Due to the fragility of the habitats in mountain nature reserves, the vegetation is extremely sensitive to climate change, and its phenological changes are more specific. Therefore, it is of great significance to study the effects of topography and climate on the vegetation phenology in mountain nature reserves. Based on the vegetation phenology data retrieved from MODIS EVI2 during 2000 to 2017, combined with temperature data, spatial trend analysis and correlation analysis methods were used to study the effects of topographic and climatic factors on vegetation phenology in the Niubeiliang Nature Reserve of the Qinling Mountains. The results showed that the GSS (growing season start) was advanced with a rate of 4.24 days/10a, and the rates in the northern and southern slopes were almost the same; the GSE (growing season end) was delayed with a rate of 3.29 days/10a, and the rate in the northern slope was faster; and the GSL (growing season length) was prolonged. There were seasonal differences and north–south differences in the effects of topography on the phenophase. The phenophase changed regularly with the increase in altitude. The higher the altitude, the more significant the effect. The steeper the slope, the later the GSS, the earlier the GSE, and the more significant its effect on the GSE. The aspect had little effect on GSS but a more significant effect on GSE, which was the latest on the sunny slope and the earliest on the zero slope. Temperature affected both the GSS advance and the GSE delay, and both had a time-lag effect of approximately 2–3 months. Its effect was more significant in the GSE, in the southern slopes, and in the high-altitude areas.

Larix chinensis Beissn., as a native, dominant and climate-sensitive coniferous species at Mount Taibai timberline, Qinling mountains, is rarely disturbed by anthropogenic activities; thus, it is an ideal proxy for the investigation of climate change or vegetation evolution. In this study, we applied dendrochronological methods to the L. chinensis tree-ring series from Mt. Taibai and investigated the relationships between tree-ring widths and NDVI/climate factors using Pearson correlation analysis. On the basis of the remarkable positive correlations (r = 0.726, p < 0.01, n = 23) between local July normalized difference vegetation indices (NDVI) and tree-ring width indices, the regional 146-year annual maximum vegetation density was reconstructed using a regression model. The reconstructed NDVI series tracked the observed data well, as the trans-function accounted for 52.8% of observed NDVI variance during AD 1991–2013. After applying an 11-year moving average, five dense vegetation coverage periods and six sparse vegetation coverage periods were clearly presented. At a decadal scale, this reconstruction was reasonably and negatively correlated with a nearby historical-record-based dryness/wetness index (DWI), precisely verifying that local vegetation cover was principally controlled by hydrothermal variations. Spectral analysis unveiled the existence of 2–3-year, 2–4-year, 5–7-year and 7–11-year cycles, which may potentially reflect the connection between local NDVI evolution and larger-scale circulations, such as the El Niño–Southern Oscillation (ENSO) and solar activity. This study is of great significance for providing a long-term perspective on the dynamics of vegetation cover in the Qinling mountains, and could help to guide expectations of future forest variations.

Vertical vegetation differentiation is the most important form of spatial pattern in mountainous areas. It is of great significance to accurately divide vegetation into vertical zones for the study of mountain ecosystems and ecological protection. In order to accurately divide the vertical zone of mountain vegetation and determine the spatial distribution of mountain vegetation, the relationship between the vegetation index of various vegetation types and altitude was examined using remote sensing and geographic information technology. Taking Taibai Mountain, the main peak of the Qinling Mountains in China, as the study area, based on the difference in NDVI between summer and autumn (DNSA), this work constructed a DEM-NDVI scatter plot and quantified the boundary of the vertical zone by the half-peak width calculation method. The findings showed that: (1) the vertical distribution pattern of mountain vegetation may very well be reflected in the scatterplot that NDSA and DEM created; (2) Six vertical belts could be accurately identified to the meter level on Taibai Mountain’s south slope. Up to the altitude, the oak forest zone from the bottom of the mountain to the elevation of 1919 m, the pine-oak mixed forest zone is distributed in 1919–2331 m, the birch forest is distributed in 2115–2585 m, the fir forest is distributed in 2516–3150 m, the redwood forest is distributed in 3109–3551 m, and the alpine scrub meadow is distributed in 3551 m to the peak. On the north slope, 1053–2087 m above sea level is oak forest, 2087–2693 is birch forest, 2562–3006 is fir forest, 2987–3513 m is redwood forest, and 3513 to the top of the mountain is alpine scrub meadow; and (3) the distribution pattern of the vegetation vertical belt on the DEM-NDVI scatter plot was essentially compatible with the vegetation classification results derived from remote sensing images. The DEM-NDVI scatter plot can reflect the average distribution of vegetation population and can more accurately express the characteristics of vegetation vertical zone changes with altitude.