Abstract
This study applied grassland related multi-index and assessed the effects of climate change by investigating grassland responses to drought. This process was performed to study grassland vegetation dynamic accurately and evaluate the effect of drought in the Mongolian Plateau (MP). The spatial–temporal characteristics of grassland dynamic in terms of coverage (Fv), surface bareness (Fb), and net primary production (NPP) from 2000 to 2013 were explored. We implemented the maximum Pearson correlation to analyze the grassland vegetation in response to drought by using self-calibrating Palmer Drought Severity Index (scPDSI). Results show that Fv and NPP present an increasing trend (0.18 vs. 0.43). Fb showed a decreasing trend with a value of −0.16. The grassland Fv and NPP positively correlated with scPDSI, with a value of 0.12 and 0.85, respectively, and Fb was −0.08. The positive correlation between Fv and NPP accounted for 84.08%, and the positive correlation between Fv and scPDSI accounted for 93.88%. On the contrary, the area with a negative correlation between Fb and scPDSI was 57.43%. The grassland in the MP showed a recovery tendency. The increase in grassland caused by positive reaction was mainly distributed in the middle of Mongolia (MG), whereas that caused by counter response was mainly distributed in the east and west MG and northeast Inner Mongolia autonomous region of China (IM). The relevant results may provide useful information for policymakers about mitigation strategies against the inverse effects of drought on grassland and help to ease the losses caused by drought.
Highlights
As the earth’s largest terrestrial ecosystem, grassland plays an important role in ecosystem cycles [1,2,3]
Fb greater than 60% distributed over the southwestern and western Mongolian Plateau (MP), while Fb less than 40% mainly distributed over the northeastern and northern MP (Figure 1B)
The results provided a new understanding of drought-driven grassland change in the MP
Summary
As the earth’s largest terrestrial ecosystem, grassland plays an important role in ecosystem cycles [1,2,3]. Evaluating the dynamic change in grassland ecosystem quantitatively is urgent because grassland provides many economic products and ecological services [4,5]. Previous research investigated the impact of climate change on the grassland vegetation dynamic by using different indicators. The indicators to evaluate grassland vegetation dynamic by remote sensing technology mainly include the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), vegetation coverage (Fv), and net primary productivity (NPP) [6,7,8]. Some recent studies have proposed ground bareness (Fb) as another important parameter of global land cover change [9,10]. Research has focused on drought events by using the combination of identified NPP and NDVI [11]. Compared with single index analysis, the vegetation dynamic inversion based on multi-index can help to improve the reliability of results due to the diversities of analysis
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