**1. Introduction**

As the earth's largest terrestrial ecosystem, grassland plays an important role in ecosystem cycles [1–3]. 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) [6–8]. Some recent studies have proposed ground bareness (Fb) as another important parameter of global land cover change [9,10]. As an opposite concept of Fv, F<sup>b</sup> contains the attribute of surface reflectivity and temperature information

**Citation:** Zhang, Y.; Wang, Z.; Wang, Q.; Yang, Y.; Bo, Y.; Xu, W.; Li, J. Comparative Assessment of Grassland Dynamic and Its Response to Drought Based on Multi-Index in the Mongolian Plateau. *Plants* **2022**, *11*, 310. https://doi.org/10.3390/ plants11030310

Academic Editors: Bingcheng Xu and Zhongming Wen

Received: 20 October 2021 Accepted: 21 December 2021 Published: 25 January 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

of grassland vegetation rather than a complementary set of coverage. In recent years, 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.

Drought is a natural phenomenon where the availability of water is significantly lower than normal for a long period and the supply cannot meet the existing demand [12]. With global warming, drought is quickly becoming a devastating environment incident [13]. The International Disaster Database estimated that droughts attributed approximately 5% of the natural disasters over the globe, and the losses caused by drought disasters accounted for more than 30% of those of the natural hazards [14]. Drought has been a crucial scientific issue in the domain of climate research due to its negative effects on water resources, livestock husbandry development, and local economy [15–17]. The influence of drought on terrestrial ecosystems is becoming increasingly acute [18]. The grassland dynamics and its response to driving factors are always investigated by researchers because grassland is more susceptible to droughts than other ecosystems [19]. Previous studies explored the impact of drought on grassland vegetation dynamic at multiple regions. Some researchers evaluated the NPP distribution and response to drought in Europe [20]. Their results suggest that rainfall deficit and extreme summer heat reduce the vegetation productivity in Eastern and Western Europe, respectively. Another study strengthened the conclusion of drought-induced reduction in NPP over the past decade in central Asia [21]. Therefore, a better understanding in grassland vegetation dynamic and its feedback on climate change will improve the local economic development, especially for the typical farming and pastoral areas.

The Mongolian Plateau (MP) is a typical arid and semiarid area, with natural grassland as the dominant vegetation type. It often suffered from different conditions of drought due to the decreasing water resource supply and climate change [22,23]. Droughts over the last century induced a heap of negative effects, such as water resource shortages, threat of food shortages, and vegetation degradation [24–27]. Therefore, quantitative assessment of grassland vegetation dynamic and the effect of droughts is urgent. In accordance with the recent analysis, the summer drought has contributed to the increasing extreme droughts since the 1990s [28]. Some researchers have proven that the self-calibrating Palmer Drought Severity Index (scPDSI) is suitable than other drought indexes when considering the impact of precipitation and temperature on the soil moisture in Inner Asia [29]. Another research from Wang revealed that the global grassland scPDSI value has a slightly increasing trend with a rate of 0.0119 per year [30]. However, there is still a lack of research on grassland vegetation dynamic and its response to droughts of the MP.

There are many related previous studies focused on single or two vegetation indexes to evaluate the grassland dynamic and its response to climate factors [31–33]. To enrich vegetation related research indicators, we selected Fv, Fb, and NPP to reflect the grassland vegetation dynamic for improving the reliability of conclusions. We evaluated the grassland response to droughts during the study period. A combined analysis of the three indexes in different drought severity areas was quantitatively assessed to enhance the credibility of the results. The results may provide a scientific basis for guiding ecological environment improvement and drought prevention for typical farming and pastoral areas in the world.

