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Journal of Arid Land  2022, Vol. 14 Issue (9): 941-961    DOI: 10.1007/s40333-022-0032-x
Research article     
Attribution analysis and multi-scenario prediction of NDVI drivers in the Xilin Gol grassland, China
XU Mengran, ZHANG Jing(), LI Zhenghai, MO Yu
College of Enoironment and Bioresources, Dalian Minzu University, Dalian 116600, China
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Grassland degradation is influenced by climate change and human activities, and has become a major obstacle for the development of arid and semi-arid areas, posing a series of environmental and socio-economic problems. An in-depth understanding of the inner relations among grassland vegetation dynamics, climate change, and human activities is therefore greatly significant for understanding the variation in regional environmental conditions and predicting future developmental trends. Based on MODIS (moderate resolution imaging spectroradiometer) NDVI (normalized difference vegetation index) data from 2000 to 2020, our objective is to investigate the spatiotemporal changes of NDVI in the Xilin Gol grassland, Inner Mongolia Autonomous Region, China. Combined with 12 natural factors and human activity factors in the same period, the dominant driving factors and their interactions were identified by using the geographic detector model, and multiple scenarios were also simulated to forecast the possible paths of future NDVI changes in this area. The results showed that: (1) in the past 21 a, vegetation cover in the Xilin Gol grassland exhibited an overall increasing trend, and the vegetation restoration (84.53%) area surpassed vegetation degradation area (7.43%); (2) precipitation, wind velocity, and livestock number were the dominant factors affecting NDVI (the explanatory power of these factors exceeded 0.4). The interaction between average annual wind velocity and average annual precipitation, and between average annual precipitation and livestock number greatly affected NDVI changes (the explanatory power of these factors exceeded 0.7). Moreover, the impact of climate change on NDVI was more significant than human activities; and (3) scenario analysis indicated that NDVI in the Xinlin Gol grassland increased under the scenarios of reduced wind velocity, increased precipitation, and ecological protection. In contrast, vegetation coverage restoration in this area was significantly reduced under the scenarios of unfavorable climate conditions and excessive human activities. This study provides a scientific basis for future vegetation restoration and management, ecological environmental construction, and sustainable natural resource utilization in this area.

Key wordsnormalized difference vegetation index (NDVI)      grassland degradation      geographical detector      Cellular Automat (CA)-Markov model      Xilin Gol grassland     
Received: 04 June 2022      Published: 30 September 2022
Corresponding Authors: ZHANG Jing     E-mail:
Cite this article:

XU Mengran, ZHANG Jing, LI Zhenghai, MO Yu. Attribution analysis and multi-scenario prediction of NDVI drivers in the Xilin Gol grassland, China. Journal of Arid Land, 2022, 14(9): 941-961.

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Fig. 1 Mean NDVI (normalized difference vegetation index) in the Xilin Gol grassland during 2000-2020
Data type Data content Code Sequence length/year Source Processing method
Remote sensing MODIS NDVI NDVI 2000-2020 HANTS (harmonic analysis of time series) for NDVI filtering in environment for visualizing images (ENVI) v.5.3
Climate Temperature (℃) TEM 2000-2020 The annual average temperature was queried by (structured query language) SQL, and the Kriging interpolation was performed in ArcGIS v.10.3
Precipitation (mm) PRE 2000-2020 The average annual precipitation was queried by SQL, and the Kriging interpolation was performed in ArcGIS v.10.3
Wind velocity (m/s) WIND 2000-2020 The annual average wind velocity was queried by SQL, and the Kriging interpolation was performed in ArcGIS v.10.3
Topography Elevation (m) ELEV 2020 Mosaic, masking, projection, and resampling in ArcGIS v.10.3
Aspect ASP 2020 - Extracted by aspect tool in ArcGIS v.10.3
Slope (°) SLP 2020 - Deriving slope from an elevation surface in ArcGIS v.10.3
Soil data Soil type SOL 2000 Masking, projection, and resampling in ArcGIS v.10.3
Vegetation Vegetation type VEG 1987 Map of vegetation types
in Inner Mongolia Autonomous Region
Masking, projection, and resampling in ArcGIS v.10.3
Human activity Land use type LAND 2020 Masking, projection, and resampling in ArcGIS v.10.3
Population density (person/km2) POP 2000-2019 Statistical Yearbook of Xilin Gol League Joining a layer to another attribute table based on a common field in ArcGIS v.10.3
GDP per capita (CNY/person) GDP 2000-2019 Statistical Yearbook of Xilin Gol League Joining a layer to another attribute table based on a common field in ArcGIS v.10.3
Livestock number (head) LIV 2000-2019 Statistical Yearbook of Xilin Gol League Joining a layer to another attribute table based on a common field in ArcGIS v.10.3
Table 1 Research data used in this study
Fig. 2 Research framework in this study
Description Interaction Description Interaction
q(X1∩X2)<min(q(X1), q(X2)) Weaken, nonlinear q(X1∩X2)=q(X1)+q(X2) Independent
min(q(X1), q(X2)<q(X1∩X2)<max(q(X1), q(X2)) Weaken, univariate q(X1∩X2)>q(X1)+q(X2) Enhance, nonlinear
q(X1∩X2)>max(q(X1), q(X2)) Enhance, bivariate
Table 2 Interaction relationship of each factor
Fig. 3 Map of driving factors of NDVI change in the Xilin Gol grassland. (a), annual mean temperature; (b), mean annual precipitation; (c), mean wind velocity; (d), elevation; (e), aspect; (f), slope; (g), soil type; (h), vegetation type; (i), land use type; (j), population density; (k), GDP per capita; (l), livestock number.
