Estimating Relations of Vegetation, Climate Change, and Human Activity: A Case Study in the 400 mm Annual Precipitation Fluctuation Zone, China

Abstract

The 400 mm annual precipitation fluctuation zone (75°55′–127°6′E and 26°55′–53°6′N) is located in central and western China, which is a transition area from traditional agricultural to animal husbandry. It is extremely sensitive to climatic changes. The corresponding changes of the ecosystem, represented by vegetation, under the dual influences of climate change and human activities are important issues in the study of the regional ecological environment. Based on the Savitzky–Golay (S–G) filtering method, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Differential Vegetation Index (NDVI) dataset (NDVI3g) was reconstructed in this paper. Sen’s slope estimation, Mann–Kendall (M–K), multiple regression residual analysis, and the Hurst index were used to quantify the impacts of climate change and human activities on vegetation; in addition, the future persistence characteristics of the vegetation changes trend were analyzed. Vegetation changes in the study area had an obvious spatio-temporal heterogeneity. On an annual scale, the vegetation increased considerably, with a growth rate of 0.50%/10a. The multi-year mean value of NDVI and growth rate of cultivated land were the highest, followed by the forest land and grassland. On a seasonal scale, the vegetation cover increased most significantly in autumn, followed by spring and summer. In the southeastern and central parts of the study area, the vegetation cover increased significantly (P < 0.05), while it decreased significantly in the northeastern and southwestern parts. In summer, the NDVI value of all vegetation types (cultivated land, forest land and grassland) reached the maximum. The change rate of NDVI value for cultivated land reached the highest in autumn (1.57%/10a), forest land reached the highest in spring (1.15%/10a), and grassland reached the highest in autumn (0.49%/10a). The NDVI of cultivated land increased in all seasons, while forest land (−0.31%/10a) and grassland (−0.009%/10a) decreased in winter. Partial correlation analysis between vegetation and precipitation, temperature found that the areas with positive correlation accounted for 66.29% and 55.05% of the total area, respectively. Under the influence of climate change alone, 62.79% of the study area showed an increasing tendency, among which 46.79% showed a significant upward trend (P < 0.05). The NDVI decreased in 37.21% of the regions and decreased significantly in 14.88% of the regions (P < 0.05). Under the influence of human activities alone, the vegetation in the study area showed an upward trend in 59.61%, with a significant increase in 41.35% (P < 0.05), a downward trend in 40.39%, and a significant downward trend in 7.95% (P < 0.05). Vegetation growth is highly unstable and prone to drastic changes, depending on the environmental conditions.

Vegetation Cover Change and Its Attribution in China from 2001 to 2018

“…Limited by earlier methods, such as linear regression, many studies did not directly consider LCC, but used linear regression and residual methods to indirectly separate the impacts of human activity and climatic factors on vegetation changes [24,[66][67][68]. However, the human activity obtained by this method contained other potential factors [69].…”

Decreasing control of precipitation on grassland spring phenology in temperate China

“…The increased differences in temperature between years probably contributed to the larger variability in the SOS. Second, precipitation increased until the mid‐1990s for the whole study area (Lee & Sohn, 2011), which was probably related to the significant preseason increase in precipitation across the whole study area over the study period, reducing growth sensitivity to water availability in these arid and semi‐arid regions (Felton et al., 2019; Li et al., 2019; Shen et al., 2015). As a result, phenology dynamics might have become more related to temperature and variability therein.…”

Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors

“…Likewise, the Hurst exponent of the newly generated time series are close to 0.5 and are smaller than the original. Furthermore, [73] have also discussed the excellent applicability of the Hurst index in vegetation change persistence analysis in arid and semi-arid regions.…”