#### **2. Results**

#### *2.1. Spatial and Temporal Distribution of Fv, F<sup>b</sup> , and NPP*

The spatial distribution of long-term mean grass Fv, Fb, and NPP in the Mongolia Plateau is shown in Figure 1. The grass F<sup>v</sup> value is relatively higher in northern and northeastern MP, while lower in southwestern and western MP (Figure 1A). On the contrary, F<sup>b</sup> greater than 60% distributed over the southwestern and western MP, while F<sup>b</sup> less than 40% mainly distributed over the northeastern and northern MP (Figure 1B). The mean actual NPP showed obvious spatial heterogeneity, too (Figure 1C). Areas with mean actual NPP larger than 200 g C/(m<sup>2</sup> ·yr) were scattered in the northern and northeastern MP with good

vegetation growth conditions. Areas with mean actual NPP lower than 100 g C/(m<sup>2</sup> ·yr) were mainly scattered in the regions with relatively scarce water resources and vegetation in the transition area of grassland and desert such as southwestern and western MP. We counted the different pixel values of grassland F<sup>v</sup> (Figure 1a), F<sup>b</sup> (Figure 1b), and NPP (Figure 1c) in the MP. The average Fv, Fb, and NPP values were 18.42%, 15.53%, and 61.41 g C/(m<sup>2</sup> ·yr), respectively, whereas the corresponding distribution rates of their peak value were 60–80%, 40–60%, and 150–200 g C/(m<sup>2</sup> ·yr). The Fv, Fb, and NPP of IM were 9.17%, 6.84%, and 24.82 g C/(m<sup>2</sup> ·yr), respectively. MG had higher values of the three indexes than IM. The corresponding distribution rates of IM and MG peak values were similar to the MP. C/(m2·yr) were mainly scattered in the regions with relatively scarce water resources and vegetation in the transition area of grassland and desert such as southwestern and western MP. We counted the different pixel values of grassland Fv (Figure 1a), Fb (Figure 1b), and NPP (Figure 1c) in the MP. The average Fv, Fb, and NPP values were 18.42%, 15.53%, and 61.41 g C/(m2·yr), respectively, whereas the corresponding distribution rates of their peak value were 60–80%, 40–60%, and 150–200 g C/(m2·yr). The Fv, Fb, and NPP of IM were 9.17%, 6.84%, and 24.82 g C/(m2·yr), respectively. MG had higher values of the three indexes than IM. The corresponding distribution rates of IM and MG peak values were similar to the MP.

The spatial distribution of long-term mean grass Fv, Fb, and NPP in the Mongolia Plateau is shown in Figure 1. The grass Fv value is relatively higher in northern and northeastern MP, while lower in southwestern and western MP (Figure 1A). On the contrary, Fb greater than 60% distributed over the southwestern and western MP, while Fb less than 40% mainly distributed over the northeastern and northern MP (Figure 1B). The mean actual NPP showed obvious spatial heterogeneity, too (Figure 1C). Areas with mean actual NPP larger than 200 g C/(m2·yr) were scattered in the northern and northeastern MP with good vegetation growth conditions. Areas with mean actual NPP lower than 100 g

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*2.1. Spatial and Temporal Distribution of Fv, Fb, and NPP* 

**2. Results** 

**Figure 1.** Spatial distribution of grassland Fv, Fb and NPP and statistics of the corresponding pixel number during 2000–2013. ((**A**), (**B**) and (**C**) are the spatial distribution of Fv, Fb and NPP, respec-**Figure 1.** Spatial distribution of grassland Fv, F<sup>b</sup> and NPP and statistics of the corresponding pixel number during 2000–2013. ((**A**–**C**) are the spatial distribution of Fv, F<sup>b</sup> and NPP, respectively; (**a**–**c**) is the statistics of the corresponding pixel number).

tively; (**a**–**c**) is the statistics of the corresponding pixel number). In this study, grassland F<sup>v</sup> in the MP exhibited an increasing trend from 2000 to 2013, with a 14-year cumulative increment of 0.18 (Figure 2). MG had a higher F<sup>v</sup> value than IM (0.21 vs. 0.09). On the contrary, grassland F<sup>b</sup> showed an overall decreasing trend in the MP, with the decreased rate of −0.08. The decrease rates of MG and IM were −0.09 and −0.05, respectively. NPP had the largest change rate compared with the two other indexes, with a 14-year cumulative increment of 0.43. MG had a higher increase value than IM (0.63 vs. 0.39).