Scenario Name Code Parameter and planning policy
Business as usual Business as usual BAU Using the seven driving factors screened in Figure 6, keeping the model parameter settings unchanged, providing no processing for the driving factors, and following the inertial development law of NDVI.
Scenario of climate change Increased wind velocity WIN+ Increasing the overall wind velocity value by 25%, while keeping other factors constant.
Reduced wind velocity WIN- Reducing the overall wind velocity value by 25%, while keeping other factors constant.
Increased precipitation PRE+ Increasing the overall precipitation value by 25%, while keeping other factors constant.
Reduced precipitation PRE- Reducing the overall precipitation value by 25%, while keeping other factors constant.
Scenario of economic priority Increased livestock number PD+ Increasing the value of livestock by 50%, while keeping other factors constant.
Scenario of ecological protection Reduced livestock number PD- Reducing the value of livestock by 50%, while keeping other factors constant.
Table 3 Parameter settings in different scenarios
Fig. 4 Inter-annual NDVI variation of the Xilin Gol grassland during 2000-2020
SNDVI Z value Trend of NDVI Area percentage (%)
S< -0.0005 Z≤ -1.64 Significant decrease 0.67
S< -0.0005 -1.64<Z<1.64 Slight decrease 6.76
-0.0005-0.0005 -1.64<Z<1.64 Stability 8.04
S≥0.0005 -1.64<Z<1.64 Slight increase 58.11
S≥0.0005 Z≥1.64 Significant increase 26.42
Table 4 Statistics of NDVI trend
Fig. 5 Trend of NDVI of the Xilin Gol grassland during 2000-2020
Fig. 6 Single factor affecting vegetation change in the Xilin Gol grassland during 2000-2020 and its q value. TEM, temperature; PRE, precipitation; WIND, wind velocity; ELEV, elevation; ASP, aspect; SLP, slope; SOL, soil type; VEG, vegetation type; LAND, land use type; POP, population density; GDP, GDP per captital; LIV, livestock number. The abbreviations are the same in Figure 7.
Fig. 7 Interactions among driving factors of NDVI
Fig. 8 Risk detection results of driving factors of mean NDVI. (a), temperature; (b), precipitation; (c), wind velocity; (d), elevation; (e), aspect; (f), slope; (g), soil type; (h), vegetation type; (i), land use type; (j), population density; (k), GDP per capita; (l), livestock number.
Factor Indicator Suitable range/type Mean NDVI
TEM Temperature (℃) 2.74-3.33 0.2262
PRE Precipitation (mm) 303.89-393.36 0.2540
WIND Wind velocity (m/s) 2.48-2.84 0.2487
ELEV Elevation (m) 1256-1695 0.2284
ASP Aspect North 0.1994
SLP Slope (°) 8.22-16.18 0.3093
SOL Soil type Gray forest soil 0.3253
VEG Vegetation type Broad-leaved forest 0.3321
LAND Land use type Forest land 0.3127
POP Population density (person/km2) 3.6-13.9 0.2262
GDP GDP per capita (CNY/person) 47,431-74,442 0.2288
LIV Livestock number (head) 1,486,331-2,570,527 0.2309
Table 5 Suitable range/type of each driving factor (95% confidence level)
Fig. 9 Real spatial distribution of NDVI in 2010 (a), 2015 (b), 2020 (c) and its simulated result in 2020 (d)
Fig. 10 Simulated spatial distribution of NDVI in the Xilin Gol grassland under different scenarios in 2030. (a), BAU; (b), WIN+; (c), WIN-; (d), PRE+; (e), PRE-; (f), PD+; (g), PD-. The detailed scenarios are presented in Table 3.
