Estimating three-dimensional shape of 40-year-old evergreen using terrestrial laser scanner and a voxel-based Three-dimensional point cloud data can be extracted using a terrestrial laser scanner, and three-dimensional forest information is applicable to various forestry studies. It is required that the estimation of a three-dimensional structure is accurate enough. Here, a voxel-based data processing seems to be an effective method because it divides space using the virtual cube with reduced data. In this study, we attempt to estimate a three-dimensional structure in evergreen coniferous species in a mature stand. Measurements were conducted using a major species of the boreal forest, Abies sachalinensis , in a 40-year-old plantation site. Using point cloud data obtained by terrestrial laser scanner, the vertical trend of trunk diameter was firstly measured, and then the stem volume and branch-leaf biomass at every 2 m layer were estimated using a voxel-based method with eight kinds of candidate size options. They were compared to the actual data acquired after artificial felling of trees. Trunk diameter was successfully estimated from ground to tree height of more than 60%, showing an RMSE of 2.30 cm. It is also demonstrated that error tends to increase as the measurement position gets higher. Stem volume represented high correlation with the manually measured values, except for the case that uses a maximum size of voxel size（0.2 m）.Especially, the voxel size of 0.08 m
{"title":"Estimating three-dimensional tree shape of 40-year-old evergreen conifer using terrestrial laser scanner and a voxel-based analysis","authors":"Masuto Ebina, W. Ishizuka, T. Abe","doi":"10.20659/jjfp.54.1_13","DOIUrl":"https://doi.org/10.20659/jjfp.54.1_13","url":null,"abstract":"Estimating three-dimensional shape of 40-year-old evergreen using terrestrial laser scanner and a voxel-based Three-dimensional point cloud data can be extracted using a terrestrial laser scanner, and three-dimensional forest information is applicable to various forestry studies. It is required that the estimation of a three-dimensional structure is accurate enough. Here, a voxel-based data processing seems to be an effective method because it divides space using the virtual cube with reduced data. In this study, we attempt to estimate a three-dimensional structure in evergreen coniferous species in a mature stand. Measurements were conducted using a major species of the boreal forest, Abies sachalinensis , in a 40-year-old plantation site. Using point cloud data obtained by terrestrial laser scanner, the vertical trend of trunk diameter was firstly measured, and then the stem volume and branch-leaf biomass at every 2 m layer were estimated using a voxel-based method with eight kinds of candidate size options. They were compared to the actual data acquired after artificial felling of trees. Trunk diameter was successfully estimated from ground to tree height of more than 60%, showing an RMSE of 2.30 cm. It is also demonstrated that error tends to increase as the measurement position gets higher. Stem volume represented high correlation with the manually measured values, except for the case that uses a maximum size of voxel size（0.2 m）.Especially, the voxel size of 0.08 m","PeriodicalId":234210,"journal":{"name":"Japanese Journal of Forest Planning","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132154255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
2019年8月27日(火)から29日(木)にかけて,北海道 札幌市道民活動センター「かでる2.7」および富良野 市東京大学演習林,千歳支笏地域の国有林において, 国際共同シンポジウム「Sustainable Forest Ecosystem Management2019」が開催された。 主催は森林計画学会。共催はKorea Society of Forest Management and Information,Taiwan Society of Forest Ecosystem Management,Chinese Forest Association,FORMATH Research Group,東京大学 大学院農学生命科学研究科附属演習林北海道演習林, 森林 GIS フォーラムである。参加者は合計50名であ り,日本24名,台湾13名,韓国10名,フィリピン3名 という内訳となった。 初日は「かでる2.7」にて口頭発表12件(表-1), ポスター発表18件(表-2)が行われた。また発表終 了後,懇親会が催された。翌28日・29日はエクスカー ションが実施され,東京大学演習林および千歳支笏地 域の国有林で見学を行った。 今回の国際共同シンポジウムは,日本と台湾との研 究交流が図られた2006年から2008年の森林計画学会夏 期セミナー以来の研究交流活動の一環であり,今回で 8回目の開催となる。
