Developing objective methods for evaluating the effects of silvicultural treatments on carbon sequestration and stock in plantation forests
| Project/Area Number |
23380085
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Forest science
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| Research Institution | Kobe University |
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Principal Investigator |
ISHII Hiroaki 神戸大学, (連合)農学研究科(研究院), 准教授 (50346251)
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| Co-Investigator(Kenkyū-buntansha) |
ENOKI Tsutomu 九州大学, 農学研究院, 准教授 (10305188)
SHIROTA Tetsuou 信州大学, 農学部, 助教 (10374711)
UMEKI Kiyoshi 千葉大学, 大学院・園芸学研究科, 准教授 (50376365)
KATO Akira 千葉大学, 大学院・園芸学研究科, 助教 (70543437)
OHSAWA Akira 京都大学, 地球環境学堂, 教授 (90288647)
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| Project Period (FY) |
2011-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥18,330,000 (Direct Cost: ¥14,100,000、Indirect Cost: ¥4,230,000)
Fiscal Year 2013: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2012: ¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
Fiscal Year 2011: ¥8,710,000 (Direct Cost: ¥6,700,000、Indirect Cost: ¥2,010,000)
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| Keywords | 森林構造 / 林分構造 / 樹形 / レーザー測量 / ＬｉＤＡＲ / 現存量 / 樹冠フラクタル係数 / 測量 |
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| Research Abstract |
We developed methods to obtain parpameters for estimating stand productivity from 3D data obtained by ground-based LiDAR. To estimate tree dimensions from LiDar data, we applied the neural network algorithm. We were able to estimate DBH with < 2m error and tree height with < 50cm error. The algorithm distinguished 95% of the stems in the stand, on average. This meant that error levels for ground-based LiDAR were similar to that of human workers. Thus, LiDAR could serve as a objective method for forest mensuration without introducing human error.
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Report
(4 results)
Research Products
(31 results)
