{"_buckets": {"deposit": "d4d25669-c6bc-41f1-862d-f9068c174dca"}, "_deposit": {"id": "18463", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "18463"}, "status": "published"}, "_oai": {"id": "oai:soar-ir.repo.nii.ac.jp:00018463", "sets": ["1016:1017"]}, "author_link": ["51423", "51424", "51425", "51426", "51427"], "item_1628147817048": {"attribute_name": "\u51fa\u7248\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_6_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2014-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "1", "bibliographicPageEnd": "110", "bibliographicPageStart": "87", "bibliographicVolumeNumber": "6", "bibliographic_titles": [{"bibliographic_title": "REMOTE SENSING"}]}]}, "item_6_description_20": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "This study attempted to measure forest resources at the individual tree level using high-resolution images by combining GPS, RS, and Geographic Information System (GIS) technologies. The images were acquired by the WorldView-2 satellite with a resolution of 0.5 m in the panchromatic band and 2.0 m in the multispectral bands. Field data of 90 plots were used to verify the interpreted accuracy. The tops of trees in three groups, namely 10 cm, 15 cm, and 20 cm DBH (diameter at breast height), were extracted by the individual tree crown (ITC) approach using filters with moving windows of 3 x 3 pixels, 5 x 5 pixels and 7 x 7 pixels, respectively. In the study area, there were 1,203,970 trees of DBH over 10 cm, and the interpreted accuracy was 73.68 +/- 15.14% averaged over the 90 plots. The numbers of the trees that were 15 cm and 20 cm DBH were 727,887 and 548,919, with an average accuracy of 68.74 +/- 17.21% and 71.92 +/- 18.03%, respectively. The pixel-based classification showed that the classified accuracies of the 16 classes obtained using the eight multispectral bands were higher than those obtained using only the four standard bands. The increments ranged from 0.1% for the water class to 17.0% for Metasequoia glyptostroboides, with an average value of 4.8% for the 16 classes. In addition, to overcome the mixed pixels problem, a crown-based supervised classification, which can improve the classified accuracy of both dominant species and smaller classes, was used for generating a thematic map of tree species. The improvements of the crown- to pixel-based classification ranged from -1.6% for the open forest class to 34.3% for Metasequoia glyptostroboides, with an average value of 20.3% for the 10 classes. All tree tops were then annotated with the species attributes from the map, and a tree count of different species indicated that the forest of Purple Mountain is mainly dominated by Quercus acutissima, Liquidambar formosana and Pinus massoniana. The findings from this study lead to the recommendation of using the crown-based instead of the pixel-based classification approach in classifying mixed forests.", "subitem_description_type": "Abstract"}]}, "item_6_description_30": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7\uff08\u30b3\u30f3\u30c6\u30f3\u30c4\u306e\u7a2e\u985e\uff09", "attribute_value_mlt": [{"subitem_description": "Article", "subitem_description_type": "Other"}]}, "item_6_description_5": {"attribute_name": "\u5f15\u7528", "attribute_value_mlt": [{"subitem_description": "REMOTE SENSING. 6(1):87-110 (2014)", "subitem_description_type": "Other"}]}, "item_6_link_3": {"attribute_name": "\u4fe1\u5dde\u5927\u5b66\u7814\u7a76\u8005\u7dcf\u89a7\u3078\u306e\u30ea\u30f3\u30af", "attribute_value_mlt": [{"subitem_link_text": "Katoh, Masato", "subitem_link_url": "http://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=000335555900005"}]}, "item_6_publisher_4": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "MDPI AG"}]}, "item_6_relation_48": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_name": [{"subitem_relation_name_text": "10.3390/rs6010087"}], "subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.3390/rs6010087", "subitem_relation_type_select": "DOI"}}]}, "item_6_rights_62": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "\u00a9 2013 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 license (http://creativecommons.org/licenses/by/3.0/)."}]}, "item_6_select_64": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "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": "3S technology; forest resource measurement; individual tree crown approach; object-based classification; Purple Mountain"}]}, "item_6_text_70": {"attribute_name": "wosonly keywords", "attribute_value_mlt": [{"subitem_text_value": "SPATIAL-RESOLUTION IMAGERY; SPECIES CLASSIFICATION; CROWN DETECTION; SATELLITE DATA; DELINEATION; BIOMASS; STANDS; AREA"}]}, "item_6_textarea_68": {"attribute_name": "wosonly abstract", "attribute_value_mlt": [{"subitem_textarea_value": "This study attempted to measure forest resources at the individual tree level using high-resolution images by combining GPS, RS, and Geographic Information System (GIS) technologies. The images were acquired by the WorldView-2 satellite with a resolution of 0.5 m in the panchromatic band and 2.0 m in the multispectral bands. Field data of 90 plots were used to verify the interpreted accuracy. The tops of trees in three groups, namely 10 cm, 15 cm, and 20 cm DBH (diameter at breast height), were extracted by the individual tree crown (ITC) approach using filters with moving windows of 3 x 3 pixels, 5 x 5 pixels and 7 x 7 pixels, respectively. In the study area, there were 1,203,970 trees of DBH over 10 cm, and the interpreted accuracy was 73.68 +/- 15.14% averaged over the 90 plots. The numbers of the trees that were 15 cm and 20 cm DBH were 727,887 and 548,919, with an average accuracy of 68.74 +/- 17.21% and 71.92 +/- 18.03%, respectively. The pixel-based classification showed that the classified accuracies of the 16 classes obtained using the eight multispectral bands were higher than those obtained using only the four standard bands. The increments ranged from 0.1% for the water class to 17.0% for Metasequoia glyptostroboides, with an average value of 4.8% for the 16 classes. In addition, to overcome the mixed pixels problem, a crown-based supervised classification, which can improve the classified accuracy of both dominant species and smaller classes, was used for generating a thematic map of tree species. The improvements of the crown- to pixel-based classification ranged from -1.6% for the open forest class to 34.3% for Metasequoia glyptostroboides, with an average value of 20.3% for the 10 classes. All tree tops were then annotated with the species attributes from the map, and a tree count of different species indicated that the forest of Purple Mountain is mainly dominated by Quercus acutissima, Liquidambar formosana and Pinus massoniana. 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