2004 Fiscal Year Final Research Report Summary
3D remote sensing of plant community using scanning lidar systems
| Project/Area Number |
13306020
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
生物環境
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| Research Institution | The University of Tokyo |
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Principal Investigator |
OMASA Kenji The University of Tokyo, Graduate School of Agricultural and Life Sciences, Professor, 大学院・農学生命科学研究科, 教授 (70109908)
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| Co-Investigator(Kenkyū-buntansha) |
OKI Kazuo The University of Tokyo, Graduate School of Agricultural and Life Sciences, Lecturer, 大学院・農学生命科学研究科, 講師 (50292628)
SHIMIZU Yo The University of Tokyo, Graduate School of Agricultural and Life Sciences, Assistant, 大学院・農学生命科学研究科, 助手 (00323486)
HASHIMOTO Yasushi Ehime University, Graduate School of Agriculture, Professor emeritus, 農学部, 名誉教授 (30036298)
HONJO Tsuyoshi Chiba University, Faculty of Horticulture, Professor, 園芸学部, 教授 (60173655)
NATORI Toshiki National Institute for Environmental Studies, Environmental Biology Division, Senior researcher, 生態系機構研究室, 主任研究員 (10132854)
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| Project Period (FY) |
2001 – 2004
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| Keywords | Scanning lidar / Forest / Biomass / Tree height / Automatic detection of tree top / Diameter at breast height / Computer graphics / 3-D measurement |
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| Research Abstract |
Forest area was measured 3-dimensionally using helicopter-borne scanning lidar system with high spatial resolution. Accuracy of the lidar system at several fright conditions was examined and flight condition of the system could be optimized. As a result, woody tree height could be estimated accurately with RMSE less than 20 cm. Next, algorithms for automatic detection of tree height and position were examined using the lidar data. A newly designed algorithm (Crown Extraction filtering : CE filtering) was compared with the conventional ones, such as Watershed method and Local maximum filtering(LM filtering). It was shown that tree height was estimated more accurately from Digital Canopy Height Model(DCHM) with tree tops identified by CE filtering after smoothing. As a result, error of identification of tree tops using this method was 4.9 % and the one of tree heights ranged -0.25 to 0.42 m with RMSE of 0.38 m. Moreover, we designed an algorithm to identify outline of each tree and it enabled to map spatial distribution of forest biomass.
On the other hand, understory of trees was measured in forest using a portable scanning lidar system and the diameter at breast height(DBH) of each tree was estimated. From the data of DBH, the forest biomass was estimated. This method enabled to estimate forest biomass without disturbing understory and felling trees. In addition, potable lidar data of trees measured from several points on the ground were merged into one 3D image to complement blind regions. Then, a complete 3D model of several trees was generated by a technique of computer graphics. From the 3D model, several parameters such as tree height, stem diameter, maximum canopy diameter and canopy area in the horizontal cross-section, and canopy volume were computed and the errors were evaluated.
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