Fig. 11 Area change of NDVI at each level under different scenarios (WIN+, WIN-, PRE+, PRE-, PD+, and PD-) in 2030. The detailed scenarios are presented in Table 3.
Category Factor Method q value Category Factor Method q value
Climate Temperature Natural break 0.3333 Topography Elevation Natural break 0.0981
Quantile 0.3301 Quantile 0.111
Geometrical interval 0.2884 Geometrical interval 0.0781
Precipitation Natural break 0.5995 Slope Natural break 0.1545
Quantile 0.6262 Quantile 0.1213
Geometrical interval 0.5770 Geometrical interval 0.1232
Drought index Natural break 0.6351 Anthropogenic activities Population density Natural break 0.2305
Quantile 0.6447 Quantile 0.3188
Geometrical interval 0.5899 Geometrical interval 0.3177
Wind speed Natural break 0.6283 GDP
per capita
Natural break 0.1256
Quantile 0.6313 Quantile 0.3242
Geometrical interval 0.6162 Geometrical interval 0.3060
Soil pH value Natural break 0.1583 Livestock Natural break 0.4491
Quantile 0.1389 Quantile 0.2921
Geometrical interval 0.1595 Geometrical interval 0.3121
Table S1 Zoning effect of geographical detector
Fig. S1 Scale effect of geographic detector result (q value and 90% quantile of q value). x1-x14 are the detailed driving factors.
C=q(X1∩X2) A=q(X1) B=q(X2) Conclusion Interpretation
x1∩x2=0.7192 0.3327 0.6190 C<A+B;C>A,B *
x1∩x3=0.7094 0.3327 0.6379 C<A+B;C>A,B *
x1∩x4=0.6955 0.3327 0.6236 C<A+B;C>A,B *
x1∩x5=0.4284 0.3327 0.0987 C<A+B;C>A,B *
x1∩x6=0.3469 0.3327 0.0051 C>A+B;C>A,B
x1∩x7=0.4776 0.3327 0.1549 C<A+B;C>A,B *
x1∩x8=0.5781 0.3327 0.4635 C<A+B;C>A,B *
x1∩x9=0.4402 0.3327 0.1653 C<A+B;C>A,B *
x1∩x10=0.5063 0.3327 0.1830 C<A+B;C>A,B *
x1∩x11=0.4832 0.3327 0.1428 C<A+B;C>A,B *
x1∩x12=0.6069 0.3327 0.3220 C<A+B;C>A,B *
x1∩x13=0.6562 0.3327 0.3217 C<A+B;C>A,B *
x1∩x14=0.6323 0.3327 0.4490 C<A+B;C>A,B *
x2∩x3=0.6586 0.6190 0.6379 C<A+B;C>A,B *
x2∩x4=0.7456 0.6190 0.6236 C<A+B;C>A,B *
x2∩x5=0.6651 0.6190 0.0987 C<A+B;C>A,B *
x2∩x6=0.6285 0.6190 0.0051 C>A+B;C>A,B
x2∩x7=0.6529 0.6190 0.1549 C<A+B;C>A,B *
x2∩x8=0.7063 0.6190 0.4635 C<A+B;C>A,B *
x2∩x9=0.6863 0.6190 0.1653 C<A+B;C>A,B *
x2∩x10=0.6597 0.6190 0.1830 C<A+B;C>A,B *
x2∩x11=0.6699 0.6190 0.1428 C<A+B;C>A,B *
x2∩x12=0.6754 0.6190 0.3220 C<A+B;C>A,B *
x2∩x13=0.7133 0.6190 0.3217 C<A+B;C>A,B *
x2∩x14=0.7182 0.6190 0.4490 C<A+B;C>A,B *
x3∩x4=0.7179 0.6379 0.6236 C<A+B;C>A,B *
x3∩x5=0.6690 0.6379 0.0987 C<A+B;C>A,B *
x3∩x6=0.6488 0.6379 0.0051 C>A+B;C>A,B *
x3∩x7=0.6856 0.6379 0.1549 C<A+B;C>A,B *
x3∩x8=0.7272 0.6379 0.4635 C<A+B;C>A,B *
x3∩x9=0.6919 0.6379 0.1653 C<A+B;C>A,B *
x3∩x10=0.6936 0.6379 0.1830 C<A+B;C>A,B *
x3∩x11=0.7019 0.6379 0.1428 C<A+B;C>A,B *
x3∩x12=0.6754 0.6379 0.3220 C<A+B;C>A,B *
x3∩x13=0.6878 0.6379 0.3217 C<A+B;C>A,B *
x3∩x14=0.6909 0.6379 0.4490 C<A+B;C>A,B *
x4∩x5=0.7112 0.6236 0.0987 C<A+B;C>A,B *
x4∩x6=0.6343 0.6236 0.0051 C>A+B;C>A,B
x4∩x7=0.6696 0.6236 0.1549 C<A+B;C>A,B *
x4∩x8=0.7144 0.6236 0.4635 C<A+B;C>A,B *
