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Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological and environmental management and planning. Based on GIMMS NDVI3g and meteorological data, the spatial and temporal evolution characteristics of NDVI were analyzed from multiple perspectives with the help of linear trend and Mann–Kendall (MK) test methods using arcgis and the R language spatial analysis module, combined with partial correlation coefficients and residual analysis methods to analyze the impacts of climate change and human activities on the regional vegetation cover of the Ferghana Basin from 1982 to 2015. NDVI driving forces. The results showed the following: (1) The growing season of vegetation NDVI in the Ferghana Basin showed an increasing trend in the 34-year period, with an increase rate of 0.0044/10a, and the spatial distribution was significantly different, which was high in the central part of the country and low in the northern and southern parts of the country. (2) Temperature and precipitation simultaneously co-influenced the vegetation NDVI growth season, with most of the temperature and precipitation contributing in the spring, most of the temperature in the summer being negatively phased and the precipitation positively correlated, and most of the temperature and precipitation in the fall inhibiting vegetation NDVI growth. (3) The combined effect of climate change and human activities was the main reason for the overall rapid increase and great spatial variations in vegetation NDVI in China, and the spatial distribution of drivers, namely human activities and climate change, contributed 44.6% to vegetation NDVI in the growing season. The contribution of climate change and human activities to vegetation NDVI in the Ferghana Basin was 62.32% and 93.29%, respectively. The study suggests that more attention should be paid to the role of human activities and climate change in vegetation restoration to inform ecosystem management and green development. [ABSTRACT FROM AUTHOR]

Tree radial growth can have significantly different responses to climate change depending on the environment. To elucidate the effects of climate on radial growth and stable carbon isotope (δ13C) fractionation of Qinghai spruce (Picea crassifolia), a widely distributed native conifer in northwestern China in different environments, we developed chronologies for tree-ring widths and δ13C in trees on the southern and northern slopes of the Qilian Mountains, and analysed the relationship between these tree-ring variables and major climatic factors. Tree-ring widths were strongly influenced by climatic factors early in the growing season, and the radial growth in trees on the northern slopes was more sensitive to climate than in trees on the southern. Tree-ring δ13C was more sensitive to climate than radial growth. δ13C fractionation was mainly influenced by summer temperature and precipitation early in the growing season. Stomatal conductance more strongly limited stable carbon isotope fractionation in tree rings than photosynthetic rate did. The response between tree rings and climate in mountains gradually weakened as climate warmed. Changes in radial growth and stable carbon isotope fractionation of P. crassifolia in response to climate in the Qilian Mountains may be further complicated by continued climate change. [ABSTRACT FROM AUTHOR]

Snow is an important source of freshwater in the Tianshan Mountains of Central Asia. This study established 18 tree ring width chronologies and compound chronologies and analyzed the effects of snow depth, measured both by remote sensing and at meteorological stations, on the radial growth of spruce trees. The results showed that the established standard chronology of tree ring width is suitable for the analysis of tree ring climatology. The correlation coefficient of the ring width index (RWI) and the remote sensing snow depth was greater than that of the meteorological station snow depth. For the remote sensing snow depth, the correlation coefficients were greater in the winter and spring months compare to other periods, while the correlation coefficients of the meteorological stations were greater only in the winter. The nonlinear method (BRNN) showed good fitting in the reconstruction of the historical snow depth. The reconstructed snow depth exhibited a decreasing trend in the Bharakonu Mountains (BM), Narathi Mountains (NM), and Halke mountains (KM) sub-regions in the overall reconstructed period; however, the trends were inconsistent in both the historical and the observed periods, indicating the importance of reconstructing snow depth in the Tianshan Mountains. [ABSTRACT FROM AUTHOR]