干旱区生态环境知识资源中心(Arid): Spatiotemporal change and prediction of land use in Manasi region based on deep learning

Arid

DOI	10.1007/s11356-023-27826-0
	Spatiotemporal change and prediction of land use in Manasi region based on deep learning
	Wang, Jiaojiao; Yin, Xiaojun; Liu, Shannan; Wang, Dimeng
通讯作者	Yin, XJ
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2023
卷号	30 期号:34 页码:82780-82794
英文摘要	The Manasi region is located in an arid and semi-arid region with fragile ecology and scarce resources. The land use change prediction is important for the management and optimization of land resources. We utilized Sankey diagram, dynamic degree of land use, and landscape indices to explore the temporal and spatial variation of land use and integrated the LSTM and MLP algorithms to predict land use prediction. The MLP-LSTM prediction model retains the spatiotemporal information of land use data to the greatest extent and extracts the spatiotemporal variation characteristics of each grid through a training set. Results showed that (1) from 1990 to 2020, cropland, tree cover, water bodies, and urban areas in the Manasi region increased by 855.3465 km(2), 271.7136 km(2), 40.0104 km(2), and 109.2483 km(2), respectively, whereas grassland and bare land decreased by 677.7243 km(2) and 598.5945 km(2), respectively; (2) Kappa coefficients reflect the accuracy of the mode's predictions in terms of quantity. The Kappa coefficients of the land use data predicted by the MLP-LSTM, MLP-ANN, LR, and CA-Markov models were calculated to be 95.58%, 93.36%, 89.48%, and 85.35%, respectively. It can be found that the MLP-LSTM and MLP-ANN models obtain higher accuracy in most levels, while the CA-Markov model has the lowest accuracy. (3) The landscape indices can reflect the spatial configuration characteristics of landscape (land use types), and evaluating the prediction results of land use models using landscape indices can reflect the prediction accuracy of the models in terms of spatial features. The results indicate that the model predicted by MLP-LSTM model conforms to the development trend of land use from 1990 to 2020 in terms of spatial features. This gives a basis for the study of the Manasi region to formulate relevant land use development and rationally allocate land resources.
英文关键词	Spatiotemporal change Land use prediction Deep learning Landscape indices MLP-LSTM
类型	Article
语种	英语
开放获取类型	Green Published, hybrid
收录类别	SCI-E
WOS记录号	WOS:001014994600006
WOS关键词	CELLULAR-AUTOMATA ; PATTERN ; COUNTY ; COVER ; MODEL
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396279
推荐引用方式 GB/T 7714	Wang, Jiaojiao,Yin, Xiaojun,Liu, Shannan,et al. Spatiotemporal change and prediction of land use in Manasi region based on deep learning[J],2023,30(34):82780-82794.
APA	Wang, Jiaojiao,Yin, Xiaojun,Liu, Shannan,&Wang, Dimeng.(2023).Spatiotemporal change and prediction of land use in Manasi region based on deep learning.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,30(34),82780-82794.
MLA	Wang, Jiaojiao,et al."Spatiotemporal change and prediction of land use in Manasi region based on deep learning".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 30.34(2023):82780-82794.