{"_buckets": {"deposit": "e99146a6-094e-49a0-a148-9f609d7fcb6b"}, "_deposit": {"id": "5779", "owners": [1], "pid": {"revision_id": 0, "type": "depid", "value": "5779"}, "status": "published"}, "_oai": {"id": "oai:nied-repo.bosai.go.jp:00005779", "sets": []}, "author_link": [], "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2021-02-10", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "4", "bibliographicPageEnd": "639", "bibliographicPageStart": "639", "bibliographicVolumeNumber": "13", "bibliographic_titles": [{"bibliographic_title": "Remote Sensing", "bibliographic_titleLang": "en"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "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.", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "MDPI AG", "subitem_publisher_language": "en"}]}, "item_10001_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "10.3390/rs13040639"}}]}, "item_10001_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "2072-4292", "subitem_source_identifier_type": "EISSN"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Wen Liu", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Kiho Fujii", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Yoshihisa Maruyama", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Fumio Yamazaki", "creatorNameLang": "en"}]}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_title": "Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images", "subitem_title_language": "en"}]}, "item_type_id": "40001", "owner": "1", "path": ["1670839190650"], "permalink_uri": "https://nied-repo.bosai.go.jp/records/5779", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2023-03-30"}, "publish_date": "2023-03-30", "publish_status": "0", "recid": "5779", "relation": {}, "relation_version_is_last": true, "title": ["Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images"], "weko_shared_id": -1}