Specific Drivers and Responses to Land Surface Phenology of Different Vegetation Types in the Qinling Mountains, Central China

Abstract

Land surface phenology (LSP), as a precise bio-indicator that responds to climate change, has received much attention in fields concerned with climate change and ecology. Yet, the dynamics of LSP changes in the Qinling Mountains (QMs)—A transition zone between warm-temperate and north subtropical climates with complex vegetation structure—under significant climatic environmental evolution are unclear. Here, we analyzed the spatiotemporal dynamics of LSP for different vegetation types in the QMs from 2001 to 2019 and quantified the degree of influence of meteorological factors (temperature, precipitation, and shortwave radiation), and soil (temperature and moisture), and biological factors (maximum of NDVI and middle date during the growing season) on LSP changes using random forest models. The results show that there is an advanced trend (0.15 days/year) for the start of the growing season (SOS), a delayed trend (0.24 days/year) for the end of the growing season (EOS), and an overall extended trend (0.39 days/year) for the length of the growing season (LOS) in the QMs over the past two decades. Advanced SOS and delayed EOS were the dominant patterns leading to a lengthened vegetation growing season, followed by a joint delay of SOS and EOS, and the latter was particularly common in shrub and evergreen broadleaved forests. The growth season length increased significantly in western QMs. Furthermore, we confirmed that meteorological factors are the main factors affecting the interannual variations in SOS and EOS, especially the meteorological factor of preseason mean shortwave radiation (SWP). The grass and crop are most influenced by SWP. The soil condition has, overall, a minor influence the regional LSP. This study highlighted the specificity of different vegetation growth in the QMs under warming, which should be considered in the accurate prediction of vegetation growth in the future.

Spatiotemporal Characteristics and Heterogeneity of Vegetation Phenology in the Yangtze River Delta

“…However, due to the difference in research area, geographical environment and altitude, there are still differences in the value of the amplitude and trend of change. The conclusion of advanced SOS at different altitude grades is consistent with previous studies [17,20,43,66]. A phenology study of the European Alps showed that although EOS was advanced by 0.69 days per year on average [66], the results of EOS were different at different altitudes, and EOS was delayed between 1400 m and 1800 m. However, above 2000 m, EOS was advanced [43].…”

“…Temperatures at 2 m and total precipitation data were extracted from the ECMWF atmospheric reanalysis data [17] (https://www.ecmwf.int/en/publications (accessed on 10 December 2021)) and resampled to 500 m resolution. The PD dataset is available to download at a resolution of 30 arc seconds (approximately equal to 1 km at the equator) (https://www.worldpop.org/ (accessed on 15 February 2022)).…”

“…Random forest is a decision tree algorithm [59] which has been applied to identify the response mechanisms of vegetation phenology [11,17,18]. According to the concentration of phenological index values in the experimental area, one month before the start and end of phenology were selected (i.e., January, February, March, and September, October, November), and two months were extended before as reference (i.e., November, December for SOS and July, August for EOS).…”

“…Meanwhile, other studies have found a different conclusion in eastern China and Nanjing due to the complexity of the influencing factors [4,12,13]. Methods such as statistical analysis, correlation, and random forests were used to explore the temporal variation characteristics of phenology and response mechanisms [11,[14][15][16][17][18]. In terms of space, the study concluded that spring and autumn phenology in different regions are different with spatial heterogeneity [11,12,14,19].…”

“…Although these studies involved the spatial analysis and the conclusion of heterogeneity, little quantitative analysis and discussion of driving factors of heterogeneity has been shown. Some scholars have proposed that the phenological characteristics of different vegetation cover types are different [15,20] and that changes in vegetation cover types will lead to changes in phenology [17,21,22], but quantitative evaluation still requires further research.…”