{"_buckets": {"deposit": "6a3b12ff-cb22-4429-99bc-4d36f1ca6a44"}, "_deposit": {"id": "21961", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21961"}, "status": "published"}, "_oai": {"id": "oai:soar-ir.repo.nii.ac.jp:00021961", "sets": ["1850"]}, "author_link": ["110745", "110746", "110747", "110748", "110749"], "item_1628147817048": {"attribute_name": "出版タイプ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_6_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2016-12-19", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "12", "bibliographicPageStart": "1034", "bibliographicVolumeNumber": "8", "bibliographic_titles": [{"bibliographic_title": "REMOTE SENSING"}]}]}, "item_6_description_20": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Individual tree delineation using remotely sensed data plays a very important role in precision forestry because it can provide detailed forest information on a large scale, which is required by forest managers. This study aimed to evaluate the utility of airborne laser scanning (ALS) data for individual tree detection and species classification in Japanese coniferous forests with a high canopy density. Tree crowns in the study area were first delineated by the individual tree detection approach using a canopy height model (CHM) derived from the ALS data. Then, the detected tree crowns were classified into four classesPinus densiflora, Chamaecyparis obtusa, Larix kaempferi, and broadleaved treesusing a tree crown-based classification approach with different combinations of 23 features derived from the ALS data and true-color (red-green-blueRGB) orthoimages. To determine the best combination of features for species classification, several loops were performed using a forward iteration method. Additionally, several classification algorithms were compared in the present study. The results of this study indicate that the combination of the RGB images with laser intensity, convex hull area, convex hull point volume, shape index, crown area, and crown height features produced the highest classification accuracy of 90.8% with the use of the quadratic support vector machines (QSVM) classifier. Compared to only using the spectral characteristics of the orthophotos, the overall accuracy was improved by 14.1%, 9.4%, and 8.8% with the best combination of features when using the QSVM, neural network (NN), and random forest (RF) approaches, respectively. In terms of different classification algorithms, the findings of our study recommend the QSVM approach rather than NNs and RFs to classify the tree species in the study area. However, these classification approaches should be further tested in other forests using different data. This study demonstrates that the synergy of the ALS data and RGB images could be a promising approach to improve species classifications.", "subitem_description_type": "Abstract"}]}, "item_6_description_30": {"attribute_name": "資源タイプ（コンテンツの種類）", "attribute_value_mlt": [{"subitem_description": "Article", "subitem_description_type": "Other"}]}, "item_6_description_5": {"attribute_name": "引用", "attribute_value_mlt": [{"subitem_description": "REMOTE SENSING.8(12):1034(2016)", "subitem_description_type": "Other"}]}, "item_6_link_3": {"attribute_name": "信州大学研究者総覧へのリンク", "attribute_value_mlt": [{"subitem_link_text": "Deng, Songqiu", "subitem_link_url": "https://soar-rd.shinshu-u.ac.jp/profile/ja.OFfhupyC.html"}, {"subitem_link_text": "Katoh, Masato", "subitem_link_url": "https://soar-rd.shinshu-u.ac.jp/profile/ja.OhyNPUkh.html"}]}, "item_6_link_67": {"attribute_name": "WoS", "attribute_value_mlt": [{"subitem_link_text": "Web of Science", "subitem_link_url": "http://gateway.isiknowledge.com/gateway/Gateway.cgi?\u0026GWVersion=2\u0026SrcAuth=ShinshuUniv\u0026SrcApp=ShinshuUniv\u0026DestLinkType=FullRecord\u0026DestApp=WOS\u0026KeyUT=000392489400063"}]}, "item_6_publisher_4": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "MDPI"}]}, "item_6_relation_48": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_name": [{"subitem_relation_name_text": "10.3390/rs8121034"}], "subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.3390/rs8121034", "subitem_relation_type_select": "DOI"}}]}, "item_6_rights_62": {"attribute_name": "権利", "attribute_value_mlt": [{"subitem_rights": "© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/)."