气候变化领域集成服务门户(CCPortal): Investigating the underlying drivers of vegetation dynamics in cold-arid mountainous

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DOI	10.1016/j.catena.2024.107831
	Investigating the underlying drivers of vegetation dynamics in cold-arid mountainous
	Xiao, Xiong; Guan, Qingyu; Zhang, Zepeng; Liu, Hanqi; Du, Qinqin; Yuan, Tingwei
发表日期	2024
ISSN	0341-8162
EISSN	1872-6887
起始页码	237
卷号	237
英文摘要	Understanding vegetation evolution trends and their driving mechanisms are essential to uncover changes in ecosystem structure and function. Under the interaction of natural and human factors in cold-arid mountainous region, the main controlling factors and degree of nonlinear influence are not yet clear. This study utilized trend analysis, along with modeling techniques such as boosted regression trees (BRT) and structural equation model (SEM), to identify and quantify the contributions and nonlinear response thresholds of natural and human factors to normalized difference vegetation index (NDVI) in the Qilian Mountains from 2000 to 2020. The results showed that the implementation of ecological projects made the overall vegetation into greening trend (91.06%), while irrational human activities led to degradation in the central-eastern regions. The BRT showed that climatic factors dominated for overall vegetation changes, greening and significant greening. Thresholds exist for different drivers to influence both greening and degradation of vegetation. Regarding degradation/significant vegetation degradation, the importance of climate factors decreased, and topography ranked first, followed by population density and LUCC. The expansion of building and unused land will inhibit vegetation greening. SEM revealed that climate factors continued to maintain positive effect with the warm-humid change. The destruction of forestlands and grasslands led to significant degradation of vegetation in the central-eastern regions with the largest total negative effect. Topographic factors coupled with multi-factors inhibited vegetation growth in the central-eastern regions. This study provided a new framework for accurately assessing the dynamic evolution of vegetation, and better reveal the multi-factor driving mechanism vegetation changes in cold-arid mountainous regions. Future research should consider combining more accurate AI algorithms with field surveys to quantify the coupling mechanisms between factors and vegetation dynamic. It is expected to provide scientific guidance for vegetation ecosystem planning in other mountainous regions globally and support the achievement of sustainable development.
英文关键词	Vegetation changes; Driving mechanisms; Boosted regression tree model; Structural equation model; Cold -arid mountainous region
语种	英语
WOS研究方向	Geology ; Agriculture ; Water Resources
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS记录号	WOS:001171865700001
来源期刊	CATENA
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/304681
作者单位	Lanzhou University
推荐引用方式 GB/T 7714	Xiao, Xiong,Guan, Qingyu,Zhang, Zepeng,et al. Investigating the underlying drivers of vegetation dynamics in cold-arid mountainous[J],2024,237.
APA	Xiao, Xiong,Guan, Qingyu,Zhang, Zepeng,Liu, Hanqi,Du, Qinqin,&Yuan, Tingwei.(2024).Investigating the underlying drivers of vegetation dynamics in cold-arid mountainous.CATENA,237.
MLA	Xiao, Xiong,et al."Investigating the underlying drivers of vegetation dynamics in cold-arid mountainous".CATENA 237(2024).