{"created":"2024-11-04T03:35:55.024857+00:00","id":2000329,"links":{},"metadata":{"_buckets":{"deposit":"2ed4a883-947c-4ebe-b5dc-ad08bed6354a"},"_deposit":{"created_by":15,"id":"2000329","owner":"15","owners":[15],"pid":{"revision_id":0,"type":"depid","value":"2000329"},"status":"published"},"_oai":{"id":"oai:nodai.repo.nii.ac.jp:02000329","sets":["1:1725514121806"]},"author_link":[],"item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"e02431","bibliographicVolumeNumber":"26","bibliographic_titles":[{"bibliographic_title":"Scientific African","bibliographic_titleLang":"en"}]}]},"item_10001_description_6":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"\"With the development of unmanned aerial vehicle (UAV) in the recent decade, very high-\nresolution aerial imagery has been used for precise land use/land cover classification (LULC).\nHowever, special structures in rural areas of developing countries such as traditional thatched\nhouses have posed challenges for precise LULC classification due to their undistinctive appearance\nand confusable characteristics in both reflectance and structure. LULC mapping is essential\nparticularly in rural areas which have high data scarcity and vulnerability to natural disasters.\nWith high-resolution observation has been achieved by UAVs, it is important to propose high-\nprecision LULC classification methods which can fully use the advantages of UAVs. To emphasize\nthe differences among the common LULC types in rural areas, this study proposed an original\nindex, the rural residence classification index (RCI). RCI was calculated as the product of the\nabove ground height and the square of the difference between the NDVI value and one. Then, a\ncomprehensive classification method was established by combining the RCI, the traditional\nthreshold method and a machine learning method. As a result of the comparison with the\ntraditional threshold method, object-based image analysis, and random forest methods, the\nmethod by this study achieved the highest overall accuracy (overall accuracy =0.903, kappa =\n0.875) and classification accuracy for detecting thatched houses (user’s accuracy =0.802, producer’s\naccuracy =0.920). These findings showed the possibility on identifying the confusable\nstructures in rural areas using remote sensing data, which was found difficult by the previous\nstudies so far. The method by this study can promote the further utility of UAVs in LULC classification\nin rural areas in developing countries, thereby providing precise and reliable material\nfor hydrological, hydraulic or ecosystem modelling, which eventually contributes to more accurate\nnatural hazard risk assessment, rural development, and natural resource management.\"","subitem_description_language":"en","subitem_description_type":"Other"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"ELSEVIER","subitem_publisher_language":"en"}]},"item_10001_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1016/j.sciaf.2024.e02431","subitem_relation_type_select":"DOI"}}]},"item_10001_rights_15":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"Creative Commons Attribution 4.0 International","subitem_rights_language":"en","subitem_rights_resource":"https://creativecommons.org/licenses/by/4.0/"}]},"item_10001_version_type_20":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"Rural Development Division, Japan International Research Center for Agricultural Sciences","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"Ke Zhang","creatorNameLang":"en"}]},{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"Faculty of Agriculture, Lilongwe University of Agriculture and Natural Resources","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"Lameck Fiwa","creatorNameLang":"en"}]},{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"Japan International Cooperation Agency","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"Madoka Kurata","creatorNameLang":"en"}]},{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"Faculty of Regional Environment Science, Tokyo University of Agriculture","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"Hiromu Okazawa","creatorNameLang":"en"}]},{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"Graduate School of Agro-Environmental Science, Tokyo University of Agriculture","affiliationNameLang":"en"}]}],"familyNames":[{"familyName":"Kenford A.B. Luweya","familyNameLang":"en"}]},{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"Forestry Division, Japan International Research Center for Agricultural Sciences","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"Mohammad Shamim Hasan Mandal","creatorNameLang":"en"}]},{"creatorAffiliations":[{"affiliationNames":[{"affiliationName":"Social Sciences Division, Japan International Research Center for Agricultural Sciences","affiliationNameLang":"en"}]}],"creatorNames":[{"creatorName":"Toru Sakai","creatorNameLang":"en"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2024-11-04"}],"filename":"1-s2.0-S2468227624003739-main.pdf","filesize":[{"value":"28 MB"}],"format":"application/pdf","licensetype":"license_0","url":{"url":"https://nodai.repo.nii.ac.jp/record/2000329/files/1-s2.0-S2468227624003739-main.pdf"},"version_id":"2b27dd7d-59b0-4eff-be88-38fc704f4824"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Precise LULC classification of rural area combining elevational and reflectance characteristics using UAV","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Precise LULC classification of rural area combining elevational and reflectance characteristics using UAV","subitem_title_language":"en"}]},"item_type_id":"10001","owner":"15","path":["1725514121806"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2024-11-04"},"publish_date":"2024-11-04","publish_status":"0","recid":"2000329","relation_version_is_last":true,"title":["Precise LULC classification of rural area combining elevational and reflectance characteristics using UAV"],"weko_creator_id":"15","weko_shared_id":-1},"updated":"2024-11-04T03:51:20.066820+00:00"}