### *2.2. Dynamic Analysis of Grassland*

The changing trend and significance levels of grassland Fv, Fb, and NPP in the MP from 2000 to 2013 are shown in Figure 3. The growth rate of F<sup>v</sup> occupied 60.51% of the MP grassland, mainly found in the east and central MG, east Xing'an, south Ordos, and central IM (Figure 3A). On the contrary, the regions of F<sup>b</sup> exhibiting decreasing trends were extremely larger than that with increasing trends (92.64% vs. 7.36%), with the decreased rate of −0.0005/14a. The decreased regions were mainly found in the entire MP, typically occurring in the southwest and middle MP (Figure 3B). The NPP increasing areas occupied 79.54% of the MP grassland, mainly found in Kent Mountains and Hanggai Mountains in MG and east Xing'an League in IM (Figure 3C). F<sup>v</sup> with clear increases was distributed in the east Dornod, Hangai Mountains, and Kent Mountains in MG, and east Xing'an and south Ordos in IM (Figure 3a). F<sup>b</sup> exhibited a significant decrease (SD) and an extremely significant decrease (ESD), accounting for 14.77% and 11.61% of the MP grassland, respectively (Figure 3b). The regions of NPP with a significant increase (SI) accounted for 4.27% of the MP grassland. The regions with significant increase were mainly distributed in the east Selenge in MG and east Xing'an in IM (Figure 3c). In this study, grassland Fv in the MP exhibited an increasing trend from 2000 to 2013, with a 14-year cumulative increment of 0.18 (Figure 2). MG had a higher Fv value than IM (0.21 vs. 0.09). On the contrary, grassland Fb showed an overall decreasing trend in the MP, with the decreased rate of −0.08. The decrease rates of MG and IM were −0.09 and −0.05, respectively. NPP had the largest change rate compared with the two other indexes, with a 14-year cumulative increment of 0.43. MG had a higher increase value than IM (0.63 vs. 0.39).

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**Figure 2.** The statistics of the temporal distribution of grassland Fv, Fb, and NPP. ((**A**), (**B**), and (**C**) are the statistics of Fv, Fb, and NPP during 2000–2013, respectively). **Figure 2.** The statistics of the temporal distribution of grassland Fv, F<sup>b</sup> , and NPP. ((**A**–**C**) are the statistics of Fv, F<sup>b</sup> , and NPP during 2000–2013, respectively). *Plants* **2022**, *11*, x FOR PEER REVIEW 5 of 17

**Figure 3.** The changing trend and significance levels of grassland Fv, Fb and NPP. ((**A**), (**B**) and (**C**) are the changing trend of grassland Fv, Fb and NPP, respectively. (**a**–**c**) are the corresponding significance levels. ESD (extremely significant decrease, slope < 0, *p* < 0.01); SD (significant decrease, slope < 0, 0.01 < *p*<0.05); NSC (no significant change, slope = 0, *p* > 0.05); SI (Significant Increase, Slope > 0, 0.01 < *p*< 0.05); ESI (extremely significant increase, slope > 0, *p* < 0.01)). **Figure 3.** The changing trend and significance levels of grassland Fv, F<sup>b</sup> and NPP. ((**A**–**C**) are the changing trend of grassland Fv, F<sup>b</sup> and NPP, respectively. (**a**–**c**) are the corresponding significance levels. ESD (extremely significant decrease, slope < 0, *p* < 0.01); SD (significant decrease, slope < 0, 0.01 < *p* < 0.05); NSC (no significant change, slope = 0, *p* > 0.05); SI (Significant Increase, Slope > 0, 0.01 < *p* < 0.05); ESI (extremely significant increase, slope > 0, *p* < 0.01)).

