*3.2. Climate Factors on Grassland Vegetation Dynamic*

In this study, we assessed the grassland dynamic on the basis of Fv, Fb, and NPP and their impact on droughts during 2001 to 2013. The results provided a new understanding of drought-driven grassland change in the MP. Climate variations, such as temperature and precipitation, influenced terrestrial vegetation directly. These climate factors regulated soil respiration, photosynthesis, growth status, and distribution [40]. Here, we calculated the temporal trends of temperature, precipitation, and radiation during the study period (Figure 7). The temperature in this study showed a downward trend (−0.03 ◦C), whereas precipitation and radiation showed an increasing trend (2.02 mm and 3.39 MJ/m<sup>2</sup> ) due to the short study period. Evidence shows that global warming is definitely occurring, and the climate in our study area tended to be wet and warm [41]. Typically, the combination of warmer temperature and higher precipitation concentration during the early growing season possibly increased NPP, partly by lengthening the growing season [11]. The related research shows that the carbon sequestration capacity of grassland ecosystem is enhanced by increased precipitation, which supports our findings [42,43]. Another study revealed that global warming helps to increase the productivity and carbon storage of grasslands in China [34]. culated the temporal trends of temperature, precipitation, and radiation during the study period (Figure 7). The temperature in this study showed a downward trend (−0.03 °C), whereas precipitation and radiation showed an increasing trend (2.02 mm and 3.39 MJ/m2) due to the short study period. Evidence shows that global warming is definitely occurring, and the climate in our study area tended to be wet and warm [41]. Typically, the combination of warmer temperature and higher precipitation concentration during the early growing season possibly increased NPP, partly by lengthening the growing season [11]. The related research shows that the carbon sequestration capacity of grassland ecosystem is enhanced by increased precipitation, which supports our findings [42,43]. Another study revealed that global warming helps to increase the productivity and carbon storage of grasslands in China [34].

In this study, we assessed the grassland dynamic on the basis of Fv, Fb, and NPP and their impact on droughts during 2001 to 2013. The results provided a new understanding of drought-driven grassland change in the MP. Climate variations, such as temperature and precipitation, influenced terrestrial vegetation directly. These climate factors regulated soil respiration, photosynthesis, growth status, and distribution [40]. Here, we cal-

*Plants* **2022**, *11*, x FOR PEER REVIEW 8 of 17

shows a recovery trend in the MP, which agrees with our findings.

*3.2. Climate Factors on Grassland Vegetation Dynamic* 

pixel frequency).

**3. Discussion**  *3.1. Methodology* 

**Figure 6.** The counter response of Fv, Fb, and NPP to PDSI changes in the Mongolian plateau from 2000 to 2013. ((**A**—**C**) are the correlation coefficient spatial distribution and (**a**–**c**) corresponding its

The current study used the slope-combined analysis based on multi-index to simulate grassland vegetation dynamic and monitor grassland response to droughts. The hypothesis is that grassland Fv and NPP dynamic are a positive feedback, whereas Fb is on the contrary. Previous studies applied single index, such as NDVI, Fv, and NPP, to simulate the grassland dynamic. However, many uncertainties remain due to the inversion model or the uncertainty of dataset itself [34]. The advantage of the current method is the reference of Fb index. Our findings show that 12.93% of the grassland in the MP experiences an increasing trend compared with 0.73% of the grassland that experienced a decreasing trend during the study period. Several studies about grassland NPP showed that grassland has an increasing trend in the similar area during the study period [35,36]. Similarly, studies on vegetation indexes, such as NDVI, Fv, and EVI, show an increasing trend of grassland vegetation [37–39]. Thus, the present studies confirmed that the grassland

**Figure 7.** Temporal variations of meteorological variables during 2000–2013 ((**A**–**C**) are the annual mean temperature, annual cumulative precipitation, and annual cumulative solar radiation, respectively). **Figure 7.** Temporal variations of meteorological variables during 2000–2013 ((**A**–**C**) are the annual mean temperature, annual cumulative precipitation, and annual cumulative solar radiation, respectively).

