EGU2020-2283, updated on 12 Jun 2020
https://doi.org/10.5194/egusphere-egu2020-2283
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Quantitatively distinguish the impact of climate change and human activities on the vegetation changes in Mainland China based on the improved residual method

Yahai Zhang and Aizhong Ye
Yahai Zhang and Aizhong Ye
  • Beijing Normal University, China (zcyahai@mail.bnu.edu.cn)

        Knowledge of the current severe global environmental changes, vegetation has faced the dual challenges posed by climate change and human activities. Quantitatively distinguishing the influence of climate change and human activities on vegetation changes is a key to develop adaptive ecological protection policies. This study used the Normalized Difference Vegetation Index (NDVI) and meteorological data from 1982 to 2015 to analyze the characteristic of vegetation changes and the relationship with climate factors in Mainland China. The contribution rates of climate change and human activities to vegetation dynamics are further calculated by the improved trend method of residual analysis. The results show that 68.81% vegetation of Mainland China is in a state of sustainable increase and cultivated vegetation (CV) and grass are the main greening vegetation types. The impact of human activities (54.45%-75.27%) on vegetation changes in Mainland China is higher than climate change (24.73%-45.46%). Human activities mainly affect grass, mixed coniferous broad-leaved forest (MCBF) and cultivated vegetation (CV), while swamp is more sensitive to climate change. The improved residual trend method considering temporal and spatial dimensions can reduce the uncertainty of the methods. This study provides a theoretical basis for future government implementation of ecological management.

How to cite: Zhang, Y. and Ye, A.: Quantitatively distinguish the impact of climate change and human activities on the vegetation changes in Mainland China based on the improved residual method, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2283, https://doi.org/10.5194/egusphere-egu2020-2283, 2020

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