The correlation coefficient of grassland indexes and scPDSI was analyzed because grassland dynamic is driven by global climate change (Figure 4). Fv and NPP were posi-

counted for −0.08. The areas with a positive correlation between Fv, NPP, and scPDSI were approximately 84.08 and 93.88%. On the contrary, a negative correlation between Fb and scPDSI accounted for 57.43%. The grassland regions (2.02%) showed a significant positive correlation (*p* < 0.05) between Fv and PSDI, mainly distributed in Baotou, Hohhot, and south Ulaan Chab in IM. The grassland areas (8.28%) showed significant negative correlation (*p* < 0.05), mainly distributed in the Kent mountain area of MG and Tong Liao, Chi Feng, and Xilin Gol of IM. In the regions with a positive correlation between NPP and PSDI, 19.57% of them showed a significant positive correlation (*p* < 0.05), mainly distrib-

uted over the west, north, and central MG, and central IM.

*2.3. Correlation Analysis of Grassland Indexes to scPDSI* 

#### *2.3. Correlation Analysis of Grassland Indexes to scPDSI*

The correlation coefficient of grassland indexes and scPDSI was analyzed because grassland dynamic is driven by global climate change (Figure 4). F<sup>v</sup> and NPP were positively correlated with scPDSI, with a value of 0.12 and 0.85, respectively, whereas F<sup>b</sup> accounted for −0.08. The areas with a positive correlation between Fv, NPP, and scPDSI were approximately 84.08 and 93.88%. On the contrary, a negative correlation between F<sup>b</sup> and scPDSI accounted for 57.43%. The grassland regions (2.02%) showed a significant positive correlation (*p* < 0.05) between F<sup>v</sup> and PSDI, mainly distributed in Baotou, Hohhot, and south Ulaan Chab in IM. The grassland areas (8.28%) showed significant negative correlation (*p* < 0.05), mainly distributed in the Kent mountain area of MG and Tong Liao, Chi Feng, and Xilin Gol of IM. In the regions with a positive correlation between NPP and PSDI, 19.57% of them showed a significant positive correlation (*p* < 0.05), mainly distributed over the west, north, and central MG, and central IM. *Plants* **2022**, *11*, x FOR PEER REVIEW 6 of 17

**Figure 4.** The spatial distribution and pixel frequency of correlation coefficient between scPDSI and three vegetation indicators ((**A**–**C**) are the correlation coefficient spatial distribution and (**a**–**c**) corresponding its pixel frequency). **Figure 4.** The spatial distribution and pixel frequency of correlation coefficient between scPDSI and three vegetation indicators ((**A**–**C**) are the correlation coefficient spatial distribution and (**a**–**c**) corresponding its pixel frequency).

The area of grassland Fv, Fb, and NPP responding to scPDSI in the control response

(Figure 5A–C). The control response to grassland increase from Fv, Fb, and NPP to scPDSI appear in similar areas, mainly concentrating on central, north, and west MG, and west IM. On the contrary, the control response to grassland decrease accounts for 10.09, 17.10, and 14.98% (Figure 5a–c). It is mainly concentrated on the south Sayan Mountains, south Hangai Mountains, and Dornod in MG, and northeast and south IM. The area of grassland Fv, Fb, and NPP responding to scPDSI in the counter response for grassland increase accounts for 44.73, 37.76, and 40.34% of the total area, respectively (Figure 6A–C). The counter response to grassland increase from Fv, Fb, and NPP to scPDSI appear in similar areas, mainly concentrating on the northeast and west MG and northeast and south IM. On the contrary, the counter response to grassland decreases accounts for 8.63, 7.15, and 16.45%

(Figure 6a–c). It is mainly concentrated on central MG and west IM.

*2.4. Changes and Trends in Grassland Response to Drought* 