#### *3.3. The Role of Ecological Policies in Grassland Restoration*

As a limited resource, water is necessary for plant growth and development, especially in arid and semi-arid ecosystems [44]. Evidence showed that grasslands experience different degrees of drought in the MP (Figure 8A-1). Although drought associates with decreased precipitation, increased precipitation does not necessarily weaken the drought [45,46]. A slight reduction of drought is observed in the MP (−0.02), mainly concentrating in the western and eastern MG (Figure 8A-2). This finding is consistent with other studies that used SPI and SPEI to show drought [47–49]. We fitted the response from grassland Fv, Fb, and NPP to scPDSI, and the results showed a recovery trend (Figure 8B-1,B-2). Grassland increased regions are obviously larger than the decrease regions (12.93% vs. 0.73%), strongly confirming the recovery of grassland in the MP. The grassland increase regions with control response to drought mainly distributed in the central MP. Few human activities were found in these areas, and the vegetation growth was mainly affected by natural climatic factors [50]. The grassland increase regions with counter response to drought were in the eastern and western MG and northeast IM. This finding shows that other factors, such as human activities affecting local grassland restoration, are greater than the climate factors [51]. The distribution of grassland decrease regions with control response and counter response is minimal (0.44% vs. 0.29%). The grassland decrease regions with control response mainly distributed in south central IM and north MG. This finding reveals the grassland degradation in these areas are under complex influence factors, such as increase pressures from people and livestock populations [52].

tors, such as increase pressures from people and livestock populations [52].

*3.3. The Role of Ecological Policies in Grassland Restoration* 

As a limited resource, water is necessary for plant growth and development, especially in arid and semi-arid ecosystems [44]. Evidence showed that grasslands experience different degrees of drought in the MP (Figure 8A-1). Although drought associates with decreased precipitation, increased precipitation does not necessarily weaken the drought [45,46]. A slight reduction of drought is observed in the MP (−0.02), mainly concentrating in the western and eastern MG (Figure 8A-2). This finding is consistent with other studies that used SPI and SPEI to show drought [47–49]. We fitted the response from grassland Fv, Fb, and NPP to scPDSI, and the results showed a recovery trend (Figure 8B-1,B-2). Grassland increased regions are obviously larger than the decrease regions (12.93% vs. 0.73%), strongly confirming the recovery of grassland in the MP. The grassland increase regions with control response to drought mainly distributed in the central MP. Few human activities were found in these areas, and the vegetation growth was mainly affected by natural climatic factors [50]. The grassland increase regions with counter response to drought were in the eastern and western MG and northeast IM. This finding shows that other factors, such as human activities affecting local grassland restoration, are greater than the climate factors [51]. The distribution of grassland decrease regions with control response and counter response is minimal (0.44% vs. 0.29%). The grassland decrease regions with control response mainly distributed in south central IM and north MG. This finding reveals the grassland degradation in these areas are under complex influence fac-

**Figure 8.** The spatial distribution (**A-1**) and changing trend (**A-2**) of scPDSI and the reaction of Fv, Fb, and NPP to scPDSI changes (**B-1**,**B-2**) in the MP from 2000 to 2013. **Figure 8.** The spatial distribution (**A-1**) and changing trend (**A-2**) of scPDSI and the reaction of Fv, F<sup>b</sup> , and NPP to scPDSI changes (**B-1**,**B-2**) in the MP from 2000 to 2013.

#### **4. Materials and Methods 4. Materials and Methods**

#### *4.1. Study Area 4.1. Study Area*

The MP is located in Siberia in the north to the northern Yinshan in the south, from the Outer Xing'an Mountains in the east, to the Altai Mountains in the west (37°22′–53°20′ N and 87°43′–126°04′ E, Figure 9). The Altai Mountains in the southwest, the Kent Mountains in the north, and the outreach area of the Xing'an Mountains in the east are found in the study area, with the mean elevation of 1580 m (Figure 9a). The MP mainly includes Mongolia and Inner Mongolia autonomous region of China, with a total area of approxi-The MP is located in Siberia in the north to the northern Yinshan in the south, from the Outer Xing'an Mountains in the east, to the Altai Mountains in the west (37◦220–53◦200 N and 87◦430–126◦040 E, Figure 9). The Altai Mountains in the southwest, the Kent Mountains in the north, and the outreach area of the Xing'an Mountains in the east are found in the study area, with the mean elevation of 1580 m (Figure 9a). The MP mainly includes Mongolia and Inner Mongolia autonomous region of China, with a total area of approximately 1.56 <sup>×</sup> <sup>10</sup><sup>6</sup> and 1.18 <sup>×</sup> <sup>10</sup><sup>6</sup> km<sup>2</sup> , respectively. The MP climate is dry and lacks precipitation due to its long distance from the ocean. The annual average temperature and mean annual rainfall is 4.12 ◦C and 269 mm, respectively. The MP has a wide variety of regional climates, and most of them are from arid to humid from west to east. All regions are sensitive and vulnerable to drought. The grassland types mainly include grasslands, woody savannas, savannas, and shrub lands in descending order (Figure 9b).

savannas, savannas, and shrub lands in descending order (Figure 9b).

mately 1.56 × 106 and 1.18 × 106 km2, respectively. The MP climate is dry and lacks precipitation due to its long distance from the ocean. The annual average temperature and mean annual rainfall is 4.12 °C and 269 mm, respectively. The MP has a wide variety of regional climates, and most of them are from arid to humid from west to east. All regions are sensitive and vulnerable to drought. The grassland types mainly include grasslands, woody

**Figure 9.** Elevation of the MP (**a**) and grassland types derived from MODIS land cover product (MCD12Q1, IGBP) by the year of 2013 (**b**). **Figure 9.** Elevation of the MP (**a**) and grassland types derived from MODIS land cover product (MCD12Q1, IGBP) by the year of 2013 (**b**).

