/jats:p>","DOI":"10.3390\/rs13040639","type":"journal-article","created":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T16:12:10Z","timestamp":1613146330000},"page":"639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0655-4114","authenticated-orcid":false,"given":"Wen","family":"Liu","sequence":"first","affiliation":[{"name":"Graduate School of Engineering, Chiba University, Chiba, Chiba 263-8522, Japan"}]},{"given":"Kiho","family":"Fujii","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Chiba University, Chiba, Chiba 263-8522, Japan"}]},{"given":"Yoshihisa","family":"Maruyama","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Chiba University, Chiba, Chiba 263-8522, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3285-5997","authenticated-orcid":false,"given":"Fumio","family":"Yamazaki","sequence":"additional","affiliation":[{"name":"National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Ibaraki 305-0006, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,10]]},"reference":[{"key":"ref_1","unstructured":"Centre for Research on the Epidemiology of Disasters\u2014CRED (2020, December 20). 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