Abstract
Based on the fifth version MOD09A1 product of NASA LP DAAC (Land Processes Distributed Active Center) from 2001 to 2016, an enhanced vegetation index (EVI) time series dataset in the Qinling Mountains was reconstructed; it was used to extract the plant phenological parameters of the region by employing the maximal slopes of the alteration method and threshold method. The results show that the Whittaker filter method was better than other methods for the reconstruction of shrubland, farmland, and forest in the Qinling Mountains. Based on the reconstructed EVI, the characteristics of vegetation coverage, the start of growth season (SOG), the end of growth season (EOG), and the length of growth season (LOG) in the Qinling Mountains were all analyzed. There was an increasing trend of vegetation coverage in most regions (about 89.93% of the monitored areas) over the Qinling Mountains in the past decades, and the average phenological distribution pattern in the Qinling Mountains was closely related to the local hydrothermal conditions. From the high altitude mountainous areas to the farming areas, the SOG was gradually postponed, the EOG was gradually earlier, and the LOG gradually shortened. Furthermore, the time series variation of SOG, EOG, and LOG from 2001 to 2016 in the Qinling Mountains was also studied. The variation showed that the SOG shifted earlier, which was more prominent in high-altitude areas, while, for some southern and northern foothills altitude below 500 m and a few areas of the eastern Funiu Mountains, the SOG was delayed. The EOG shifted later, which was more apparent in the northern Qinling foothills and mid- to low-altitude areas of the eastern ranges, and the LOG was extended. Finally, the studies of correlation analysis between the plant phenology and a temperature threshold of 10°C showed that global warming was the major factor affecting the phytophenology of the Qinling Mountains, and the effects were concentrated mainly in the 1000- to 2000-m zones of the southern and northern Qinling Mountains. Moreover, the northern Qinling foothills showed a greater response to accumulated temperature than the southern foothills, and the low-altitude area of the eastern Funiu Mountains exhibited the lowest correlation with the accumulated temperature.
Highlights
Phenology refers to periodic changes of organisms after their long-term adaptation to climate conditions, thereby developing corresponding growth and development cycles
Many studies have been devoted to investigating approaches for extracting phenological parameters based on remote sensing data, such as the normalized difference vegetation index (NDVI)-based threshold method and the maximal slope of alteration method that are indicative of vegetative types
This study analyzed the MODIS MOD13Q1 product with a 16-day interval, and it showed that when the NDVI of the high-vegetation coverage areas in the Qinling Mountains approached 0.8, the fluctuation decreased, which indicated that the chlorophyll responses of the vegetation
Summary
Phenology refers to periodic changes of organisms after their long-term adaptation to climate conditions, thereby developing corresponding growth and development cycles. It has been demonstrated that over the last several decades, the plant growth seasons in the northern hemisphere have gradually extended, which leads to an increase in carbon sequestration in highlatitude areas.[15] In China, many studies show similar trends in most regions using ground observation and satellite monitoring, Fang et al.[16] and Zheng et al.[17] discussed the effect of global warming on plant phenological changes, and the results show that the response of ahead (or delay) of phenophase to increasing (or decreasing) of temperature was nonlinear. Considerable effort has been dedicated to examining the phytophenological responses in areas of different altitudes in the Qinling Mountains,[28,29,30] and vegetation degeneration, its causes, and spatiotemporal changes of phenology in the Qinling Mountains have been analyzed. The study provides crucial evidence to understand the phytophenological response features at different altitudes that are under the dual influence of human activity and climate change
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