| Project/Area Number |
22K18055
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 64040:Social-ecological systems-related
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| Research Institution | Research Institute for Humanity and Nature |
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Principal Investigator |
NguyenTien Hoang 総合地球環境学研究所, 研究部, 客員助教 (20829379)
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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2024: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2023: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2022: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | Machine learning / Remote sensing / Ecological modeling |
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| Outline of Research at the Start |
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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| Outline of Annual Research Achievements |
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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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The maps developed are highly accurate, and necessary data for modeling continues to be collected.
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| Strategy for Future Research Activity |
Different machine learning models will be applied and tested for accuracy in predicting the distribution of critically endangered mammals.
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