Fusion and Analysis of Land Use/Cover Datasets Based on Bayesian-Fuzzy Probability Prediction: A Case Study of the Indochina Peninsula
文献类型：期刊论文
| 作者 | Wang, Hao2,3; Hu, Yunfeng2,3; Feng, Zhiming1,2,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-11-01 |
| 卷号 | 14期号:22页码:18 |
| 关键词 | land mapping data fusion posterior probability data accuracy Indochina |
| DOI | 10.3390/rs14225786 |
| 通讯作者 | Hu, Yunfeng(huyf@lreis.ac.cn) |
| 英文摘要 | Land use/cover (LUC) datasets are the basis of global change studies and cross-scale land planning. Data fusion is an important direction for correcting errors and improving the reliability of multisource LUC datasets. In this study, a new fusion method based on Bayesian fuzzy probability prediction was developed, and a case study was conducted in five countries of the Indochina Peninsula to form a fusion dataset with a resolution of 30 m in 2020 (BeyFusLUC30). After precision and uncertainty analysis, it was found that: (1) using accuracy validation information as prior knowledge and considering spatial relations can be well applied to LUC data fusion. (2) When compared to the four source datasets (LSV10, GLC_FCS30, ESRI10, and Globeland30), the accuracy indices of BeyFusLUC30 are all optimal. The average overall consistency increased by 6.42-13.61%, the overall accuracy increased by 4.84-7.11%, and the kappa coefficient increased by 4.98-7.60%. (3) The accuracy of the fusion result improved less for land types with good original accuracy (cropland, forest, water area, and built-up land), and the improved range of F1 score was at least 0.40-2.29%, and at most 6.66-9.88%. For the land types with poor original accuracy (grassland, shrubland, wetland, and bare land), the accuracy of the fusion result improved more, and the F1 score improved by at least 4.02-5.82%, and at most 14.41-48.35%. The LUC dataset fusion and quality improvement method developed in this study can be applied to other regions of the world as well. BeyFusLUC30 can provide reliable LUC data for scientific research and government applications in the peninsula. |
| WOS关键词 | COVER DATASETS ; CROPLAND |
| 资助项目 | National Natural Science Foundation of China[42130508] ; Network Security and Information Program of the Chinese Academy of Sciences[CAS-WX2021SF0106] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20010202] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000887615800001 |
| 资助机构 | National Natural Science Foundation of China ; Network Security and Information Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187534]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Hu, Yunfeng |
| 作者单位 | 1.Minist Nat Resources, Key Lab Carrying Capac Assessment Resource & Envi, Beijing 101149, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Hao,Hu, Yunfeng,Feng, Zhiming. Fusion and Analysis of Land Use/Cover Datasets Based on Bayesian-Fuzzy Probability Prediction: A Case Study of the Indochina Peninsula[J]. REMOTE SENSING,2022,14(22):18. |
| APA | Wang, Hao,Hu, Yunfeng,&Feng, Zhiming.(2022).Fusion and Analysis of Land Use/Cover Datasets Based on Bayesian-Fuzzy Probability Prediction: A Case Study of the Indochina Peninsula.REMOTE SENSING,14(22),18. |
| MLA | Wang, Hao,et al."Fusion and Analysis of Land Use/Cover Datasets Based on Bayesian-Fuzzy Probability Prediction: A Case Study of the Indochina Peninsula".REMOTE SENSING 14.22(2022):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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