干旱区生态环境知识资源中心(Arid): Simulation model of vegetation dynamics by combining static and dynamic data using the gated recurrent unit neural network-based method

Arid

DOI	10.1016/j.jag.2022.102901
	Simulation model of vegetation dynamics by combining static and dynamic data using the gated recurrent unit neural network-based method
	Zhang, Pu; Li, Zhipeng; Zhang, Heyu; Ding, Jie; Zhang, Xufeng; Peng, Rui; Feng, Yiming
通讯作者	Feng, YM
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	1569-8432
EISSN	1872-826X
出版年	2022
卷号	112
英文摘要	The simulation of vegetation dynamics is essential for guiding regional ecological remediation and environ-mental management. Recent progress in deep learning methods has provided possible solutions to vegetation simulations. The gated recurrent unit (GRU) is one of the latest deep learning algorithms that can effectively process dynamic data. However, static and dynamic data, which typically coexist in the datasets of vegetation dynamic changes, are typically processed indistinguishably. To efficiently extract spatiotemporal patterns and improve our ability to simulate potential vegetation changes, we introduced GRU into vegetation simulation and further amended the original structure of GRU according to the characteristics of the simulation dataset. The new model, the vegetation dynamics model (VDM), can independently process static and dynamic data using a more appropriate algorithm, thereby improving the simulation accuracy. Moreover, we presented a model test applied in the Luntai Desert-Oasis Ecotone in Northwest China and compared the performance of the VDM with baseline models. The results showed that the VDM produced a 7.51% higher coefficient of determination (R-2) value, 7.51% higher adjusted R-2 value, 16.67% lower mean squared error, and 10.78% lower mean absolute error than those of the GRU, which is the best baseline model. The proposed VDM is the first GRU-based simulation model of vegetation dynamics that has the potential to detect the time-order characteristics of dynamic factors by comprehensively considering the static information that affects vegetation changes. Moreover, the flexibility of the VDM, in combination with the wide availability of data from different data sources, aids the broader application of the VDM.
英文关键词	Vegetation dynamic Gated recurrent unit neural network Static data Dynamic data Simulation model
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000844328100004
WOS关键词	LAND-USE ; NDVI ; COVER ; REGRESSION ; REGION ; FOREST ; PREDICTION ; SATELLITE ; IMPACT ; SOIL
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393120
推荐引用方式 GB/T 7714	Zhang, Pu,Li, Zhipeng,Zhang, Heyu,et al. Simulation model of vegetation dynamics by combining static and dynamic data using the gated recurrent unit neural network-based method[J],2022,112.
APA	Zhang, Pu.,Li, Zhipeng.,Zhang, Heyu.,Ding, Jie.,Zhang, Xufeng.,...&Feng, Yiming.(2022).Simulation model of vegetation dynamics by combining static and dynamic data using the gated recurrent unit neural network-based method.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,112.
MLA	Zhang, Pu,et al."Simulation model of vegetation dynamics by combining static and dynamic data using the gated recurrent unit neural network-based method".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 112(2022).