Decoupling and partitioning the effect of climate and afforestation on long‐term vegetation greening in China since the 1990s

Abstract

Vegetation is an essential component of the Earth's surface system, and is a clear indicator to global climate changes. Understanding the long-term characteristics of vegetation variations and their relationship to climate and human activities is important for regional sustainable development and ecological construction. In this study the ensemble empirical mode decomposition, the breaks for additive seasonal and trend algorithm and trend analysis were applied to obtain the spatiotemporal characteristics of the long-term interannual normalized difference vegetation index in China. Partial least squares-structural equation modeling and geographically weighted regression were used to separate the effects of climate and human activities on vegetation greening and achieve the partitioning of these driving forces. The results suggested that vegetation growth in China experienced an abrupt change in 1995, there was obvious vegetation 'browning' during 1990-1995, and noticeable vegetation recovery from 1996 to 2018 (slope = 0.129/yr, p = 0.009). Spatially, vegetation 'greening' was occurred in central and southern China, reflecting the positive effect of ecological restoration projects on vegetation growth. Climate was a directly main driving force for vegetation greening in China. It played a positive role in SouthChina, but had a negative effect in Northwest China. Improving socio-economic conditions had a slightly negative impact on vegetation greening, while afforestation played a direct and obvious role in promoting vegetation growth, especially in Northwest China. Furthermore, afforestation and socio-economic conditions would also indirectly affect vegetation growth by directly influencing the local microclimate, and the indirect effect of them on vegetation growth was far greater than its direct impact in some cases; therefore, research attention should be paid to the indirect effects of these driving forces on vegetation growth. There was obvious spatial heterogeneity in the effects of different forces on vegetation greening in China, and the dominant driving force of vegetation change in each geographical region also differed.

A comparative study on the spatio-temporal patterns and drivers of net primary productivity in three basins across different climate gradients in China

“…More systematic and comprehensive quantitative analyses are needed for the hydrothermal limitation as well as its spatio-temporal patterns during recent decades. Secondly, human activities also have critical influences on the spatio-temporal patterns of NPP (Gang et al, 2018; Peng et al, 2023; Yang et al, 2021), but these lack indepth analysis in our study. Thirdly, we only used the mean annual NPP from previous studies to validate the simulations in this study.…”

“…In addition, the elevations of NPP from the HRB to the WRB, and then the LYRB (Figures 2, 4, 8–10) imply a decrease of hydrothermal limitation of vegetation along the climate gradients. Previous studies also concluded an increasing trend of vegetation productivity from northwest to southeast of China, along with the increase of temperature and precipitation (e.g., Peng et al, 2023; Piao et al, 2005). Lower NPP in the HRB is primarily due to that the widespread insufficiency of hydrothermal condition for vegetation growth (Gao et al, 2016; Liu et al, 2017), which is confirmed by the greater importance of temperature and precipitation to the spatial pattern of NPP in the HRB (Figure 3a).…”

“…The comparative study between two or more study regions with obvious gradients in climate, biome, and topography is less seen. As the spatio-temporal patterns of NPP are deeply affected by the climate, land cover type, topography (Liang et al, 2015; Peng et al, 2023), the NPP in different regions across climate gradients may exhibit remarkably different spatial and temporal variability. Through this comparative study, we may achieve a more intuitional and comprehensive recognition on the core controls of the spatio-temporal patterns of NPP in different regions, as well as the response pattern of ecosystem to climate change.…”

Differentiation characteristics of karst vegetation resilience and its response to climate and ecological restoration projects

“…It is derived from the National Earth System Science Data Center (http://www.geodata.cn) during 1990–2018, with a spatial resolution of 5 km. This data set has been widely used to investigate the changes in vegetation growth and productivity in diverse ecoregions globally (Chen, Wang, et al, 2022; Peng et al, 2023). Based on the vegetation NDVI dataset, we implement seasonal and trend decomposition using Loess (STL, see the supplementary materials online for the method) decomposition to split the time‐series data in each grid into an overall trend, a repeating annual cycle, and a residual component (Okedi & Fisher, 2021; Scotch et al, 2021); and we use the residual component in our resilience analysis.…”