x4∩x9=0.6553 0.6236 0.1653 C<A+B;C>A,B *
x4∩x10=0.6775 0.6236 0.1830 C<A+B;C>A,B *
x4∩x11=0.6844 0.6236 0.1428 C<A+B;C>A,B *
x4∩x12=0.6893 0.6236 0.3220 C<A+B;C>A,B *
x4∩x13=0.6800 0.6236 0.3217 C<A+B;C>A,B *
x4∩x14=0.6414 0.6236 0.4490 C<A+B;C>A,B *
x5∩x6=0.1131 0.0987 0.0051 C>A+B;C>A,B
x5∩x7=0.2682 0.0987 0.1549 C<A+B;C>A,B *
x5∩x8=0.5320 0.0987 0.4635 C<A+B;C>A,B *
x5∩x9=0.2834 0.0987 0.1653 C<A+B;C>A,B *
x5∩x10=0.2655 0.0987 0.1830 C<A+B;C>A,B *
x5∩x11=0.2289 0.0987 0.1428 C<A+B;C>A,B *
x5∩x12=0.3949 0.0987 0.3220 C<A+B;C>A,B *
x5∩x13=0.5603 0.0987 0.3217 C<A+B;C>A,B *
x5∩x14=0.5644 0.0987 0.4490 C<A+B;C>A,B *
x6∩x7=0.1691 0.0051 0.1549 C>A+B;C>A,B
x6∩x8=0.4859 0.0051 0.4635 C>A+B;C>A,B
x6∩x9=0.1916 0.0051 0.1653 C>A+B;C>A,B
x6∩x10=0.2029 0.0051 0.1830 C>A+B;C>A,B
x6∩x11=0.1590 0.0051 0.1428 C>A+B;C>A,B
x6∩x12=0.3342 0.0051 0.3220 C>A+B;C>A,B
x6∩x13=0.3348 0.0051 0.3217 C>A+B;C>A,B
x6∩x14=0.4654 0.0051 0.4490 C>A+B;C>A,B
x7∩x8=0.5047 0.1549 0.4635 C<A+B;C>A,B *
x7∩x9=0.2882 0.1549 0.1653 C<A+B;C>A,B *
x7∩x10=0.2823 0.1549 0.1830 C<A+B;C>A,B *
x7∩x11=0.2424 0.1549 0.1428 C<A+B;C>A,B *
x7∩x12=0.4222 0.1549 0.3220 C<A+B;C>A,B *
x7∩x13=0.4106 0.1549 0.3217 C<A+B;C>A,B *
x7∩x14=0.5165 0.1549 0.4490 C<A+B;C>A,B *
x8∩x9=0.5402 0.4635 0.1653 C<A+B;C>A,B *
x8∩x10=0.5350 0.4635 0.1830 C<A+B;C>A,B *
x8∩x11=0.5375 0.4635 0.1428 C<A+B;C>A,B *
x8∩x12=0.6301 0.4635 0.3220 C<A+B;C>A,B *
x8∩x13=0.5966 0.4635 0.3217 C<A+B;C>A,B *
x8∩x14=0.6628 0.4635 0.4490 C<A+B;C>A,B *
x9∩x10=0.3169 0.1653 0.1830 C<A+B;C>A,B *
x9∩x11=0.2952 0.1653 0.1428 C<A+B;C>A,B *
x9∩x12=0.4319 0.1653 0.3220 C<A+B;C>A,B *
x9∩x13=0.4137 0.1653 0.3217 C<A+B;C>A,B *
x9∩x14=0.5422 0.1653 0.4490 C<A+B;C>A,B *
x10∩x11=0.2631 0.1830 0.1428 C<A+B;C>A,B *
x10∩x12=0.4232 0.1830 0.3220 C<A+B;C>A,B *
x10∩x13=0.4587 0.1830 0.3217 C<A+B;C>A,B *
x10∩x14=0.5300 0.1830 0.4490 C<A+B;C>A,B *
x11∩x12=0.4188 0.1428 0.3220 C<A+B;C>A,B *
x11∩x13=0.4380 0.1428 0.3217 C<A+B;C>A,B *
x11∩x14=0.5287 0.1428 0.4490 C<A+B;C>A,B *
x12∩x13=0.6098 0.3220 0.3217 C<A+B;C>A,B *
x12∩x14=0.6103 0.3220 0.4490 C<A+B;C>A,B *
x13∩x14=0.5671 0.3217 0.4490 C<A+B;C>A,B *
Table S2 Interaction of driver factor
NDVI type Very low Low Medium High Very high
Very low 48,087 25,769 4551 310 31
Low 1412 12,212 13,617 3705 326
Medium 113 2987 15,283 12,800 2621
High 22 342 5196 14,269 8505
Very high 21 33 576 5160 23,856
Table S3 Conversion matrix of NDVI
NDVI type Very low Low Medium High Very high
Very low 0.610647 0.327237 0.057788 0.003937 0.000391
Low 0.045158 0.390517 0.435443 0.118467 0.010415
Medium 0.003339 0.088354 0.452113 0.378662 0.077532
High 0.000768 0.012079 0.183370 0.503620 0.300163
Very high 0.000706 0.001105 0.019444 0.174058 0.804688
Table S4 Conversion probability of NDVI
[1]   Aguejdad R. 2021. The influence of the calibration interval on simulating non-stationary urban growth dynamic using CA-Markov Model. Remote Sensing, 13(3): 468-488.
