Quantitatively distinguishing the impact of climate change and human activities on vegetation in mainland China with the improved residual method

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Abstract

In recent decades, vegetation has faced the dual challenges posed by climate change and human activities. Quantitatively distinguishing the influences of climate change and human activities on vegetation changes is key to developing adaptive ecological protection policies. This study examined changes in temperature and precipitation to determine if anthropogenic land use changes have affected vegetation in mainland China. The contribution rates of temperature and precipitation changes and land use changes to vegetation dynamics are further calculated by the improved residual trend method, which considers the nonlinear relationship between vegetation and climate factors and time-lag effects from a spatiotemporal perspective and sets the base period for the equation. The results show that 68.81% of the vegetation in mainland China is in a state of sustained growth, where cultivated vegetation and grasses are the main greening vegetation types. The contribution of land use changes to vegetation changes in mainland China is higher than that of temperature and precipitation changes. Planting trees and grasses and returning farmlands to forests and grassland has increased the area covered by grasses and mixed coniferous broad-leaved forests, while cultivated vegetation coverage has decreased. Swamps are more sensitive to temperature and precipitation changes. We show that the improved residual trend method that considers temporal and spatial dimensions can reduce the uncertainty in quantifying the effects of climatic and anthropogenic factors on vegetation dynamics. This study provides a theoretical basis and a useful tool for future governmental implementation of ecological management strategies.

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Zhang, Y., & Ye, A. (2021). Quantitatively distinguishing the impact of climate change and human activities on vegetation in mainland China with the improved residual method. GIScience and Remote Sensing, 58(2), 235–260. https://doi.org/10.1080/15481603.2021.1872244

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