Development of a machine learning-based species distribution model and its application to conservation planning of critically endangered mammal
Project/Area Number |
22K18055
|
Research Category |
Grant-in-Aid for Early-Career Scientists
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Allocation Type | Multi-year Fund |
Review Section |
Basic Section 64040:Social-ecological systems-related
|
Research Institution | Research Institute for Humanity and Nature |
Principal Investigator |
NguyenTien Hoang 総合地球環境学研究所, 研究部, 特任助教 (20829379)
|
Project Period (FY) |
2022-04-01 – 2025-03-31
|
Project Status |
Granted (Fiscal Year 2022)
|
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)
|
Keywords | Machine learning / Remote sensing / Ecological modeling |
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 |
During the initial year, I analyzed satellite imagery to create a map of land use changes and deforestation patterns in the study area over the past decade. Furthermore, various machine learning techniques have been evaluated to predict the locations where local people typically harvest forest resources. These results, which were presented at the NERPS Conference and the General Meeting of the AJG, serve to pinpoint intact forests where rare species are likely to exist. Additionally, I conducted a survey to collect ground truth data to enhance forest mapping accuracy and assess the applicability of thermal sensing with drone technology for wildlife monitoring.
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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
Despite some deviations from the initial plan, I believe the research is generally progressing smoothly.
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Strategy for Future Research Activity |
I intend to move forward with the research as initially planned.
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Report
(1 results)
Research Products
(2 results)