DOI: 10.1016/j.gloplacha.2016.10.019
论文题名: Identifying long-term variations in vegetation and climatic variables and their scale-dependent relationships: A case study in Southwest Germany
作者: Liu Z. ; Menzel L.
刊名: Global and Planetary Change
ISSN: 0921-8181
出版年: 2016
卷: 147 起始页码: 54
结束页码: 66
语种: 英语
英文关键词: Climatic variables
; Discrete wavelet transform
; Mann–Kendall trend test
; NDVI
; Southwest Germany
Scopus关键词: Discrete wavelet transforms
; Time measurement
; Time series
; Water resources
; Wavelet transforms
; Climatic variables
; Dependent relationship
; Ecological development
; NDVI
; Normalized difference vegetation index
; Southwest Germany
; Trend tests
; Water resources management
; Vegetation
英文摘要: Geographic time series are usually non-stationary and contain different frequency components (e.g., seasonal variations, long-term and short-term fluctuations) which may significantly affect the overall variance structure in the original data. Based upon the monthly normalized difference vegetation index (NDVI), precipitation and temperature data for six different vegetation types in two precipitation regimes (low and high precipitation regimes) of Rhineland-Palatinate (Southwest Germany), this study aims to examine the temporal trends in the original time series of these variables and their relationships. In addition, the further objectives are to evaluate which time-scale is dominantly responsible for the trend production found in the original data and find out the certain time-scales that represent the strongest correlation between NDVI and climatic variables (i.e., precipitation and temperature). A combined approach using the discrete wavelet transform (DWT), Mann-Kendall (MK) trend test and correlation analysis was implemented to achieve these goals. The trend assessment for the original data shows that the monthly NDVI time series for all vegetation types in both precipitation regimes have upward trends, most of which are significant. The precipitation and temperature data for six vegetation types in two precipitation regimes present weak downward trends and significant increasing trends, respectively. The most important time-scales contributing the trend production in the original NDVI data are the 2-month and 8-month events. For precipitation, the most influential ones are 2-month and 4-month scales. The 4-month periodic mode predominantly affects the trends in the original temperature time series. Based on the original time series, the change in temperature is found to be the primary driver influencing the variability in vegetation greenness over this study area, while there is a negative correlation between NDVI and precipitation for all vegetation types and precipitation regimes. For the scale-dependent relationships between NDVI and precipitation, the 2-month and 8-month scales generally present the strongest negative correlation. The most significant positive correlations between NDVI and temperature are obtained at the 8- to 16-month scales for most vegetation types. The results of the present study might be beneficial for water resources management as well as agricultural and ecological development planning in Rhineland-Palatinate, and also provide a helpful reference for other regions with similar climate condition. © 2016 Elsevier B.V. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994336767&doi=10.1016%2fj.gloplacha.2016.10.019&partnerID=40&md5=cd3627fed1f8e32794ec9bd08e6412ee
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/11591
Appears in Collections: 全球变化的国际研究计划 气候变化与战略
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作者单位: Institute of Geography, Heidelberg University, Heidelberg, Germany
Recommended Citation:
Liu Z.,Menzel L.. Identifying long-term variations in vegetation and climatic variables and their scale-dependent relationships: A case study in Southwest Germany[J]. Global and Planetary Change,2016-01-01,147.