}]}, "item_6_select_64": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_6_source_id_35": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "2072-4292", "subitem_source_identifier_type": "ISSN"}]}, "item_6_source_id_39": {"attribute_name": "NII ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "2072-4292", "subitem_source_identifier_type": "ISSN"}]}, "item_6_text_69": {"attribute_name": "wosonly authkey", "attribute_value_mlt": [{"subitem_text_value": "SMALL-FOOTPRINT LIDAR; HIGH-DENSITY LIDAR; AIRBORNE LIDAR; STEM VOLUME; FOREST DATA; SEGMENTATION; BIOMASS; EXTRACTION; PARAMETERS; FEATURES"}]}, "item_6_text_70": {"attribute_name": "wosonly keywords", "attribute_value_mlt": [{"subitem_text_value": "Individual tree delineation using remotely sensed data plays a very important role in precision forestry because it can provide detailed forest information on a large scale, which is required by forest managers. This study aimed to evaluate the utility of airborne laser scanning (ALS) data for individual tree detection and species classification in Japanese coniferous forests with a high canopy density. Tree crowns in the study area were first delineated by the individual tree detection approach using a canopy height model (CHM) derived from the ALS data. Then, the detected tree crowns were classified into four classesPinus densiflora, Chamaecyparis obtusa, Larix kaempferi, and broadleaved treesusing a tree crown-based classification approach with different combinations of 23 features derived from the ALS data and true-color (red-green-blueRGB) orthoimages. To determine the best combination of features for species classification, several loops were performed using a forward iteration method. Additionally, several classification algorithms were compared in the present study. The results of this study indicate that the combination of the RGB images with laser intensity, convex hull area, convex hull point volume, shape index, crown area, and crown height features produced the highest classification accuracy of 90.8% with the use of the quadratic support vector machines (QSVM) classifier. Compared to only using the spectral characteristics of the orthophotos, the overall accuracy was improved by 14.1%, 9.4%, and 8.8% with the best combination of features when using the QSVM, neural network (NN), and random forest (RF) approaches, respectively. In terms of different classification algorithms, the findings of our study recommend the QSVM approach rather than NNs and RFs to classify the tree species in the study area. However, these classification approaches should be further tested in other forests using different data. This study demonstrates that the synergy of the ALS data and RGB images could be a promising approach to improve species classifications."}]}, "item_6_textarea_68": {"attribute_name": "wosonly abstract", "attribute_value_mlt": [{"subitem_textarea_value": "forest resource measurement; airborne laser scanning; RGB imagery; individual tree detection; tree crown-based classification; machine learning approaches"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Deng, Songqiu"}], "nameIdentifiers": [{"nameIdentifier": "110745", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Katoh, Masato"}], "nameIdentifiers": [{"nameIdentifier": "110746", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Yu, Xiaowei"}], "nameIdentifiers": [{"nameIdentifier": "110747", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Hyyppa, Juha"}], "nameIdentifiers": [{"nameIdentifier": "110748", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Gao, Tian"}], "nameIdentifiers": [{"nameIdentifier": "110749", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2021-02-22"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "16K18716_07.pdf", "filesize": [{"value": "22.8 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensefree": "© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).", "licensetype": "license_note", "mimetype": "application/pdf", "size": 22800000.0, "url": {"label": "16K18716_07.pdf", "url": "https://soar-ir.repo.nii.ac.jp/record/21961/files/16K18716_07.pdf"}, "version_id": "e145c6e0-23b4-4275-a18c-f702da9adc58"}]}, "item_keyword": {"attribute_name": "キーワード", "attribute_value_mlt": [{"subitem_subject": "forest resource measurement", "subitem_subject_scheme": "Other"}, {"subitem_subject": "airborne laser scanning", "subitem_subject_scheme": "Other"}, {"subitem_subject": "RGB imagery", "subitem_subject_scheme": "Other"}, {"subitem_subject": "individual tree detection", "subitem_subject_scheme": "Other"}, {"subitem_subject": "tree crown-based classification", "subitem_subject_scheme": "Other"}, {"subitem_subject": "machine learning approaches", "subitem_subject_scheme": "Other"}]}, "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": "Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms", "subitem_title_language": "en"}]}, "item_type_id": "6", "owner": "1", "path": ["1850"], "permalink_uri": "http://hdl.handle.net/10091/00022716", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2021-02-22"}, "publish_date": "2021-02-22", "publish_status": "0", "recid": "21961", "relation": {}, "relation_version_is_last": true, "title": ["Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms"], "weko_shared_id": -1}