#### *4.2. Data Source and Processing 4.2. Data Source and Processing*

We obtained the global land cover maps of 2013 from the MODIS data products (MCD12Q1, http://modis-land.gsfc.nasa.gov/landcover.html/, accessed on 24 February 2020) with the International Geosphere-biosphere Programme (IGBP) classification system. The IGBP classification system defines 17 classes of primary land cover types. In this study, classes 1 to 5 were reclassified as forest, classes 6 to 9 were reclassified as shrubland, classes 12 and 13 were reclassified as farmland, classes 15–16 were reclassified as water, We obtained the global land cover maps of 2013 from the MODIS data products (MCD12Q1, http://modis-land.gsfc.nasa.gov/landcover.html/, accessed on 24 February 2020) with the International Geosphere-biosphere Programme (IGBP) classification system. The IGBP classification system defines 17 classes of primary land cover types. In this study, classes 1 to 5 were reclassified as forest, classes 6 to 9 were reclassified as shrubland, classes 12 and 13 were reclassified as farmland, classes 15–16 were reclassified as water, and class 17 was reclassified as city (Table 1).

and class 17 was reclassified as city (Table 1). The 0.05 degree monthly NDVI (normalized difference vegetation index) was offered from the Moderate Resolution Imaging Spectroradiometer (MODIS) data products MOD13C2 (http://ladsweb.nascom.nasa.gov/data/search.html, accessed on 4 March 2020). The 0.05 degree monthly NDII (normalized difference impervious index) was calculated by using a red band 1 from MOD13C2 and thermal infrared band 32 from MOD11C3. Both MOD13C2 and MOD11C3 image datasets were converted to Albers equal area conical projection and WGS-84 datum using the ArcGIS V9.3 software (ESRI, San Diego, CA, USA). To reduce the image noise from the atmospheric clouds, particles, shadows, etc., three-point smoothing was used to improve data quality.


**Table 1.** The reclassification of land use type according to the IGBP classification system.

We obtained the monthly meteorological data from the gridded datasets of Climatic Research Unit (CRU) TS 3.22 (http://crudata.uea.ac.uk/cru/data, accessed on 6 January 2019). These gridded datasets cover the global land surface (excluding Antarctica) at a 0.5◦ resolution and provide the best estimates for month-by-month variations in climate variables [53]. No measurement value is missing in the datasets. The scPDSI datasets were provided by the CRU. The scPDSI uses −0.99–0.99 as normal, and negative values indicate drought. Classification relevant to this research mainly includes extreme moist, heavy moist, moderate moist, slightly moist, slightly normal drought, moderate drought, heavy drought, and extreme drought (Table 2). In order to facilitate spatial statistics, meteorological data are resampled by ArcGIS 10.2 software with a resolution of 0.05 degree.

**Table 2.** Classification relevant of self-calibrating Palmer drought severity index (scPDSI).


*4.3. Methods*

4.3.1. Estimation of F<sup>v</sup>

*F*<sup>v</sup> is an index directly used to determine grassland health condition. We estimated the grassland coverage by using the *NDVI* data due to the significant linear correlation relationship between grassland coverage and *NDVI*. The calculated model is pixel dichotomy model. The specific calculation formula is as follows:

$$F\_{\rm V} = \frac{NDVI - NDVI\_{\rm min}}{NDVI\_{\rm max} - NDVI\_{\rm min}} \times 100\% \tag{1}$$

where *F*<sup>v</sup> is the grassland coverage (%), *NDVI* is the *NDVI* value of a single pixel, *NDVI*min is the NDVI value of bare soil or areas without vegetation coverage, and *NDVI*max is the NDVI value of pixels completely covered by vegetation. Theoretically, the *NDVI*min value should be close to 0, and the *NDVI*max value represents the maximum value of *NDVI* per

unit pixel of total vegetation coverage. However, considering the influence of vegetation type, noise, terrain, image quality, and other factors, the *NDVI*min and *NDVI*max values will deviate from the actual values, which are generally represented by the maximum and minimum values within a certain confidence range. In this paper, *NDVI* values near 2% and 98% of the cumulative percentage of *NDVI* values in remote sensing images in the study area are selected as *NDVI*min and *NDVI*max values.
