干旱区生态环境知识资源中心(Arid): Inconsistency of Global Vegetation Dynamics Driven by Climate Change: Evidences from Spatial Regression

Arid

DOI	10.3390/rs13173442
	Inconsistency of Global Vegetation Dynamics Driven by Climate Change: Evidences from Spatial Regression
	Zhang, Dou; Geng, Xiaolei; Chen, Wanxu; Fang, Lei; Yao, Rui; Wang, Xiangrong; Zhou, Xiao
通讯作者	Wang, XR (corresponding author), Fudan Univ, Dept Environm Sci & Engn, Shanghai 200438, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:17
英文摘要	Global greening over the past 30 years since 1980s has been confirmed by numerous studies. However, a single-dimensional indicator and non-spatial modelling approaches might exacerbate uncertainties in our understanding of global change. Thus, comprehensive monitoring for vegetation's various properties and spatially explicit models are required. In this study, we used the newest enhanced vegetation index (EVI) products of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 to detect the inconsistency trend of annual peak and average global vegetation growth using the Mann-Kendall test method. We explored the climatic factors that affect vegetation growth change from 2001 to 2018 using the spatial lag model (SLM), spatial error model (SEM) and geographically weighted regression model (GWR). The results showed that EVImax and EVImean in global vegetated areas consistently showed linear increasing trends during 2001-2018, with the global averaged trend of 0.0022 yr(-1) (p < 0.05) and 0.0030 yr(-1) (p < 0.05). Greening mainly occurred in the croplands and forests of China, India, North America and Europe, while browning was almost in the grasslands of Brazil and Africa (18.16% vs. 3.08% and 40.73% vs. 2.45%). In addition, 32.47% of the global vegetated area experienced inconsistent trends in EVImax and EVImean. Overall, precipitation and mean temperature had positive impacts on vegetation variation, while potential evapotranspiration and vapour pressure had negative impacts. The GWR revealed that the responses of EVI to climate change were inconsistent in an arid or humid area, in cropland or grassland. Climate change could affect vegetation characteristics by changing plant phenology, consequently rendering the inconsistency between peak and mean greening. In addition, anthropogenic activities, including land cover change and land use management, also could lead to the differences between annual peak and mean vegetation variations.
英文关键词	global vegetation growth climate change inconsistent greening trend spatial autocorrelation and heterogeneity spatial regression models
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:000694531200001
WOS关键词	GEOGRAPHICALLY WEIGHTED REGRESSION ; SPRING PHENOLOGY ; LAND-SURFACE ; DATA SETS ; SPOT-VGT ; MODIS ; AVHRR ; EARTH ; CHINA ; GIMMS
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
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368476
作者单位	[Zhang, Dou; Geng, Xiaolei; Fang, Lei; Wang, Xiangrong] Fudan Univ, Dept Environm Sci & Engn, Shanghai 200438, Peoples R China; [Chen, Wanxu] China Univ Geosci, Sch Geog & Informat Engn, Dept Geog, Wuhan 430074, Peoples R China; [Yao, Rui] China Univ Geosci, Sch Geog & Informat Engn, Hubei Key Lab Crit Zone Evolut, Wuhan 430074, Peoples R China; [Zhou, Xiao] China Univ Geosci, Sch Publ Adm, Wuhan 430074, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Dou,Geng, Xiaolei,Chen, Wanxu,et al. Inconsistency of Global Vegetation Dynamics Driven by Climate Change: Evidences from Spatial Regression[J],2021,13(17).
APA	Zhang, Dou.,Geng, Xiaolei.,Chen, Wanxu.,Fang, Lei.,Yao, Rui.,...&Zhou, Xiao.(2021).Inconsistency of Global Vegetation Dynamics Driven by Climate Change: Evidences from Spatial Regression.REMOTE SENSING,13(17).
MLA	Zhang, Dou,et al."Inconsistency of Global Vegetation Dynamics Driven by Climate Change: Evidences from Spatial Regression".REMOTE SENSING 13.17(2021).