doi: 10.3390/rs13030468
[2]   Alfredo H. 2016. Ecology: Vegetation's responses to climate variability. Nature, 531(7593):181-182.
doi: 10.1038/nature17301
[3]   Ali B A, Oumayma B, Riadh F I, et al. 2018. Comparative study of three satellite image time-series decomposition methods for vegetation change detection. European Journal of Remote Sensing, 51(1): 607-615.
doi: 10.1080/22797254.2018.1465360
[4]   Batunacun, Claas N, Hu Y F, et al. 2018. Land-use change and land degradation on the Mongolian Plateau from 1975 to 2015-A case study from Xilingol, China. Land Degradation and Development, 29(6): 1595-1606.
doi: 10.1002/ldr.2948
[5]   Burrell A L, Evans J P, Liu Y. 2017. Detecting dryland degradation using time series segmentation and residual trend analysis (TSS-RESTREND). Remote Sensing of Environment, 197: 43-57.
doi: 10.1016/j.rse.2017.05.018
[6]   Chen T, Xia J, Zou L, et al. 2020. Quantifying the influences of natural factors and human activities on NDVI changes in the Hanjiang River Basin, China. Remote Sensing, 12(22): 3780, doi: 10.3390/rs12223780.
doi: 10.3390/rs12223780
[7]   Culik II K, Hurd L P, Yu S. 1990. Computation theoretic aspects of cellular automata. Physica D: Nonlinear Phenomena, 45(1-3): 357-378.
doi: 10.1016/0167-2789(90)90194-T
[8]   Dai G S, Ulgiati S, Zhang Y S, et al. 2014. The false promises of coal exploitation: How mining affects herdsmen well-being in the grassland ecosystems of Inner Mongolia. Energy Policy, 67: 146-153.
doi: 10.1016/j.enpol.2013.12.033
[9]   Dong S K, Gao H W, Xu G C, et al. 2007. Farmer and professional attitudes to the large-scale ban on livestock grazing of grasslands in China. Environmental Conservation, 34(3): 246-254.
[10]   Du J Q, Quan Z J, Fang S F, et al. 2020. Spatiotemporal changes in vegetation coverage and its causes in China since the Chinese economic reform. Environmental Science and Pollution Research, 27(1): 1144-1159.
doi: 10.1007/s11356-019-06609-6
[11]   Fu X, Wang X, Yang Y J. 2018. Deriving suitability factors for CA-Markov land use simulation model based on local historical data. Journal of Environmental Management, 206: 10-19.
doi: 10.1016/j.jenvman.2017.10.012
[12]   Gardiner B, Berry P, Moulia B. 2016. Wind impacts on plant growth, mechanics and damage. Plant Sciences, 245: 94-118.
doi: 10.1016/j.plantsci.2016.01.006
[13]   Gong Z N, Zhao S Y, Gu J Z. 2017. Correlation analysis between vegetation coverage and climate drought conditions in North China during 2001-2013. Journal of Geographical Sciences, 27(2): 143-160.
doi: 10.1007/s11442-017-1369-5
[14]   Gu Z J, Duan X W, Shi Y D, et al. 2018. Spatiotemporal variation in vegetation coverage and its response to climatic factors in the Red River Basin, China. Ecological Indicators, 93: 54-64.
doi: 10.1016/j.ecolind.2018.04.033
[15]   Hao L, Sun G, Liu Y Q, et al. 2014. Effects of precipitation on grassland ecosystem restoration under grazing exclusion in Inner Mongolia, China. Landscape Ecology, 29: 1657-1673.
