Alpine vegetation phenology dynamic over 16 years and its covariation with climate in a semi-arid region of China

Zhou, Jihua; Cai, Wentao; Qin, Yue; Lai, Liming; Guan, Tianyu; Zhang, Xiaolong; Jiang, Lianhe; Du, Hui; Yang, Dawen; Cong, Zhen; Zheng, Yuanrun

doi:10.1016/j.scitotenv.2016.07.206

Cited by 85 publications

(44 citation statements)

References 46 publications

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“…The Theil-Sen median slope estimator was applied to estimate the rate of change of NPP, which was more appropriate for assessing the rate of change in short or noisy time series [48]. The Theil-Sen median slope was computed as (Equation (9)):…”

Section: Trend Analysis and Turning Point Year Detectionmentioning

confidence: 99%

Nonlinear Variations of Net Primary Productivity and Its Relationship with Climate and Vegetation Phenology, China

Yang

Zhang

Luo

et al. 2017

Forests

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Net primary productivity (NPP) is an important component of the terrestrial carbon cycle. In this study, NPP was estimated based on two models and Moderate Resolution Imaging Spaectroradiometer (MODIS) data. The spatiotemporal patterns of NPP and the correlations with climate factors and vegetation phenology were then analyzed. Our results showed that NPP derived from MODIS performed well in China. Spatially, NPP decreased from the southeast toward the northwest. Temporally, NPP showed a nonlinear increasing trend at a national scale, but the magnitude became slow after 2004. At a regional scale, NPP in Northern China and the Tibetan Plateau showed a nonlinear increasing trend, while the NPP decreased in most areas of Southern China. The decreases in NPP were more than offset by the increases. At the biome level, all vegetation types displayed an increasing trend, except for shrub and evergreen broad forests (EBF). Moreover, a turning point year occurred for all vegetation types, except for EBF. Generally, climatic factors and Length of Season were all positively correlated with the NPP, while the relationships were much more diverse at a regional level. The direct effect of solar radiation on the NPP was larger (0.31) than precipitation (0.25) and temperature (0.07). Our results indicated that China could mitigate climate warming at a regional and/or global scale to some extent during the time period of 2001-2014.

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Section: Trend Analysis and Turning Point Year Detectionmentioning

confidence: 99%

Nonlinear Variations of Net Primary Productivity and Its Relationship with Climate and Vegetation Phenology, China

Yang

Zhang

Luo

et al. 2017

Forests

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“…Previous comprehensive regional studies [7,23,25,69] report a general greening trend in northwestern China and have attributed the trend to the positive effects of a warming climate. However, the vegetation responses to climate differ across regions depending on many kinds of changes experienced and the specific response of regional vegetation to those changes including both climate and human activities.…”

Section: Regional Implicationsmentioning

confidence: 99%

“…Many previous studies have examined the vegetation response to climate change using satellite-derived Normalized Difference Vegetation Index (NDVI) and ground-based climatic measurements [4,5,[23][24][25]. However, vegetation index such as NDVI is easily saturated in the high vegetation coverage areas [26], such as broadleaf forests and needle leaf forests, during the plants' blooming period.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to NDVI, several methods are available for LAI retrieval, being less affected by saturation [28]. However, most of the previous studies on LAI dynamics are limited to short time series vegetation data [23,29] because LAI time series products are less extensive than NDVI products, and long term analysis and the linking of LAI to environmental controls is still relatively rare [4,30].…”

Section: Introductionmentioning

confidence: 99%

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Detection of the Coupling between Vegetation Leaf Area and Climate in a Multifunctional Watershed, Northwestern China

