干旱区生态环境知识资源中心(Arid): Nonlinear Changes in Dryland Vegetation Greenness over East Inner Mongolia, China, in Recent Years from Satellite Time Series

Arid

DOI	10.3390/s20143839
	Nonlinear Changes in Dryland Vegetation Greenness over East Inner Mongolia, China, in Recent Years from Satellite Time Series
	Ding, Chao; Huang, Wenjiang; Li, Yao; Zhao, Shuang; Huang, Fang
通讯作者	Huang, WJ
来源期刊	SENSORS
EISSN	1424-8220
出版年	2020
卷号	20 期号:14
英文摘要	Knowledge of the dynamics of dryland vegetation in recent years is essential for combating desertification. Here, we aimed to characterize nonlinear changes in dryland vegetation greenness over East Inner Mongolia, an ecotone of forest-grassland-cropland in northern China, with time series of Moderate-resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and GEOV2 leaf area index (LAI) values during 2000 to 2016. Changes in the growing season EVI and LAI were detected with the polynomial change fitting method. This method characterizes nonlinear changes in time series by polynomial fitting with the highest polynomial order of three, and simultaneously provides an estimation of monotonic trends over the time series by linear fitting. The relative contribution of climatic factors (precipitation and temperature) to changes in the EVI and LAI were analyzed using linear regression. In general, we observed similar patterns of change in the EVI and LAI. Nonlinear changes in the EVI were detected for about 21% of the region, and for the LAI, the percentage of nonlinear changes was about 16%. The major types of nonlinear changes include decrease-increase, decrease-increase-decrease, and increase-decrease-increase changes. For the overall monotonic trends, very small percentages of decrease (less than 1%) and widespread increases in the EVI and LAI were detected. Furthermore, large areas where the effects of climate variation on vegetation changes were not significant were observed for all major types of change in the grasslands and rainfed croplands. Changes with an increase-decrease-increase process had large percentages of non-significant effects of climate. The further analysis of increase-decrease-increase changes in different regions suggest that the increasing phases were likely to be mainly driven by human activities, and droughts induced the decreasing phase. In particular, some increase-decrease changes were observed around the large patch of bare areas. This may be an early signal of degradation, to which more attention needs to be paid to combat desertification.
英文关键词	land desertification remote sensing enhanced vegetation index leaf area index vegetation trend nonlinear changes East Inner Mongolia
类型	Article
语种	英语
开放获取类型	DOAJ Gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000557870500001
WOS关键词	HORQIN SANDY LAND ; ECOLOGICAL RESTORATION PROGRAM ; SEMIARID AREAS ; COVER CHANGE ; DESERTIFICATION ; DEGRADATION ; DYNAMICS ; TRENDS ; FRAMEWORK ; INCREASE
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/325535
作者单位	[Ding, Chao; Huang, Wenjiang] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Li, Yao] Texas A&M Univ, Zachry Dept Civil & Environm Engn, College Stn, TX 77843 USA; [Zhao, Shuang] Tianjin Chengjian Univ, Sch Geol & Geomet, Tianjin 300384, Peoples R China; [Huang, Fang] Northeast Normal Univ, Sch Geog Sci, Changchun 130024, Peoples R China
推荐引用方式 GB/T 7714	Ding, Chao,Huang, Wenjiang,Li, Yao,et al. Nonlinear Changes in Dryland Vegetation Greenness over East Inner Mongolia, China, in Recent Years from Satellite Time Series[J],2020,20(14).
APA	Ding, Chao,Huang, Wenjiang,Li, Yao,Zhao, Shuang,&Huang, Fang.(2020).Nonlinear Changes in Dryland Vegetation Greenness over East Inner Mongolia, China, in Recent Years from Satellite Time Series.SENSORS,20(14).
MLA	Ding, Chao,et al."Nonlinear Changes in Dryland Vegetation Greenness over East Inner Mongolia, China, in Recent Years from Satellite Time Series".SENSORS 20.14(2020).