High-resolution vegetation mapping in Inner Mongolia based on Sentinel-2 imagery and Random Forest
文献类型：期刊论文
| 作者 | Ma, Yuhui2,3; Wang, Jianmin1,3; Zhang, Lei1,2,3; Ren, Hongrui2,3 |
| 刊名 | ADVANCES IN SPACE RESEARCH
 |
| 出版日期 | 2026-05-01 |
| 卷号 | 77期号:9页码:8912-8925 |
| 关键词 | Vegetation mapping GEE Vegetation classification Random Forest classifier Inner Mongolia |
| ISSN号 | 0273-1177 |
| DOI | 10.1016/j.asr.2026.02.092 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | The ecological conditions in the Inner Mongolia region are shaped by the interplay between natural processes and anthropogenic influences. As a vital ecological barrier in northern China, detailed monitoring of its vegetation dynamics is essential. This study pro-duced a 10-meter resolution vegetation map of Inner Mongolia for 2023 using the Random Forest classifier on the Google Earth Engine platform. We integrated Sentinel-2A/B imagery, spectral indices (band reflectance and spectral indices), topographic (elevation, slope, and aspect), and climatic data (annual precipitation and temperature) to classify vegetation into 14 types, with a key contribution being the subdivision of grassland into five distinct subtypes: meadow steppe (109660.1 km2), typical steppe (199455.2 km2), desert steppe (124556.5 km2), steppe-desert (109407.2 km2), and desert (144249.5 km2). This refinement addresses a significant gap in existing land cover products. The classification achieves an overall accuracy of 84.46%, with a Kappa coefficient of 0.83, demonstrating strong con-sistency with reference data and effectively capturing the vegetation distribution patterns in Inner Mongolia; Furthermore, among the 14 classified vegetation types, coniferous forest, broad-leaved forest, meadow steppe, typical steppe, desert steppe, steppe-desert, and desert display clear zonal distribution patterns, which are closely correlated with the region's climatic and topographic gradients. This study provides the latest high-resolution vegetation dataset and detailed area statistics for Inner Mongolia. The results provide critical data support for promoting sustainable development in Inner Mongolia and offer valuable insights into regional vegetation dynamics. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. |
| URL标识 | 查看原文 |
| WOS关键词 | COVER ; CHINA ; CLASSIFICATION ; DYNAMICS ; DATASET |
| WOS研究方向 | Engineering ; Astronomy & Astrophysics ; Geology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001746230800001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221526]  |
| 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, Jianmin; Zhang, Lei |
| 作者单位 | 1.Taiyuan Univ Technol, 79 West Yingze St, Taiyuan 030024, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China; 3.Taiyuan Univ Technol, Dept Geomat, Taiyuan 030024, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Ma, Yuhui,Wang, Jianmin,Zhang, Lei,et al. High-resolution vegetation mapping in Inner Mongolia based on Sentinel-2 imagery and Random Forest[J]. ADVANCES IN SPACE RESEARCH,2026,77(9):8912-8925. |
| APA | Ma, Yuhui,Wang, Jianmin,Zhang, Lei,&Ren, Hongrui.(2026).High-resolution vegetation mapping in Inner Mongolia based on Sentinel-2 imagery and Random Forest.ADVANCES IN SPACE RESEARCH,77(9),8912-8925. |
| MLA | Ma, Yuhui,et al."High-resolution vegetation mapping in Inner Mongolia based on Sentinel-2 imagery and Random Forest".ADVANCES IN SPACE RESEARCH 77.9(2026):8912-8925. |
入库方式： OAI收割
来源：地理科学与资源研究所
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