Fine Extraction of Plateau Wetlands Based on a Combination of Object-Oriented Machine Learning and Ecological Rules: A Case Study of Dianchi Basin
文献类型：期刊论文
| 作者 | Cai, Fangliang1,2; Tang, Bo-Hui1,2,3; Sima, Ouyang1,2; Chen, Guokun1,2; Zhang, Zhen1,2 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2024 |
| 卷号 | 17页码:5364-5377 |
| 关键词 | Ecological rules Gaofen-2 object-oriented classification random forest wetland extraction |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2024.3356656 |
| 通讯作者 | Tang, Bo-Hui(tangbh@kust.edu.cn) |
| 英文摘要 | Wetland ecosystems are essential to the preservation of biodiversity. Plateau wetland ecosystems are vital components of wetland ecosystems, characterized by diverse wetland types and fragmented land distribution. In the extraction of plateau wetlands, there are such issues as inaccuracy in classification, inadequately fine categories, and difficulty in extracting vegetated wetlands. The aim of this study was to establish a new classification framework for extracting detailed information about plateau wetlands, and the Dianchi Basin was used as the study area. Using Gaofen-2 (GF-2) imagery from 2019 to 2021, land cover information was extracted by applying nearest neighbor classification and random forest classification. Wetlands were then extracted from the land cover data using ecological rule classification, and a detailed wetland map of the Dianchi Basin was obtained in 2020 with a 1 m resolution. Results showed that the production accuracies of forest wetlands, shrub wetlands, meadow wetlands, rivers, ponds, reservoirs, and lakes in the Dianchi Basin were 89.4%, 87.9%, 91.4%, 90.7%, 89.9%, 92.9%, and 95.9%, respectively, and the user accuracies were 94.9%, 92.4%, 92.6%, 95.4%, 94.2%, 91.0%, and 99.4%, respectively. Compared to the CAS_Wetlands, this study categorized the five categories of plateau wetlands into seven types with greater specificity and increased the spatial resolution of wetland mapping from 30 to 1 m. This study can provide a new reference framework for wetland extraction and support the conservation of plateau wetland ecosystems. |
| WOS关键词 | RANDOM FOREST ; ZOIGE PLATEAU ; CLASSIFICATION ; CHINA ; IMAGE |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001178971500003 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/203850]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Tang, Bo-Hui |
| 作者单位 | 1.Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming 650093, Peoples R China 2.Yunnan Prov Dept Educ, Key Lab Plateau Remote Sensing, Kunming 650093, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cai, Fangliang,Tang, Bo-Hui,Sima, Ouyang,et al. Fine Extraction of Plateau Wetlands Based on a Combination of Object-Oriented Machine Learning and Ecological Rules: A Case Study of Dianchi Basin[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2024,17:5364-5377. |
| APA | Cai, Fangliang,Tang, Bo-Hui,Sima, Ouyang,Chen, Guokun,&Zhang, Zhen.(2024).Fine Extraction of Plateau Wetlands Based on a Combination of Object-Oriented Machine Learning and Ecological Rules: A Case Study of Dianchi Basin.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,17,5364-5377. |
| MLA | Cai, Fangliang,et al."Fine Extraction of Plateau Wetlands Based on a Combination of Object-Oriented Machine Learning and Ecological Rules: A Case Study of Dianchi Basin".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17(2024):5364-5377. |
入库方式： OAI收割
来源：地理科学与资源研究所
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