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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (10): 1796-1812    DOI: 10.1016/j.jaridl.2026.10.007     CSTR: 32276.14.JAL.20250669
Research article     
Spatiotemporal variation of vegetation cover dynamics and driving factors in the vulnerable sandy lands, China
XU Xiaona1, ZHANG Huayong1,2,*(), WANG Zhongyu1
1 Research Center for Engineering Ecology and Nonlinear Science, North China Electric Power University, Beijing 102206, China
2 Theoretical Ecology and Engineering Ecology Research Group, School of Life Sciences, Shandong University, Qingdao 266237, China
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Abstract  

Sandy vegetation plays a critical role in maintaining ecological balance and combating desertification in arid regions. Accurately elucidating the vegetation dynamics and analyzing how it responds to the impacts of natural and human activity hold vital scientific significance for promoting the sustainable preservation and development of sandy lands. This research focuses on four major sandy lands (Hulun Buir, Mu Us, Horqin, and Otindag) in the typical fragile ecological zones of China, aiming to analyze the vegetation change and identify the contribution of driving factors to vegetation dynamics. Using normalized difference vegetation index (NDVI) data from the Google Earth Engine, we analyzed the spatiotemporal patterns of fractional vegetation cover (FVC) during the period 2000-2020. Furthermore, by integrating the optimal parameter-based geographical detector (OPGD) and structural equation modeling (SEM), we systematically assessed the relationships of natural factors and human activities with FVC changes. Compared with the period 2000-2010, FVC degradation in the Horqin and Mu Us sandy lands decreased by 5.74% and 3.49%, respectively, while FVC improvement increased by 6.12% and 1.24% during the period 2011-2020. Meanwhile, the Hulun Buir Sandy Land showed reductions in both degradation (14.92%) and improvement (0.08%), whereas the Otindag Sandy Land exhibited increased degradation (1.43%) and reduced improvement (0.41%). Climate was the dominant driver of FVC changes in the Hulun Buir and Otindag sandy lands, whereas human activity played a key role in the Horqin and Mu Us sandy lands. Specifically, temperature and precipitation were the key drivers in the Hulunbuir and Otindag sandy lands, while FVC dynamics in the Horqin and Mu Us sandy lands were mainly driven by land use intensity. These findings provide insights into vegetation dynamics and driving mechanisms in sandy land ecosystems, and offer practical implications for ecological management in vulnerable regions.

Key words： natural and human factors      optimal parameter-based geographical detector (OPGD)      structural equation modeling (SEM)      fractional vegetation cover (FVC)      sandy land     
Received: 31 December 2025      Published: 31 October 2026
Corresponding Authors: *ZHANG Huayong (E-mail: rceens@ncepu.edu.cn)
About author: First author contact:

Conceptualization: ZHANG Huayong; Methodology: XU Xiaona, ZHANG Huayong; Formal analysis: XU Xiaona; Writing - original draft preparation: XU Xiaona; Writing - review and editing: XU Xiaona, ZHANG Huayong, WANG Zhongyu; Funding acquisition: ZHANG Huayong; Resources: ZHANG Huayong; Supervision: ZHANG Huayong, WANG Zhongyu. All authors approved the manuscript.
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XU Xiaona
ZHANG Huayong
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Cite this article:

XU Xiaona, ZHANG Huayong, WANG Zhongyu. Spatiotemporal variation of vegetation cover dynamics and driving factors in the vulnerable sandy lands, China. Journal of Arid Land, 2026, 18(10): 1796-1812.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.10.007     OR     http://jal.xjegi.com/Y2026/V18/I10/1796

