| 研究課題/領域番号 |
22K18055
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分64040:自然共生システム関連
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| 研究機関 | 総合地球環境学研究所 |
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研究代表者 |
NguyenTien Hoang 総合地球環境学研究所, 研究部, 客員助教 (20829379)
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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,550千円 (直接経費: 3,500千円、間接経費: 1,050千円)
2024年度: 650千円 (直接経費: 500千円、間接経費: 150千円)
2023年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
2022年度: 2,080千円 (直接経費: 1,600千円、間接経費: 480千円)
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| キーワード | Machine learning / Remote sensing / Ecological modeling |
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| 研究開始時の研究の概要 |
This research will develop a machine learning-based species distribution model based on multiple remotely sensed data sources. The results will enrich the understanding of habitat characteristics and contribute to conservation planning of critically endangered mammals.
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| 研究実績の概要 |
The forest cover in the study area has recently been restored, primarily through the planting of non-native trees. Unfortunately, these planted forests do not provide suitable habitats for many wild animals. Using mapping methods from remote sensing data, we were able to identify fragmented natural forest ecosystems and distinguish old-growth natural forests from monoculture plantations. The accuracy of the maps is verified using ground-truth data and official forest inventory records. Additionally, methods for employing UAVs to monitor mammals in tropical forests are being refined and developed. The data collected and forest ecosystem maps will enable more precise delineation of the distribution areas for critically endangered mammal species.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The maps developed are highly accurate, and necessary data for modeling continues to be collected.
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| 今後の研究の推進方策 |
Different machine learning models will be applied and tested for accuracy in predicting the distribution of critically endangered mammals.
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