| Project/Area Number |
16K18716
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Forest science
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| Research Institution | Shinshu University |
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Principal Investigator |
DENG SONGQIU 信州大学, 先鋭領域融合研究群山岳科学研究所, 研究員 (00772477)
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| Research Collaborator |
Yu Xiaowei
Gao Tian
Liang Xinlian
Wang Yunsheng
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2018: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2017: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2016: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | 森林計測 / リモートセンシング / 航空レーザ / 高分解能画像 / 精密樹冠抽出 / 樹種分類 / ドローンレーザ / ドローン画像 / 航空レーザー / 3D樹冠抽出 / Forest Measurement / Airborne Laser Scanning / Multispectral Imagery / Single Tree Delineation / Species Classification |
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| Outline of Final Research Achievements |
An accurate tree crown detection method was developed using airborne and drone laser scanning data. Then, we established a tree species classification method at the individual tree level by combining laser data and high resolution aerial images. In coniferous forests, the accuracies of single tree extraction and tree species classification are both more than 90%, achieving the target of practical use level. Furthermore, we have developed a method for estimating forest biomass at the stand level using single-tree crown information of different species at the large scale. These research results were published in international English journals with open access, and our oral presentations at the international specialized conferences were highly acclaimed.
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| Academic Significance and Societal Importance of the Research Achievements |
3D情報を持つレーザデータから開発した高精度な単木樹冠抽出手法、樹種分類方法とバイオマス推定方法を用いて、森林調査をせずに森林資源現況とバイオマスが客観的かつ広域的に推定・区分できることから、多大な労力と費用を要している森林調査を基本にした資源把握が効率的になり、コストの削減効果が大きい。解析精度が実用化レベルに達成したため、日本の林業成長産業化に貢献できる。また、日本の森林だけではなく、人工林面積が増えている中国、熱帯広葉樹林の東南アジア、針葉樹天然林の広がる北米など諸外国の森林にも応用可能なことから、国際共同研究に貢献できる。
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