研究課題/領域番号 |
18H06496
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配分区分 | 補助金 |
研究機関 | 京都大学 |
研究代表者 |
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研究期間 (年度) |
2018-08-24 – 2020-03-31
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キーワード | Ecological modeling / Machine learning / Remote sensing / Spatial analysis / Wildlife corridor |
研究実績の概要 |
The meetings for community-based mapping with local people were conducted in main villages in the targeted critically endangered mammal landscape in Central Vietnam. The indirect observation data of the species distribution were collected thoroughly by using participatory GIS. The detailed base maps of the study area were acquired. By overlaying the community-based mapping data with the high-resolution layers of environmental variables, habitat characteristics and distribution determinants of the species were identified. The preliminary results show that several isolated forest fragments on the mountainous area of Hue-Quang Nam landscape and forests along the border between Central Vietnam and Laos had the highest potential for the distribution of the targeted critically endangered mammal.
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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
Although this project was approved late last year, the field survey was conducted as scheduled. The input data were collected and processed steadily and promptly. The proposed high-resolution species distribution model has been partly developed. Some of the results were presented in an international symposium. Based on these achievements, it can be concluded that the project progress has been made well in general.
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今後の研究の推進方策 |
The proposed high-resolution species distribution model will be fully developed. The resultant model will be used to predict the spatial species distribution of the targeted critically endangered mammal. This project will facilitate the identification of existing and potential biological corridors between protected areas and areas prioritized for landscape conservation. I will present and publish the results at a reputable international conference and peer-review scientific journal. I also collaborate with local stakeholders to develop solutions and policies for biodiversity conservation.
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