Monitoring vegetation restoration on potentially contaminated mining land using a novel multi-source data fusion method
文献类型：期刊论文
| 作者 | Guo, Changqing1,2; Dou, Yinyin2; Kuang, Wenhui2; Xiao, Linying3,4,5; Bao, Wenxuan1,2 |
| 刊名 | GEO-SPATIAL INFORMATION SCIENCE
 |
| 出版日期 | 2026-02-19 |
| 卷号 | N/A |
| 关键词 | Remote sensing mapping mining land mine greening ecological recovery Inner Mongolia Autonomous Region |
| ISSN号 | 1009-5020 |
| DOI | 10.1080/10095020.2026.2615484 |
| 产权排序 | 1 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Accurately mapping and monitoring of vegetation restoration area on potentially contaminated mining land (PCML) is crucial for evaluating ecological recovery from mining-related pollution. However, the types and patterns of PCML's vegetation restoration area (PCML-VRA) remain unclear at large scales, despite numerous studies conducted at single-site or local scales. In this study, we developed a PCML-VRA dataset in Inner Mongolia Autonomous Region from 2010 to 2020 using a novel multi-source data fusion method. This method integrated high-resolution satellite imagery, potentially contaminated enterprise sites data, mineral deposit database, land use/cover data, and other thematic data using the Google Earth Engine (GEE) cloud platform in combination with GIS local platform. The overall accuracy of the PCML-VRA vector dataset in Inner Mongolia was (87.80 +/- 5.81)%, demonstrating high reliability and quality. Results showed that the area of PCML in 2010 was 954.34 km2, and the area of PCML-VRA in Inner Mongolia between 2010 and 2020 was 373.33 km2. Consequently, the level of vegetation restoration within PCML in Inner Mongolia was 39.12% over the decade, with gross domestic product, precipitation, wind speed, and population identified as the dominant factors. This study enhances access to high-resolution mapping of PCML-VRA, facilitating the evaluation of ecological protection and the effectiveness of mine greening initiatives. |
| URL标识 | 查看原文 |
| WOS关键词 | CHINA ; COVER ; URBAN ; EXPANSION ; TRAJECTORIES ; RECLAMATION ; LANDSCAPE ; PATTERNS ; REGION ; WATER |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001695904900001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/220980]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Kuang, Wenhui |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China; 3.Yunnan Acad Forestry & Grassland, Yunnan Jianshui Desert Ecosyst Natl Positioning Re, Jianshui, Peoples R China 4.Beijing Forestry Univ, Key Lab State Forestry Adm Soil & Water Conservat, Beijing, Peoples R China; 5.Beijing Forestry Univ, Sch Soil & Water Conservat, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Guo, Changqing,Dou, Yinyin,Kuang, Wenhui,et al. Monitoring vegetation restoration on potentially contaminated mining land using a novel multi-source data fusion method[J]. GEO-SPATIAL INFORMATION SCIENCE,2026,N/A. |
| APA | Guo, Changqing,Dou, Yinyin,Kuang, Wenhui,Xiao, Linying,&Bao, Wenxuan.(2026).Monitoring vegetation restoration on potentially contaminated mining land using a novel multi-source data fusion method.GEO-SPATIAL INFORMATION SCIENCE,N/A. |
| MLA | Guo, Changqing,et al."Monitoring vegetation restoration on potentially contaminated mining land using a novel multi-source data fusion method".GEO-SPATIAL INFORMATION SCIENCE N/A(2026). |
入库方式： OAI收割
来源：地理科学与资源研究所
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