et al. 2016

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Accurate detection and quantification of vegetation dynamics and drivers of observed climatic and anthropogenic change in space and time is fundamental for our understanding of the atmosphere-biosphere interactions at local and global scales. This case study examined the coupled spatial patterns of vegetation dynamics and climatic variabilities during the past three decades in the Upper Heihe River Basin (UHRB), a complex multiple use watershed in arid northwestern China. We apply empirical orthogonal function (EOF) and singular value decomposition (SVD) analysis to isolate and identify the spatial patterns of satellite-derived leaf area index (LAI) and their close relationship with the variability of an aridity index (AI = Precipitation/Potential Evapotranspiration). Results show that UHRB has become increasingly warm and wet during the past three decades. In general, the rise of air temperature and precipitation had a positive impact on mean LAI at the annual scale. At the monthly scale, LAI variations had a lagged response to climate. Two major coupled spatial change patterns explained 29% and 41% of the LAI dynamics during 1983-2000 and 2001-2010, respectively. The strongest connections between climate and LAI were found in the southwest part of the basin prior to 2000, but they shifted towards the north central area afterwards, suggesting that the sensitivity of LAI to climate varied over time, and that human disturbances might play an important role in altering LAI patterns. At the basin level, the positive effects of regional climate warming and precipitation increase as well as local ecological restoration efforts overwhelmed the negative effects of overgrazing. The study results offer insights about the coupled effects of climatic variability and grazing on ecosystem structure and functions at a watershed scale. Findings from this study are useful for land managers and policy makers to make better decisions in response to climate change in the study region.

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“…Grassland covers more than 40% of China's total land area, a large percentage of which is found in the Inner Mongolia Autonomous Region (IMAR) [3]. Due to arid and semi-arid climate conditions, as well as the impact of human activities during the past several decades, the grassland ecosystems showed a limited capacity to withstand human interference; as a result, grassland degradation has been widely observed in IMAR [4][5][6]. Grassland degradation is a complex process in which grassland productivity, vegetation coverage, grass height, soil biodiversity, or community composition decreases [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Modeling Grassland Degradation with and without Considering Localized Spatial Associations in Vegetation Changing Patterns

Wang

Li³

et al. 2018

Sustainability

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Abstract:Grassland ecosystems worldwide are confronted with degradation. It is of great importance to understand long-term trajectory patterns of grassland vegetation by advanced analytical models. This study proposes a new approach called a binary logistic regression model with neighborhood interactions, or BLR-NIs, which is based on binary logistic regression (BLR), but fully considers the spatio-temporally localized spatial associations or characterization of neighborhood interactions (NIs) in the patterns of grassland vegetation. The BLR-NIs model was applied to a modeled vegetation degradation of grasslands in the Xilin river basin, Inner Mongolia, China. Residual trend analysis on the normalized difference vegetation index (RESTREND-NDVI), which excluded the climatic impact on vegetation dynamics, was adopted as a preprocessing step to derive three human-induced trajectory patterns (vegetation degradation, vegetation recovery, and no significant change in vegetation) during two consecutive periods, T 1 (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) and T 2 (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). Human activities, including livestock grazing intensity and transportation accessibility measured by road network density, were included as explanatory variables for vegetation degradation, which was defined for locations if vegetation recovery or no significant change in vegetation in T 1 and vegetation degradation in T 2 were observed. Our work compared the results of BLR-NIs and the traditional BLR model that did not consider NIs. The study showed that: (1) both grazing intensity and road density had a positive correlation to vegetation degradation based on the traditional BLR model; (2) only road density was found to positively correlate to vegetation degradation by the BLR-NIs model; NIs appeared to be critical factors to predict vegetation degradation; and (3) including NIs in the BLR model improved the model performance substantially. The study provided evidence for the importance of including localized spatial associations between the trajectory patterns for mapping vegetation degradation, which has practical implications for designing management policies to counterpart grassland degradation in arid and semi-arid areas.

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Alpine vegetation phenology dynamic over 16 years and its covariation with climate in a semi-arid region of China

Cited by 85 publications

References 46 publications

Nonlinear Variations of Net Primary Productivity and Its Relationship with Climate and Vegetation Phenology, China

Nonlinear Variations of Net Primary Productivity and Its Relationship with Climate and Vegetation Phenology, China

Detection of the Coupling between Vegetation Leaf Area and Climate in a Multifunctional Watershed, Northwestern China

Comparison of Modeling Grassland Degradation with and without Considering Localized Spatial Associations in Vegetation Changing Patterns

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