Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin, China

Qu, Shuping; Wang, Lunche; Lin, Aiming; Yu, Deqing; Yuan, Moxi; Chang-an, LI

doi:10.1016/j.ecolind.2019.105724

Cited by 224 publications

(143 citation statements)

References 43 publications

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“…Other factors may also be important for NPP variations. Some studies suggest that solar radiation also affects vegetation trends [25,59]. This should also be considered in the future, especially with grazing data, to account for the maximum possible impact factors.…”

Section: Discussionmentioning

confidence: 99%

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Quantifying the Impacts of Anthropogenic Activities and Climate Variations on Vegetation Productivity Changes in China from 1985 to 2015

et al. 2020

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Accurate assessment of vegetation dynamics provides important information for ecosystem management. Anthropogenic activities and climate variations are the major factors that primarily influence vegetation ecosystems. This study investigates the spatiotemporal impacts of climate factors and human activities on vegetation productivity changes in China from 1985 to 2015. Actual net primary productivity (ANPP) is used to reflect vegetation dynamics quantitatively. Climate-induced potential net primary productivity (PNPP) is used as an indicator of climate change, whereas the difference between PNPP and ANPP is considered as an indicator of human activities (HNPP). Overall, 91% of the total vegetation cover area shows declining trends for net primary productivity (NPP), while only 9% shows increasing trends before 2000 (base period). However, after 2000 (restoration period), 78.7% of the total vegetation cover area shows increasing trends, whereas 21.3% of the area shows decreasing trends. Moreover, during the base period, the quantitative contribution of climate change to NPP restoration is 0.21 grams carbon per meter square per year (gC m−2 yr−1) and to degradation is 2.41 gC m−2 yr−1, while during the restoration period, climate change contributes 0.56 and 0.29 gC m−2 yr−1 to NPP restoration and degradation, respectively. Human activities contribute 0.36 and 0.72 gC m−2 yr−1 during the base period, and 0.63 and 0.31 gC m−2 yr−1 during the restoration period to NPP restoration and degradation, respectively. The combined effects of climate and human activities restore 0.65 and 1.11 gC m−2 yr−1, and degrade 2.01 and 0.67 gC m−2 yr−1 during the base and restoration periods, respectively. Climate factors affect vegetation cover more than human activities, while precipitation is found to be more sensitive to NPP change than temperature. Unlike the base period, NPP per unit area increases with an increase in the human footprint pressure during the restoration period. Grassland has more variability than other vegetation classes, and the grassland changes are mainly observed in Tibet, Xinjiang, and Inner Mongolia regions. The results may help policy-makers by providing necessary guidelines for the management of forest, grassland, and agricultural activities.

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Section: Discussionmentioning

confidence: 99%

“…The Hurst exponent (H) was introduced by a British hydrologist in 1951 [57] and was modified in 1969 by Mandelbrots [58]. In recent studies, it was effectively applied to investigate the long term consistency of time-series data to predict future trends [59]. The following steps are involved in H value calculations:…”

Section: Hurst Exponentmentioning

confidence: 99%

Quantifying the Impacts of Anthropogenic Activities and Climate Variations on Vegetation Productivity Changes in China from 1985 to 2015

et al. 2020

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“…Besides the climate factors, anthropogenic disturbance has been identified to be another key driver for vegetative growth [42]. It is acknowledged that over-grazing and over-cultivation were two major causes of reduced vegetative growth, especially in semi-arid areas, where the ecosystem is fragile [43]. Over-grazing would threaten vegetation species composition, variety and richness [44], while the over-cultivation may significantly deplete ground water for both human and crops [36].…”

Section: Implicationmentioning

confidence: 99%

Response of Vegetation to Changes in Temperature and Precipitation at a Semi-Arid Area of Northern China Based on Multi-Statistical Methods