2019年8月27日(星期二)至29日(星期四)，将在北海道札幌市道民活动中心“shor2.7”及富良野市东京大学演习林、千岁支笏地区国有林举行。国际共同研讨会“able Forest Ecosystem Management2019年”在此召开。主办单位是森林计划学会。协办单位:Korea Society of Forest Management and Information,Taiwan Society of Forest EcosystemManagement,Chinese Forest Association,FORMATH Research Group，东京大学大学院农学生命科学研究科附属演习林北海道演习林，这就是森林GIS论坛。参加者共计50人，日本24人，台湾13人，韩国10人，菲律宾3人。首日在“kador2.7”进行了口头发表12件(表-1)，海报发表18件(表-2)。发表完毕后，又举行了联欢会。翌日28日、29日举行展览，参观了东京大学演习林及千岁支笏地区的国有林。此次国际共同研讨会是日本与台湾自2006年至2008年森林计划学会夏季研讨会以来的研究交流活动之一，此次是第8次举办。
{"title":"Report on SFEM 2019: The International Symposium of Sustainable Forest Ecosystem Management- Accelerating Innovation for Multiple Ecosystem Services","authors":"Tatsuki Yoshii, N. Matsumura","doi":"10.20659/jjfp.53.2_103","DOIUrl":"https://doi.org/10.20659/jjfp.53.2_103","url":null,"abstract":"2019年8月27日(火)から29日(木)にかけて,北海道 札幌市道民活動センター「かでる2.7」および富良野 市東京大学演習林,千歳支笏地域の国有林において, 国際共同シンポジウム「Sustainable Forest Ecosystem Management2019」が開催された。 主催は森林計画学会。共催はKorea Society of Forest Management and Information,Taiwan Society of Forest Ecosystem Management,Chinese Forest Association,FORMATH Research Group,東京大学 大学院農学生命科学研究科附属演習林北海道演習林, 森林 GIS フォーラムである。参加者は合計50名であ り,日本24名,台湾13名,韓国10名,フィリピン3名 という内訳となった。 初日は「かでる2.7」にて口頭発表12件(表-1), ポスター発表18件(表-2)が行われた。また発表終 了後,懇親会が催された。翌28日・29日はエクスカー ションが実施され,東京大学演習林および千歳支笏地 域の国有林で見学を行った。 今回の国際共同シンポジウムは,日本と台湾との研 究交流が図られた2006年から2008年の森林計画学会夏 期セミナー以来の研究交流活動の一環であり,今回で 8回目の開催となる。","PeriodicalId":234210,"journal":{"name":"Japanese Journal of Forest Planning","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129192300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
53: 43~52, The aim of this study was to identify tree species based on venation patterns of leaf images,which were photographed with a mobile device in the outdoors. Forty leaves (10 species) collected at the Kyoto University and Kyoto Prefectural University Campus were used as samples in this study. Seven learning patterns were determined from the differences in photography methods and conditions. The venation patterns were evaluated by histograms of oriented gradients (HOG). Two decision-tree algorithms (J48, RandomForest), a lazy learning (IBk) and a neural network (MultilayerPerceptron) were used for machine-learning classification. A performance evaluation of the proposed model was performed with Matthews correlation coefficient ( MCC ) and correct answer rate. The classification accuracy for test data was verified by the 10-fold cross-validation method. Every learning pattern resulted in classification accuracy for training data; however, the classification accuracy for test data varied greatly according to the difference in learning patterns. By considering camera-to-subject distance, the angle at which subjects were photographed, and the light environment, high classification accuracy could be obtained from the leaf images, which were photographed with a mobile device.
{"title":"Tree species identification based on venation patterns of leaf images photographed with a mobile device in the outdoors","authors":"Y. Minowa, Keita Asao","doi":"10.20659/jjfp.53.2_43","DOIUrl":"https://doi.org/10.20659/jjfp.53.2_43","url":null,"abstract":"53: 43~52, The aim of this study was to identify tree species based on venation patterns of leaf images,which were photographed with a mobile device in the outdoors. Forty leaves (10 species) collected at the Kyoto University and Kyoto Prefectural University Campus were used as samples in this study. Seven learning patterns were determined from the differences in photography methods and conditions. The venation patterns were evaluated by histograms of oriented gradients (HOG). Two decision-tree algorithms (J48, RandomForest), a lazy learning (IBk) and a neural network (MultilayerPerceptron) were used for machine-learning classification. A performance evaluation