The Construction of a Mountain Vegetation Knowledge Graph Incorporating With Geographical Principles, Maps, and Remote Sensing Images
文献类型：期刊论文
| 作者 | Yao, Yonghui1; Liu, Yulian1,2 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2024-12-01 |
| 卷号 | 62页码:4416215 |
| 关键词 | Vegetation mapping Knowledge graphs Spatiotemporal phenomena Geoscience Remote sensing Data models Ontologies Data mining Semantics Geography Climate change Deep learning geographical principles geoscience knowledge graph (GKG) remote sensing (RS) spatiotemporal information vegetation distribution |
| DOI | 10.1109/TGRS.2024.3493455 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | A great deal of geoscience knowledge exists in the form of unstructured text or maps, which are difficult to use by structured models or to process by computers. Thus, it is urgent to transform them to structured knowledge graph (KG). However, the development of geoscience KG (GKG) lags behind the general KG because it involves in the complexity of spatiotemporal relationships and knowledge from multisources. This study constructed a mountain vegetation KG (MVKG) incorporating with vegetation geographical principles, maps, and remote sensing (RS) images with the support of ArcGIS and deep learning method to facilitate the use of vegetation knowledge in various disciplines. The results showed that: 1) for the construction of a GKG, such as the MVKG, it is first necessary to define a strict and compatible ontology to classify and organize all the knowledge in order to facilitate structured representation and storage of them; 2) the MVKG entities were labeled from vegetation maps with the support of ArcGIS, which indicated that the spatiotemporal representation, organization, and analysis techniques of GIS can effectively support the construction of the GKG; 3) the RS image features extracted by the deep learning method were embedded into the properties of the MVKG entities, which will be significant for the MVKG application because RS monitoring is indispensable for the study of vegetation distribution and changes. The MVKG can also enhance the application of vegetation knowledge and information in RS monitoring for vegetation cover and change, mountain ecology, and climate change. |
| WOS关键词 | LARGE-SCALE ; CLIMATE ; CO2 |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001360785900018 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210494]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Yao, Yonghui |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 101408, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yao, Yonghui,Liu, Yulian. The Construction of a Mountain Vegetation Knowledge Graph Incorporating With Geographical Principles, Maps, and Remote Sensing Images[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:4416215. |
| APA | Yao, Yonghui,&Liu, Yulian.(2024).The Construction of a Mountain Vegetation Knowledge Graph Incorporating With Geographical Principles, Maps, and Remote Sensing Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,4416215. |
| MLA | Yao, Yonghui,et al."The Construction of a Mountain Vegetation Knowledge Graph Incorporating With Geographical Principles, Maps, and Remote Sensing Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):4416215. |
入库方式： OAI收割
来源：地理科学与资源研究所
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