气候变化领域集成服务门户(CCPortal): Mechanisms of climate change impacts on vegetation and prediction of changes on the Loess Plateau, China

CCPortal

DOI	10.1007/s12665-024-11559-5
	Mechanisms of climate change impacts on vegetation and prediction of changes on the Loess Plateau, China
	Gou, Yongcheng; Jin, Zhao; Kou, Pinglang; Tao, Yuxiang; Xu, Qiang; Zhu, Wenchen; Tian, Haibo
发表日期	2024
ISSN	1866-6280
EISSN	1866-6299
起始页码	83
结束页码	8
卷号	83 期号:8
英文摘要	Monitoring and forecasting the spatiotemporal dynamics of vegetation across the Loess Plateau emerge as critical endeavors for environmental conservation, resource management, and strategic decision-making processes. Despite the swift advances in deep learning techniques for spatiotemporal prediction, their deployment for future vegetation forecasting remains underexplored. This investigation delves into vegetation alterations on the Loess Plateau from March 2000 to February 2023, employing fractional vegetation cover (FVC) as a metric, and scrutinizes its spatiotemporal interplay with precipitation and temperature. The introduction of a convolutional long short-term memory network enhanced by an attention mechanism (CBAM-ConvLSTM) aims to forecast vegetation dynamics on the Plateau over the ensuing 4 years, leveraging historical data on FVC, precipitation, and temperature. Findings revealed an ascending trajectory in the maximum annual FVC at a pace of 0.42% per annum, advancing from southeast to northwest, alongside a monthly average FVC increment at 0.02% per month. The principal driver behind FVC augmentation was identified as the growth season FVC surge in warm-temperate semi-arid and temperate semi-arid locales. Precipitation maintained a robust positive long-term association with FVC (Pearson coefficient > 0.7), whereas the temperature-FVC nexus displayed more variability, with periodic complementary trends. The CBAM-ConvLSTM framework, integrating FVC, precipitation, and temperature data, showcased commendable predictive accuracy. Future projections anticipate ongoing greening within the warm-temperate semi-arid region, contrasted by significant browning around the Loess Plateau's peripheries. This research lays the groundwork for employing deep learning in the simulation of vegetation's spatiotemporal dynamics.
英文关键词	Vegetation dynamics; Fractional vegetation cover (FVC); Loess Plateau; Deep Learning; Spatio-temporal prediction
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Water Resources
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources
WOS记录号	WOS:001197462500003
来源期刊	ENVIRONMENTAL EARTH SCIENCES
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/301440
作者单位	Chongqing University of Posts & Telecommunications; Chinese Academy of Sciences; Institute of Earth Environment, CAS; Xi'an Jiaotong University; Chongqing University of Posts & Telecommunications; Chengdu University of Technology
推荐引用方式 GB/T 7714	Gou, Yongcheng,Jin, Zhao,Kou, Pinglang,et al. Mechanisms of climate change impacts on vegetation and prediction of changes on the Loess Plateau, China[J],2024,83(8).
APA	Gou, Yongcheng.,Jin, Zhao.,Kou, Pinglang.,Tao, Yuxiang.,Xu, Qiang.,...&Tian, Haibo.(2024).Mechanisms of climate change impacts on vegetation and prediction of changes on the Loess Plateau, China.ENVIRONMENTAL EARTH SCIENCES,83(8).
MLA	Gou, Yongcheng,et al."Mechanisms of climate change impacts on vegetation and prediction of changes on the Loess Plateau, China".ENVIRONMENTAL EARTH SCIENCES 83.8(2024).