doi: 10.1007/s10980-014-0092-1
[16]   Hao R, Yu D, Wu J. 2017. Relationship between paired ecosystem services in the grassland and agro-pastoral transitional zone of China using the constraint line method. Agriculture Ecosystems and Environment, 240: 171-181.
doi: 10.1016/j.agee.2017.02.015
[17]   He B, Liu J, Guo L, et al. 2018. Recovery of ecosystem carbon and energy FluXes from the 2003 drought in Europe and the 2012 drought in the United States. Geophysical Research Letters, 45(10): 4879-4888.
doi: 10.1029/2018GL077518
[18]   He C Y, Tian J, Gao B, et al. 2015. Differentiating climate- and human-induced drivers of grassland degradation in the Liao River Basin, China. Environmental Monitoring and Assessment, 187: 4199, doi: 10.1007/s10661-014-4199-2.
doi: 10.1007/s10661-014-4199-2
[19]   Hu Y F, Alatengtuya, Yan Y. 2013. Comprehensive Monitoring and Assessment of Xilin Gol Ecosystem in Inner Mongolia. Beijing: China Environment Publishing House, 1-331. (in Chinese)
[20]   IPCC(Intergovernmental Panel on Climate Change). 2013. Climate Change 2013:The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva: IPCC.
[21]   Jiang W G, Yuan L H, Wang W J, et al. 2015. Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecological Indicators, 51: 117-126.
doi: 10.1016/j.ecolind.2014.07.031
[22]   Kendall M G. 1990. Rank correlation methods. British Journal of Psychology, 25(1): 86-91.
[23]   Li A, Wu J, Huang J. 2012. Distinguishing between human-induced and climate-driven vegetation changes: a critical application of RESTREND in Inner Mongolia. Landscape Ecology, 27(7): 969-982.
doi: 10.1007/s10980-012-9751-2
[24]   Li J Q, Li Z L, Dong S P, et al. 2021. Spatial and temporal changes in vegetation and desertification (1982-2018) and their responses to climate change in the Ulan Buh Desert, Northwest China. Theoretical and Applied Climatology, 143: 1643-1654.
doi: 10.1007/s00704-021-03522-2
[25]   Liu C, Melack J, Tian Y, et al. 2019. Detecting land degradation in eastern China grasslands with time series segmentation and residual trend analysis (TSS-RESTREND) and GIMMS NDVI3g data. Remote Sensing, 11(9): 1014-1032.
doi: 10.3390/rs11091014
[26]   Liu C L, Li W L, Zhu G F, et al. 2020. Land use/land cover changes and their driving factors in the northeastern Tibetan Plateau based on geographical detectors and Google Earth engine: A case study in Gannan Prefecture. Remote Sensing, 12(19): 3139-3157.
doi: 10.3390/rs12193139
[27]   Mann H B. 1945. Nonparametric test against trend. Econometrica, 13(3): 245-259.
doi: 10.2307/1907187
[28]   Meng L Q, Gao S, Li Y S, et al. 2020. Spatial and temporal characteristics of vegetation NDVI changes and the driving forces in Mongolia during 1982-2015. Remote Sensing, 12(4): 603-628.
doi: 10.3390/rs12040603
[29]   Munkhnasan L, Woo-Kyun L, Woo J S, et al. 2018. Long-term trend and correlation between vegetation green-ness and climate variables in Asia based on satellite data. Science of the Total Environment, 618(15): 1089-1095.
doi: 10.1016/j.scitotenv.2017.09.145
[30]   Naeem S, Zhang Y, Zhang X, et al. 2021. Both climate and socioeconomic drivers contribute to vegetation greening of the Loess Plateau. Science Bulletin, 66(12): 1160-1163.
doi: 10.1016/j.scib.2021.03.007
[31]   Otgonbayar M, Atzberger C, Chambers J, et al. 2017. Land suitability evaluation for agricultural cropland in Mongolia using the spatial MCDM method and AHP based GIS. Journal of Geoscience and Environment Protection, 5: 238-263.
doi: 10.4236/gep.2017.59017
[32]   Pan N Q, Feng X M, Fu B J, et al. 2018. Increasing global vegetation browning hidden in overall vegetation greening: insights from time-varying trends. Remote Sensing of Environment, 214: 59-72.
doi: 10.1016/j.rse.2018.05.018
[33]   Pan T, Zou X, Liu Y, et al. 2017. Contributions of climatic and non-climatic drivers to grassland variations on the Tibetan Plateau. Ecological Engineering, 108: 307-317.
doi: 10.1016/j.ecoleng.2017.07.039
[34]   Piao S L, Mohammat A, Fang J Y, et al. 2006. NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China. Global Environmental Change, 16(4): 340-348.
