A Novel Model Integrating Deep Learning for Land Use/Cover Change Reconstruction: A Case Study of Zhenlai County, Northeast China
文献类型：期刊论文
| 作者 | Yubo, Zhang2,3; Zhuoran, Yan2,3; Jiuchun, Yang3; Yuanyuan, Yang1 ; Dongyan, Wang2; Yucong, Zhang4; Fengqin, Yan1; Lingxue, Yu3; Liping, Chang3; Shuwen, Zhang3
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020-10-01 |
| 卷号 | 12期号:20页码:22 |
| 关键词 | land use change spatiotemporal modeling deep learning model integration |
| DOI | 10.3390/rs12203314 |
| 通讯作者 | Dongyan, Wang(wang_dy@jlu.edu.cn) |
| 英文摘要 | In recent decades, land use/cover change (LUCC) due to urbanization, deforestation, and desertification has dramatically increased, which changes the global landscape and increases the pressure on the environment. LUCC not only accelerates global warming but also causes widespread and irreversible loss of biodiversity. Therefore, LUCC reconstruction has important scientific and practical value for studying environmental and ecological changes. The commonly used LUCC reconstruction models can no longer meet the growing demand for uniform and high-resolution LUCC reconstructions. In view of this circumstance, a deep learning-integrated LUCC reconstruction model (DLURM) was developed in this study. Zhenlai County of Jilin Province (1986-2013) was taken as an example to verify the proposed DLURM. The average accuracy of the DLURM reached 92.87% (compared with the results of manual interpretation). Compared with the results of traditional models, the DLURM had significantly better accuracy and robustness. In addition, the simulation results generated by the DLURM could match the actual land use (LU) map better than those generated by other models. |
| WOS关键词 | COVER CHANGE ; USE LEGACIES ; PATTERNS ; SCALE ; CROP ; CLASSIFICATION ; SENTINEL-2 ; SIMULATION ; MAPS |
| 资助项目 | National Key Research and Development Project[2017YFC0504202] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA2003020103] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23060405] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000585594300001 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Project ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/156521]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Dongyan, Wang |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Jilin Univ, Coll Earth Sci, Changchun 130021, Peoples R China 3.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun 130102, Peoples R China 4.Northwest Normal Univ, Coll Phys & Elect Engn, Lanzhou 730070, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yubo, Zhang,Zhuoran, Yan,Jiuchun, Yang,et al. A Novel Model Integrating Deep Learning for Land Use/Cover Change Reconstruction: A Case Study of Zhenlai County, Northeast China[J]. REMOTE SENSING,2020,12(20):22. |
| APA | Yubo, Zhang.,Zhuoran, Yan.,Jiuchun, Yang.,Yuanyuan, Yang.,Dongyan, Wang.,...&Shuwen, Zhang.(2020).A Novel Model Integrating Deep Learning for Land Use/Cover Change Reconstruction: A Case Study of Zhenlai County, Northeast China.REMOTE SENSING,12(20),22. |
| MLA | Yubo, Zhang,et al."A Novel Model Integrating Deep Learning for Land Use/Cover Change Reconstruction: A Case Study of Zhenlai County, Northeast China".REMOTE SENSING 12.20(2020):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
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