of the proposed model was performed with Matthews correlation coefficient ( MCC ) and correct answer rate. The classification accuracy for test data was verified by the 10-fold cross-validation method. Every learning pattern resulted in classification accuracy for training data; however, the classification accuracy for test data varied greatly according to the difference in learning patterns. By considering camera-to-subject distance, the angle at which subjects were photographed, and the light environment, high classification accuracy could be obtained from the leaf images, which were photographed with a mobile device.","PeriodicalId":234210,"journal":{"name":"Japanese Journal of Forest Planning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128651596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
*連絡先(Corresponding author)E-mail : nakagawa-masahiko@hro.or.jp 1 北海道立総合研究機構林業試験場(079-0198 北海道美唄市光珠内) Forestry Research Institute, Hokkaido Research Organization, Koshunai, Bibai-shi, Hokkaido079-0198,Japan 2 九州大学農学部附属演習林(089-3705 北海道足寄郡足寄町) Ashoro Research Forest, Department of Agriculture, Kyushu University, Ashoro-cho, Ashoro-gun, Hokkaido089-3705,Japan 中川昌彦・田代直明:表土除去がカラマツの初期成長の推移に与える影響,森林計画誌53:63~68,2020 表 土除去がカラマツの初期成長の推移に与える影響の調査を行った。北海道東神楽町の試験地では,植栽8年後 の樹高,胸高直径は,表土を全く除去していない草刈地拵区と表土除去区の間に有意な違いはみられなかった。 北海道足寄町の試験地では,植栽5年後の樹高,胸高直径は,表土浅剥区で一番大きく,次いで表土を移動さ せてない草刈地拵区が大きく,表土深剥区で一番小さかった。これまで,表土を除去することでカラマツの初 期成長が大きく低下すると報告されていたが,本研究の結果から,表土除去を行っても植栽5~8年後のカラ マツの成長が低下することはない場合や,よくなる場合もあると考えられた。しかし,土壌を深く剥いで,植 物の根が成育できる有効土層が極端に薄くなると,初期成長が大きく劣ると考えられた。 キーワード:カラマツ,表土除去,成長,土壌
联系方式(Corresponding author)电子邮箱:nakagawa-masahiko@hro.or.jp 1北海道立综合研究机构林业试验场(079-0198北海道美歌市光珠内)Forestry Research Institute，Hokkaido Research Organization, Koshunai, Bibai-shi，Hokkaido079-0198,Japan 2九州大学农学院附属训练林(089-3705北海道足寄郡足寄町)Ashoro Research Forest，Department of Agriculture, Kyushu University, Ashoro-cho, Ashoro-gun，Hokkaido089-3705,Japan中川昌彦·田代直明:去除表土对落叶松初期成长趋势的影响，森林计划志53:63~68,202表进行了去除泥土对落叶松初期成长推移的影响的调查。在北海道东神乐町的试验地，栽植8年后的树高、胸高直径在完全没有除去表土的割草地制作区和表土除去区之间没有显著差异。在北海道足寄町的试验地，栽植5年后的树高、胸高直径，表土浅剥区最大，其次是没有移动表土的草割地做区最大，表土深剥区最小。此前有报道称，去除表土会使落叶松的初期生长大幅下降，但从本研究的结果来看，即使去除表土，栽植5~8年后的结果也是如此。松树的生长有时不会下降，有时会好转。但是，如果把土壤剥得太深，植物的根能够成长的有效土层变得非常薄的话，初期的成长就会大大下降。关键词:落叶松，去除表层土壤，生长，土壤
{"title":"Effect of top soil removal on transition of initial growth of Japanese larch","authors":"M. Nakagawa, Naoaki Tashiro","doi":"10.20659/jjfp.53.2_63","DOIUrl":"https://doi.org/10.20659/jjfp.53.2_63","url":null,"abstract":"*連絡先(Corresponding author)E-mail : nakagawa-masahiko@hro.or.jp 1 北海道立総合研究機構林業試験場(079-0198 北海道美唄市光珠内) Forestry Research Institute, Hokkaido Research Organization, Koshunai, Bibai-shi, Hokkaido079-0198,Japan 2 九州大学農学部附属演習林(089-3705 北海道足寄郡足寄町) Ashoro Research Forest, Department of Agriculture, Kyushu University, Ashoro-cho, Ashoro-gun, Hokkaido089-3705,Japan 中川昌彦・田代直明:表土除去がカラマツの初期成長の推移に与える影響,森林計画誌53:63~68,2020 表 土除去がカラマツの初期成長の推移に与える影響の調査を行った。北海道東神楽町の試験地では,植栽8年後 の樹高,胸高直径は,表土を全く除去していない草刈地拵区と表土除去区の間に有意な違いはみられなかった。 北海道足寄町の試験地では,植栽5年後の樹高,胸高直径は,表土浅剥区で一番大きく,次いで表土を移動さ せてない草刈地拵区が大きく,表土深剥区で一番小さかった。これまで,表土を除去することでカラマツの初 期成長が大きく低下すると報告されていたが,本研究の結果から,表土除去を行っても植栽5~8年後のカラ マツの成長が低下することはない場合や,よくなる場合もあると考えられた。しかし,土壌を深く剥いで,植 物の根が成育できる有効土層が極端に薄くなると,初期成長が大きく劣ると考えられた。 キーワード:カラマツ,表土除去,成長,土壌","PeriodicalId":234210,"journal":{"name":"Japanese Journal of Forest Planning","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122316864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"If Sakhalin spruce or Sakhalin fir die in windbreaks on seasonally frozen soils, what species can be recommended?","authors":"M. Nakagawa","doi":"10.20659/jjfp.53.2_81","DOIUrl":"https://doi.org/10.20659/jjfp.53.2_81","url":null,"abstract":"","PeriodicalId":234210,"journal":{"name":"Japanese Journal of Forest Planning","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122901872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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