| Project/Area Number |
17K08188
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Environmental agriculture(including landscape science)
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| Research Institution | Ishikawa Prefectural University |
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Principal Investigator |
Ueno Yusuke 石川県立大学, 生物資源環境学部, 准教授 (90638818)
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| Project Period (FY) |
2017-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | ドローン / 空間生態学 / 水田生態系 / 景観生態学 / SfM / UAV / 水田生物 / 水路ネットワーク / リモートセンシング |
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| Outline of Final Research Achievements |
This research is a study of basic technology for more precisely understanding the distribution patterns of organisms by supplementing the spatial gap between existing basic information on national land (GIS information such as vegetation maps and topographic maps) and environmental data from field measurements with aerial drone photography data. In particular, using frogs and grasshoppers, which are indicator organisms of the paddy field environment, as model organisms, the objective was to examine the effects of differences in spatial scale and resolution of natural environmental information on the accuracy of predicting the distribution patterns of organisms. In the survey, 3D measurements of Satoyama landscape were made using aerial images taken continuously by a drone and SfM (Structure from Motion) technology. The distribution patterns of indicator organisms in the paddy fields and their factors were also investigated.
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| Academic Significance and Societal Importance of the Research Achievements |
高解像度なカメラを搭載したドローンが普及し、SfM（Structure from Motion）の技術によって複数枚の写真から3次元形状を復元することが可能になっている。一方で、既存の国土に関する基盤情報（植生図、地形図などのGIS情報）と現地計測の間には、環境データの空間ギャップが存在する。本研究は、特に、水田環境の指標生物であり、生態的特性が異なるカエルとバッタなどをモデル生物とし、自然環境情報の空間ギャップと空間スケール、解像度の違いが、生物の分布パターンの予測精度に及ぼす影響を検討している。
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