干旱区生态环境知识资源中心(Arid): A Novel Model Integrating Deep Learning for Land Use/Cover Change Reconstruction: A Case Study of Zhenlai County, Northeast China

Arid

DOI	10.3390/rs12203314
	A Novel Model Integrating Deep Learning for Land Use/Cover Change Reconstruction: A Case Study of Zhenlai County, Northeast China
	Yubo, Zhang; Zhuoran, Yan; Jiuchun, Yang; Yuanyuan, Yang; Dongyan, Wang; Yucong, Zhang; Fengqin, Yan; Lingxue, Yu; Liping, Chang; Shuwen, Zhang
通讯作者	Dongyan, W
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:20
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.
英文关键词	land use change spatiotemporal modeling deep learning model integration
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000585594300001
WOS关键词	COVER CHANGE ; USE LEGACIES ; PATTERNS ; SCALE ; CROP ; CLASSIFICATION ; SENTINEL-2 ; SIMULATION ; MAPS
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	中国科学院地理科学与资源研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/327300
作者单位	[Yubo, Zhang; Zhuoran, Yan; Dongyan, Wang] Jilin Univ, Coll Earth Sci, Changchun 130021, Peoples R China; [Yubo, Zhang; Zhuoran, Yan; Jiuchun, Yang; Lingxue, Yu; Liping, Chang; Shuwen, Zhang] Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun 130102, Peoples R China; [Yuanyuan, Yang; Fengqin, Yan] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; [Yucong, Zhang] 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]. 中国科学院地理科学与资源研究所,2020,12(20).
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).
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).