Zhang

et al. 2020

Forests

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Hydrothermal and climatic conditions determine vegetation productivity and its dynamic changes. However, the legacy effect and the causal relationships between these climatic variables and vegetation growth are still unclear, especially in the dry regions. Based on multi-statistical methods, including bivariate correlation analysis and composite Granger causality tests, we investigated the correlation, causality, and lag length between temperature/precipitation and the vegetation growth (Normalized Difference Vegetation Index, NDVI) in three typical sub-watersheds in the Luanhe River Basin, China. The results show that: (1) Precipitation and temperature are the Granger causes of NDVI variation in the study catchment; (2) temperature and precipitation are not strictly positively correlated with NDVI during growing seasons along with the whole sequence, and excessive warmth and precipitation inhibits vegetative growth; (3) the lag length of vegetation growth in response to temperature/precipitation was shorter in agriculture areas (~2 months) than the forest-dominant area, which have indicated 3-4 months lag length; and (4) anthropogenic disturbance did not result in notable negative effects on vegetation growth at the Luanhe River Basin. Our study further suggests that use of these multi-statistical methods could be a valuable approach for comprehensively understanding the correlation between vegetation growth and climatic variations. We have also provided an avenue to bridge the gaps between stationary and non-stationary sequence, as well as to eliminate pseudo regression problems. These findings provide critical information for developing cost-efficient policies and land use management applications for forest conservation in arid and semi-arid area.

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“…The population is concentrated in the eastern part of the study area (Yu et al 2018), where the economy and urbanization has been rapidly developed, and plantation and farmland are strongly affected by human activities. By contrast, the climate in the western part of the study area is dry and cold (Feng et al 2020), where the development of economic is slow, and the impact of humans on vegetation is weak (Qu et al 2020;. The results of RESTREND analysis also revealed that human activities had a stronger influence on vegetation growth in the eastern part of the study area than in the western part ( Fig.…”

Section: Effects Of Human Activities and Limitations Of Restrendmentioning

confidence: 82%

“…As a key component of terrestrial ecosystems (Peng et al 2016), vegetation plays a vital role in regulating cycles of carbon, water, and energy (Cao and Woodward, 1998;Foley et al 2000;Zoran et al 2016;Tong et al 2016), affecting the global climate (Pei et al 2018), and providing food, fiber and wood production (Liang et al 2015). Vegetation in an ecosystem is influenced by synergistic effects of climatic change and human activities (Qu et al 2020;Chu et al 2019;Hua et al 2017). The dynamics of vegetation can be used as an indicator of vegetation health and ecosystem stability.…”

Section: Introductionmentioning

confidence: 99%

Satellite View of Vegetation Dynamics and Drivers Over Southwestern China

Yan

Wang

Jiang

et al. 2020

Preprint

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Background: Vegetation is a key component of terrestrial ecosystems. The dynamics of vegetation can be used as an indicator of vegetation health and ecosystem stability. However, the dynamics and responses of vegetation in Southwestern China remain unclear.Methods: Both monotonic and turning point approaches were used in trend analysis to understand the dynamics of vegetation. We applied partial correlation analysis and the relative importance of climatic factors to determine the drivers of vegetation dynamics. Residual trend analysis was applied to distinguish other drivers.Results: The annual mean NDVI generally increased at a rate of 0.0011 year−1 from 1982 to 2015, but much of the study area showed the opposite trend before and after the turning point during this period. The vegetation in the western part of the study area was degraded and increased significantly in the eastern part of the study area. An increasing trend that continued to an increasing trend (II) and a decreasing trend that changed into an increasing trend (DI) were two of the largest patterns of vegetation trends, and they were mainly distributed in the eastern part of the study area. An increasing trend that changed into a decreasing trend (ID) was mainly distributed in the western part of the study area, where the vegetation types were mainly evergreen needle-leaved and grassland. Temperature was the dominant climatic factor and had a positive correlation with vegetation in the eastern part of the study area. Temperature also had some negative correlations with vegetation in western mountainous areas where surface soils are prone to droughts. Solar radiation had a positive correlation with vegetation in the western part of the study area. In addition, the regions with more intensity human activities than other areas were concentrated in the eastern part of the study area.Conclusions: The spatial distribution and trend showed high heterogeneity over Southwestern China. Temperature was the dominant climatic factor. In the eastern part of the study area, human activities played an important role in vegetation greening.

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Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin, China

Cited by 224 publications

References 43 publications

Quantifying the Impacts of Anthropogenic Activities and Climate Variations on Vegetation Productivity Changes in China from 1985 to 2015

Quantifying the Impacts of Anthropogenic Activities and Climate Variations on Vegetation Productivity Changes in China from 1985 to 2015

Response of Vegetation to Changes in Temperature and Precipitation at a Semi-Arid Area of Northern China Based on Multi-Statistical Methods

Satellite View of Vegetation Dynamics and Drivers Over Southwestern China

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