Long-Term Spatiotemporal Information Extraction of Cultivated Land in the Nomadic Area: A Case Study of the Selenge River Basin
文献类型：期刊论文
| 作者 | Sun, Yifei1,2; Wang, Juanle1,2,3; Li, Kai1,2; Chonokhuu, Sonomdagva4 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-06-06 |
| 卷号 | 17期号:12页码:1970 |
| 关键词 | cultivated land extraction Selenge River Basin remote sensing machine learning agriculture |
| DOI | 10.3390/rs17121970 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The Mongolian Plateau, a region where nomadic and agrarian civilizations intersect, exemplifies regional sustainable development and natural resource utilization through the spatiotemporal distribution of cultivated land. However, large-scale, long-term, high-precision extraction of cultivated land has not been systematically conducted in this area. This study integrated remote sensing technology with machine learning methodologies to develop an automated extraction process based on spectral, textural, and topographical features. We monitored changes in cultivated land across eight time periods from 1990 to 2023 within the Selenge River Basin, utilizing Google Earth Engine and 3527 scenes derived from Landsat and Sentinel satellite imagery. The area of cultivated land fluctuated between 6332.78 km2 and 14,799.22 km2, representing 2.26% to 5.29% of the total area. Cultivated land exhibited a significant decline prior to 2005 and gradually increased after 2010, largely influenced by agricultural policy reforms. Traditional nomadic areas showed a spatial pattern of reconstruction, characterized by a significant transformation to agricultural land. The overall accuracy exceeded 90%, and kappa coefficients remained above 0.83. Consistency checks and comparisons of different integration methods further validate the feasibility and reliability of the research methods and results. This approach holds promise for application across the entire Mongolian Plateau and other arid and semi-arid regions for monitoring cultivated land dynamics. |
| URL标识 | 查看原文 |
| WOS关键词 | CROPLAND EXTENT ; CHINA ; CLASSIFICATION |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001516092800001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214626]  |
| 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 | Wang, Juanle |
| 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 100049, Peoples R China; 3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China; 4.Natl Univ Mongolia, Dept Environm & Forest Engn, Ulaanbaatar City 210646, Mongolia |
| 推荐引用方式 GB/T 7714 | Sun, Yifei,Wang, Juanle,Li, Kai,et al. Long-Term Spatiotemporal Information Extraction of Cultivated Land in the Nomadic Area: A Case Study of the Selenge River Basin[J]. REMOTE SENSING,2025,17(12):1970. |
| APA | Sun, Yifei,Wang, Juanle,Li, Kai,&Chonokhuu, Sonomdagva.(2025).Long-Term Spatiotemporal Information Extraction of Cultivated Land in the Nomadic Area: A Case Study of the Selenge River Basin.REMOTE SENSING,17(12),1970. |
| MLA | Sun, Yifei,et al."Long-Term Spatiotemporal Information Extraction of Cultivated Land in the Nomadic Area: A Case Study of the Selenge River Basin".REMOTE SENSING 17.12(2025):1970. |
入库方式： OAI收割
来源：地理科学与资源研究所
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