Phenology Response to Climatic Dynamic across China’s Grasslands from 1985 to 2010

Abstract

Because the dynamics of phenology in response to climate change may be diverse in different grasslands, quantifying how climate change influences plant growth in different grasslands across northern China should be particularly informative. In this study, we explored the spatiotemporal variation of the phenology (start of the growing season [SOS], peak of the growing season [POS], end of the growing season [EOS], and length of the growing season [LOS]) across China’s grasslands using a dataset of the GIMMS3g normalized difference vegetation index (NDVI, 1985–2010), and determined the effects of the annual mean temperature (AMT) and annual mean precipitation (AMP) on the significantly changed phenology. We found that the SOS, POS, and EOS advanced at the rates of 0.54 days/year, 0.64 days/year, and 0.65 days/year, respectively; the LOS was shortened at a rate of 0.62 days/year across China’s grasslands. Additionally, the AMT combined with the AMP explained the different rates (ER) for the significantly dynamic SOS in the meadow steppe (R2 = 0.26, p = 0.007, ER = 12.65%) and typical steppe (R2 = 0.28, p = 0.005, ER = 32.52%); the EOS in the alpine steppe (R2 = 0.16, p < 0.05, ER = 6.22%); and the LOS in the alpine (R2 = 0.20, p < 0.05, ER = 6.06%), meadow (R2 = 0.18, p < 0.05, ER = 16.69%) and typical (R2 = 0.18, p < 0.05, ER = 19.58%) steppes. Our findings demonstrated that the plant phenology in different grasslands presented discrepant dynamic patterns, highlighting the fact that climate change has played an important role in the variation of the plant phenology across China’s grasslands, and suggested that the variation and relationships between the climatic factors and phenology in different grasslands should be explored further in the future.

Water and heat availability are drivers of the aboveground plant carbon accumulation rate in alpine grasslands on the Tibetan Plateau

“…Two indicators of plant phenology (SOS and POS) on the plateau were calculated from 1982 to 2013, and were used to obtain the phenology indicators in 2015 with simple linear regression analysis. In this calculation, the NDVI dataset was firstly smoothed by applying the Fourier series model, as shown in Equation 2 (Wang, Zhou, & Peng, ):…”

“…Then, the SOS and POS were determined using the derivatives of the fitted Fourier equation. As illustrated in Figure b, SOS was defined as the Julian day when the derivative reached the highest value, and POS was the Julian day when the estimated maximum NDVI value occurred (Wang et al, ). In this study, Matlab R2012b (Natick, MA, USA) was used to fit the data and to calculate the phenology indicators.…”

“…Normalized difference vegetation index (NDVI, 1982(NDVI, -2013 values were obtained from the satellite-based surface reflectance data acquired by advanced very high resolution radiometer (AVHRR) sensors. In this calculation, the NDVI dataset was firstly smoothed by applying the Fourier series model, as shown in Equation 2 (Wang, Zhou, & Peng, 2018): In this calculation, the NDVI dataset was firstly smoothed by applying the Fourier series model, as shown in Equation 2 (Wang, Zhou, & Peng, 2018):…”

Spatio-Temporal Changes of Oases in the Hexi Corridor over the Past 30 Years

“…The waterbody has the highest accuracy due to its distinct reflectance characteristic. The difference of vegetation phenology derived from the remote-sensing images obtained in different time also increased uncertainty [40]. For example, part of the vegetation on these images obtained at the beginning or end of the vegetation flourishing season may not be extracted, especially for the lower-cover vegetation.…”

Declining Effect of Precipitation on the Normalized Difference Vegetation Index of Grasslands in the Inner Mongolian Plateau, 1982–2010

“…NDVI is considered to be one of the best indicators for monitoring land vegetation cover and it has been widely used in the study of large-scale vegetation change [8]. The peak NDVI value reflects synthetically the photosynthetic activity of plants under current environmental conditions [9,10].…”