干旱区生态环境知识资源中心(Arid): Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

Arid

DOI	10.3390/rs13081499
	Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata
	Liu, Jiamin; Xiao, Bin; Li, Yueshi; Wang, Xiaoyun; Bie, Qiang; Jiao, Jizong
通讯作者	Jiao, JZ (corresponding author), Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou 730000, Peoples R China. ; Jiao, JZ (corresponding author), Minist Educ MOE, Key Lab Western Chinas Environm Syst, Lanzhou 730000, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:8
英文摘要	Rapid urban expansion has seriously threatened ecological security and the natural environment on a global scale, thus, the simulation of dynamic urban expansion is a hot topic in current research. Existing urban expansion simulation models focus on the mining of spatial neighborhood features among driving factors, however, they ignore the over-fitting, gradient explosion, and vanishing problems caused by the long-term dependence of time series data, which results in limited model accuracy. In this study, we proposed a new dynamic urban expansion simulation model. Considering the long-time dependence issue, long short term memory (LSTM) was employed to automatically extract the transformation rules through memory units and provide the optimal attribute features for cellular automata (CA). This study selected Lanzhou, which is a semi-arid region in Northwest China, as an example to confirm the validity of the model performance using data from 2000 to 2020. The results revealed that the overall accuracy of the model was 91.01%, which was higher than that of the traditional artificial neural network (ANN)-CA and recurrent neural network (RNN)-CA models. The LSTM-CA framework resolved existing problems with the traditional algorithm, while it significantly reduced complexity and improved simulation accuracy. In addition, we predicted urban expansion to 2030 based on natural expansion (NE) and ecological constraint (EC) scenarios, and found that EC was an effective control strategy. This study provides a certain theoretical basis and reference value toward the realization of new urbanization and ecologically sound civil construction, in the context of territorial spatial planning and healthy/sustainable urban development.
英文关键词	urban expansion long short term memory scenario simulation ecological constraint semi-arid region remote sensing
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:000644689900001
WOS关键词	LAND-USE CHANGES ; ECOSYSTEM SERVICES ; NEURAL-NETWORK ; CLIMATE-CHANGE ; LSTM NETWORK ; CONSERVATION ; GROWTH ; CHINA ; SECURITY ; IMPACTS
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	兰州大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351508
作者单位	[Liu, Jiamin; Xiao, Bin; Li, Yueshi; Wang, Xiaoyun; Bie, Qiang; Jiao, Jizong] Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou 730000, Peoples R China; [Liu, Jiamin; Xiao, Bin; Li, Yueshi; Wang, Xiaoyun; Bie, Qiang; Jiao, Jizong] Minist Educ MOE, Key Lab Western Chinas Environm Syst, Lanzhou 730000, Peoples R China; [Bie, Qiang] Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou 730070, Peoples R China
推荐引用方式 GB/T 7714	Liu, Jiamin,Xiao, Bin,Li, Yueshi,et al. Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata[J]. 兰州大学,2021,13(8).
APA	Liu, Jiamin,Xiao, Bin,Li, Yueshi,Wang, Xiaoyun,Bie, Qiang,&Jiao, Jizong.(2021).Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata.REMOTE SENSING,13(8).
MLA	Liu, Jiamin,et al."Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata".REMOTE SENSING 13.8(2021).