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J-GLOBAL ID：202101004516560192   Update date: Mar. 24, 2025

HIROSHIMA Takuya

ヒロシマ タクヤ | HIROSHIMA Takuya
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Affiliation and department：
The University of Tokyo Graduate School of Agricultural and Life Sciences, Global Agricultural Sciences

About The University of Tokyo Graduate School of Agricultural and Life Sciences, Global Agricultural Sciences


Research field  (2)： Environmental policy and society ,  Forest science
Research keywords  (10)： forest carbon credit ,  carbon offset ,  forest carbon sink ,  survival analysis ,  timber yield prediction ,  growth prediction ,  timber production ,  GIS ,  forest management ,  forest planning
Research theme for competitive and other funds  (9)：
  • 2019 - 2023 Harvesting tree selection based on age structure and survival analysis of natural forest
  • 2018 - 2023 Quantitative reconstruction of long-term growth process for old Sugi plantation stands using historical forest management records and past aerial photos
  • 2018 - 2022 Assessment of effective forest management to reduce driftwood disasters based on analyses of long-term water and sediment discharge using a distributed process-oriented model
  • 2016 - 2019 Calculation of thinning volume considering cascade use at prefecture level
  • 2013 - 2016 Developing harvest prediction model in a national scale by adding up prefectural final cutting and utilization thinning volumes
Show all
Papers (61)：
  • Hantao Li, Xiaoxuan Li, Tomomichi Kato, Shinya Inukai, Takuya Hiroshima. National-scale calibrated GEDI AGBD models for effective assessment of growth conditions across forest strata. Forest Ecology and Management. 2025. 585. 122657-122657
  • Jeyavanan Karthigesu, Toshiaki Owari, Satoshi Tsuyuki, Takuya Hiroshima. Improving the Individual Tree Parameters Estimation of a Complex Mixed Conifer-Broadleaf Forest Using a Combination of Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle RGB and Multispectral Imagery. Geomatics. 2025. 5. 1. 12-12
  • Win Sithu Maung, Satoshi Tsuyuki, Takuya Hiroshima, San San Htay. Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing data. Discover Conservation. 2025. 2. 1
  • Huiqing Pei, Toshiaki Owari, Satoshi Tsuyuki, Takuya Hiroshima, Danfeng Hong. Combined gated graph convolution neural networks with multi-modal geospatial data for forest type classification. International Journal of Applied Earth Observation and Geoinformation. 2025. 136. 104372-104372
  • Mary M. McClure, Satoshi Tsuyuki, Takuya Hiroshima. Integration of Structural Characteristics from GEDI Waveforms for Improved Forest Type Classification. Remote Sensing. 2024. 16. 24. 4776-4776
ｍore...
MISC (21)：
  • 広嶋卓也. 書評:梶原幹弘 著『植栽による択伐林で日本の森林改善-樹冠の働きと量から考える-』. 林業経済. 2021. 73. 10. 25-29
  • 笠原久臣, 広嶋卓也, 岡村行治, 犬飼浩, 福士憲司, 松井理生. 東京大学北海道演習林が生産する優良丸太の銘木販売. 北方林業. 2020. 71. 3. 91-95
  • 広嶋卓也. 林分施業法のこれまでとこれから. 北方林業. 2020. 71. 3. 87-90
  • Hiroshima Takuya, Nakajima Tohru. Mid-to-long term prediction of commercial thinning volumes in 47 prefectures. The Japanese Forest Society Congress. 2019. 130. 687
  • 廣嶋 卓也, 中島 徹. 都道府県別 間伐材生産量の中・長期的予測. 日本森林学会大会学術講演集. 2019. 130. 75-75
ｍore...
Work history (2)：
  • 2021/01 - 現在 The University of Tokyo Graduate School of Agricultural and Life Sciences, Department of Global Agricultural Sciences associate professor
  • 2007/04 - 2020/12 The University of Tokyo The University of Tokyo Forests, Graduate School of Agricultural and Life Sciences lecturer
Committee career (11)：
  • 2017/04 - 現在 カーボンオフセット協会 第三者認証プログラム運営委員会委員
  • 2016/04 - 2018/03 愛知県瀬戸市 環境審議会委員
  • 2016/04 - 2018/03 愛知県犬山市 環境審議会委員
  • 2016/04 - 2018/03 愛知県瀬戸市 せと環境塾運営協議会委員
  • 2016/04 - 2018/03 環境省 モニタリング1000・コアサイト代表者(愛知赤津サイト)
Show all
Awards (3)：
  • 2020/03 - Japan Society of Forest Planning Japan Society of Forest Planning Award
  • 2012/03 - The Japanese Forest Society Journal of Forest Research Award
  • 2007/03 - Japan Society of Forest Planning Japan Society of Forest Planning Young Investigator Award
※ Researcher’s information displayed in J-GLOBAL is based on the information registered in researchmap. For details, see here.

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