doi: 10.1016/j.gloenvcha.2006.02.002
[35]   Qian T, Bagan H, Kinoshita T, et al. 2014. Spatial-temporal analyses of surface coal mining dominated land degradation in Holingol, Inner Mongolia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(5): 1675-1687.
doi: 10.1109/JSTARS.2014.2301152
[36]   Qiao J M, Yu D Y, Wu G J. 2018. How do climatic and management factors affect agricultural ecosystem services? A case study in the agro-pastoral transitional zone of northern China. Science of the Total Environment, 613-614: 314-323.
doi: 10.1016/j.scitotenv.2017.08.264
[37]   Rhif M, Abbes A B, Martinez B, et al. 2020. An improved trend vegetation analysis for non-stationary NDVI time series based on wavelet transform. Environmental Science and Pollution Research, 28(34): 46603-46613.
doi: 10.1007/s11356-020-10867-0
[38]   Shi N N, Xiao N W, Wang Q, et al. 2019. Temporal and spatial variation of NDVI and its driving forces in Xilingol. Chinese Journal of Plant Ecology, 43(4): 331-341. (in Chinese)
doi: 10.17521/cjpe.2018.0254
[39]   Subramanian B, Zhou W J, Ji H L, et al. 2020. Environmental and management controls of soil carbon storage in grasslands of southwestern China. Journal of Environmental Management, 254(2): 109810, doi: 10.1016/j.jenvman.2019.109810.
doi: 10.1016/j.jenvman.2019.109810
[40]   Sun B, Li Z Y, Gao Z H, et al. 2017. Grassland degradation and restoration monitoring and driving forces analysis based on long time-series remote sensing data in Xilin Gol League. Acta Ecologica Sinica, 37(4): 219-228. (in Chinese)
doi: 10.1016/j.chnaes.2017.02.009
[41]   Sun R, Chen S H, Su H B. 2021. Climate dynamics of the spatiotemporal changes of vegetation NDVI in Northern China from 1982 to 2015. Remote Sensing, 13(2): 187-207.
doi: 10.3390/rs13020187
[42]   Tao S, Guang T T, Peng W F, et al. 2020. Spatial-temporal variation and driving forces of NDVI in the upper Reaches of the Yangtze River from 2000 to 2015: A case study of Yibin Sinica, Ecologica City. Acta Ecologica Sinica, 40(14): 5029-5043. (in Chinese)
[43]   Wang J F, Li X H, Christakos G, et al. 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. International Journal of Geographical Information Science, 24(1): 107-127.
doi: 10.1080/13658810802443457
[44]   Wang J F, Wu T L. 2019. Analysis on runoff variation characteristics and its attribution in the upper reaches of Zhanghe river basin. Arid Land Resources and Management, 33: 165-171. (in Chinese)
[45]   Wang J S, Xu C D. 2017. Geodetector: Principle and prospective. Acta Geographica Sinica, 72(1): 116-134. (in Chinese)
[46]   Wang L X, Yu D Y, Liu Z, et al. 2018. Study on NDVI changes in Weihe Watershed based on CA-Markov model. Geological Journal, 53(S2): 435-441.
doi: 10.1002/gj.3259
[47]   Wang M, Yang W B, Wu N, et al. 2019. Patterns and drivers of soil carbon stock in southern China’s grasslands. Agricultural and Forest Meteorology, 276-277: 107634, doi: 10.1016/j.agrformet.2019.107634.
doi: 10.1016/j.agrformet.2019.107634
[48]   Wang X, Yi S, Wu Q, et al. 2016. The role of permafrost and soil water in distribution of alpine grassland and its NDVI dynamics on the Qinghai-Tibetan Plateau. Global and Planetary Change, 147: 40-53.
doi: 10.1016/j.gloplacha.2016.10.014
[49]   Wang X M, Lang L L, Yan P, et al. 2016. Aeolian processes and their effect on sandy desertification of the Qinghai-Tibet Plateau: A wind tunnel experiment. Soil and Tillage Research, 158: 67-75.
doi: 10.1016/j.still.2015.12.004
[50]   Wu D G, Zhao X, Liang S L, et al. 2015. Time-lag effects of global vegetation responses to climate change. Global Change Biology, 21(9): 3520-3531.
doi: 10.1111/gcb.12945
[51]   Wu N T, Liu G X, Liu A J, et al. 2020. Monitoring and driving force analysis of net primary productivity in native grassland: A case study in Xilingol steppe, China. The Journal of Applied Ecology, 31(4): 1233-1240. (in Chinese)
[52]   Wu S, Gao X, Lei J, et al. 2020. Spatial and temporal changes in the normalized difference vegetation index and their driving factors in the desert/grassland biome transition zone of the Sahel Region of Africa. Remote Sensing, 12(24): 4119-4146.