Sandy land Mean temperature (°C) Mean precipitation (mm) Altitude (m) Area (km2)
Hulun Buir -0.30 291.04 713.71 83,025.38
Horqin 6.51 391.89 473.75 125,694.79
Otindag 3.46 248.36 1190.31 150,756.97
Mu Us 3.70 354.55 1344.21 86,606.84
Table 1 Geographical characteristics of the four sandy lands
Fig. 1 Spatial distribution of land use types in the four sandy lands. (a), Hulun Buir Sandy Land; (b), Horqin Sandy Land; (c), Otindag Sandy Land; (d) Mu Us Sandy Land.
Data type Data source Derived variable Unit Resolution Website
Climate National Tibetan Plateau Data Centre Temperature (TEM) °C 1000 m https://www.tpdc.ac.cn/
Precipitation (PRE) mm 1000 m
Potential evapotranspiration (PET) mm 1000 m
Digital elevation model (DEM) Resources and Environment Science and Data Center Slope (SLP) ° 90 m http://www.resdc.cn/
Aspect (ASP) ° 90 m
Land use China Land Cover Datasets (CLCD) Land use intensity (LUI) - 30 m https://zenodo.org/records/8176941
Soil National Tibetan Plateau/Third Pole Environment Data Center pH - 1000 m https://www.tpdc.ac.cn/
Total phosphorus (TP) g/100 g 1000 m
Total nitrogen (TN) g/100 g 1000 m
Soil organic matter (SOM) g/100 g 1000 m
Total potassium (TK) g/100 g 1000 m
Soil moisture (SM) m3/m3 1000 m
Human activity Global Human Footprint Dataset Human footprint (HFP) - 1000 m https://github.com/HaoweiGis/humanFootprintMapping/
NDVI Google Earth Engine (GEE) FVC - 30 m https://lpdaac.usgs.gov/
Table 2 Description of the research datasets
FVC value FVC classification FVC value FVC classification
0.8≤FVC≤1.0 Very high coverage 0.2≤FVC<0.4 Low coverage
0.6≤FVC<0.8 High coverage 0.0≤FVC<0.2 Very low coverage
0.4≤FVC<0.6 Medium coverage
Table 3 Classification of fractional vegetation coverage (FVC) in this study
Fig. 2 Spatial distribution of fractional vegetation coverage (FVC) in the Hulun Buir (a), Horqin (b), Otindag (c), and Mu Us (d) sandy lands, and the percentage of FVC (e)
Fig. 3 Inter-annual variations in the percentage of FVC in the Hulun Buir (a), Horqin (b), Otindag (c), and Mu Us (d) sandy lands
Fig. 4 Spatial change trends of FVC in the Hulun Buir (a1-a3), Horqin (b1-b3), Otindag (c1-c3), and Mu Us (d1-d3) sandy lands during the periods 2000-2020, 2000-2010, and 2011-2020
Fig. 5 Factor detector in the Hulun Buir (a), Horqin (b), Otindag (c), and Mu Us (d) sandy lands. TEM, temperature; PRE, precipitation; PET, potential evapotranspiration; DEM, digital elevation model; SLP, slope; HFP, human footprint; LUI, land use intensity; SM, soil moisture; SOM, soil organic matter; TP, total phosphorus; TK, total potassium.
Fig. 6 Interaction detection of driving factors on FVC in the Hulun Buir (a1-a3), Horqin (b1-b3), Otindag (c1-c3), and Mu Us (d1-d3) sandy lands during the periods 2000-2020, 2000-2010, and 2011-2020
Fig. 7 Structural equation model (SEM) for FVC in the Hulun Buir (a), Horqin (b), Otindag (c), and Mu Us (d) sandy lands during the period 2000-2020. p, probability; df, degree of freedom; *, P<0.050 level; **, P<0.010 level; ***, P<0.001 level.
Fig. 8 SEM for FVC in the Hulun Buir(a), Horqin (b), Otindag (c), and Mu Us (d) sandy lands during the period 2000-2010. p, probability; df, degree of freedom; *, P<0.050 level; ***, P<0.001 level.
Fig. 9 SEM for FVC in the Hulun Buir (a), Horqin (b), Otindag (c), and Mu Us (d) sandy lands during the period 2011-2020. p, probability; df, degree of freedom; **, P<0.010 level; ***, P<0.001 level.
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