doi: 10.3390/rs12244119
[53]   Xie L F, Wu W C, Huang X L, et al. 2020. Mining and restoration monitoring of rare earth element (REE) exploitation by new remote sensing indicators in southern JiangXi, China. Remote Sensing, 12(21): 3558-3577.
doi: 10.3390/rs12213558
[54]   Xie Y C, Sha Z Y. 2012. Quantitative analysis of driving factors of grassland degradation: A case study in Xilin River Basin, Inner Mongolia. The Scientific World Journal, 2012: 169724, doi: 10.1100/2012/169724.
doi: 10.1100/2012/169724
[55]   Xin L, Li X B, Dou H S, et al. 2020. Evaluation of grassland carbon pool based on TECO-R model and climate-driving function: A case study in the Xilingol typical steppe region of Inner Mongolia, China. Ecological Indicators, 117: 106508, doi: 10.1016/j.ecolind.2020.106508.
doi: 10.1016/j.ecolind.2020.106508
[56]   Yan J, Zhang G, Ling H,et al. 2022. Comparison of time-integrated NDVI and annual maximum NDVI for assessing grassland dynamics. Ecological Indicators, 136: 108611, doi: 10.1016/j.ecolind.2022.108611.
doi: 10.1016/j.ecolind.2022.108611
[57]   Yang Y, Niu J M, Zhang Q, et al. 2011. Ecological footprint analysis of a semi-arid grassland region facilitates assessment of its ecological carrying capacity: a case study of Xilinguole League. Acta Ecologica Sinica, 31(17): 5096-5104. (in Chinese)
[58]   Yang Y J, Wang S J, Bai X Y. 2019. Factors affecting long-term trends in global NDVI. Forests, 10(5): 372-389.
doi: 10.3390/f10050372
[59]   Yu L F, Chen Y, Sun W J, et al. 2019. Effects of grazing exclusion on soil carbon dynamics in alpine grasslands of the Tibetan Plateau. Geoderma, 353: 133-143.
doi: 10.1016/j.geoderma.2019.06.036
[60]   Zhang L Y, Liu A J, Xing Q, et al. 2006. Trend and analysis of vegetation variation of typical rangeland in Inner Mongolia-a case study of typical rangeland of Xilinguole. Journal of Arid Land Resources and Environment, 20(2): 185-190. (in Chinese)
[61]   Zhao Y, He C, Zhang Q. 2012. Monitoring vegetation dynamics by coupling linear trend analysis with change vector analysis: A case study in the Xilingol steppe in northern China. International Journal of Remote Sensing, 33(1): 287-308.
doi: 10.1080/01431161.2011.594102
[62]   Zheng K Y, Tan L S, Sun Y W, et al. 2021. Impacts of climate change and anthropogenic activities on vegetation change: Evidence from typical areas in China. Ecological Indicators, 126: 107648, doi: 10.1016/j.ecolind.2021.107648.
doi: 10.1016/j.ecolind.2021.107648
[63]   Zou X Y, Li J F, Cheng H, et al. 2018. Spatial variation of topsoil features in soil wind erosion areas of northern China. CATENA, 167: 429-439.
doi: 10.1016/j.catena.2018.05.022
[1] ZHOU Siyuan, DUAN Yufeng, ZHANG Yuxiu, GUO Jinjin. Vegetation dynamics of coal mining city in an arid desert region of Northwest China from 2000 to 2019[J]. Journal of Arid Land, 2021, 13(5): 534-547.
[2] Ayad M F AL-QURAISHI, Heman A GAZNAYEE, Mattia CRESPI. Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index, Normalized Difference Water Index and Standardized Precipitation Index[J]. Journal of Arid Land, 2021, 13(4): 413-430.
[3] Yupeng LI, Yaning CHEN, Zhi LI. Effects of land use and cover change on surface wind speed in China[J]. Journal of Arid Land, 2019, 11(3): 345-356.
[4] Yunxiao BAI, Xiaobing LI, Wanyu WEN, Xue MI, Ruihua LI, Qi HUANG, Meng ZHANG. CO2, CH4 and N2O flux changes in degraded grassland soil of Inner Mongolia, China[J]. Journal of Arid Land, 2018, 10(3): 347-361.
[5] Qiang LI, DaoWei ZHOU, YingHua JIN, MinLing WANG, YanTao SONG, GuangDi LI. Effects of fencing on vegetation and soil restoration in a degraded alkaline grassland in northeast China[J]. Journal of Arid Land, 2014, 6(4): 478-487.
[6] Adrian R WILLIAMS. On sustaining the ecology and livestock industry of the Bayanbuluk Grasslands[J]. Journal of Arid Land, 2010, 2(1): 57-63.