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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (5): 605-623    DOI: 10.1007/s40333-025-0052-4    
Research article     
Dynamic evolution of the NDVI and driving factors in the Mu Us Sandy Land of China from 2002 to 2021
CHAO Yan, ZHU Yonghua, WANG Xiaohan, LI Jiamin, LIANG Li'e*()
College of Civil Engineering and Architecture, Yan'an University, Yan'an 716000, China
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Abstract  

Investigating the spatiotemporal evolution of vegetation and its response mechanisms to natural and anthropogenic elements is crucial for regional vegetation restoration and ecological preservation. The Mu Us Sandy Land (MUSL), which is situated in the semi-arid zone of northwestern China adjacent to the Loess Plateau, has been at the forefront of desertification and oasis formation over the past two millennia. This study is based on the synthesis of the Normalized Difference Vegetation Index (NDVI) data from MOD13A3 data in the MODIS (Moderate-Resolution Imaging Spectroradiometer) dataset (2002-2021) and climate data (temperature and precipitation) at annual and monthly scales from the National Earth System Science Data Center. A range of analytical methods, including univariate linear regression, Theil-Sen trend analysis and Mann-Kendall significance test, correlation analysis, residual analysis, and Hurst index, were used to explore the response mechanisms of the NDVI to climate change and human activities and to predict the future trends of the NDVI in the MUSL. The results showed that through the method of correlation analysis, in terms of both spatially averaged correlation coefficients and area proportion, the NDVI was positively correlated with temperature and precipitation in 97.59% and 96.51% of the study area, respectively, indicating that temperature has a greater impact on the NDVI than precipitation. Residual analysis quantified the contributions of climate change and human activities to the NDVI changes, revealing that climate change and human activities contribute up to 30.00% and 70.00%, respectively, suggesting that human activities predominantly affect the NDVI changes in the MUSL. The Hurst index was used to categorize the future trend of the NDVI into four main directions of development: continuous degradation (0.05% of the study area), degradation in the past but improvement in the future (54.45%), improvement in the past but degradation in the future (0.13%), and continuous improvement (45.36%). In more than 50.00% of the regions that have been degraded in the past but were expected to improve in the future, the NDVI was expected to exhibit a stable trend of anti-persistent improvement. These findings provide theoretical support for future ecological protection, planning, and the implementation of ecological engineering in the MUSL, and also offer a theoretical basis for the planning and execution of construction projects, environmental protection measures, and the sustainable development of vegetation.

Key words： Normalized Difference Vegetation Index (NDVI)      climate change      human activities      residual analysis      Hurst index      Mu Us Sandy Land     
Received: 30 November 2024      Published: 31 May 2025
Corresponding Authors: *LIANG Li'e (E-mail: ydjzlle89@yau.edu.cn)
About author: First author contact:

The first author and the second author contributed equally to this work.
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CHAO Yan
ZHU Yonghua
WANG Xiaohan
LI Jiamin
LIANG Li'e
Cite this article:

CHAO Yan, ZHU Yonghua, WANG Xiaohan, LI Jiamin, LIANG Li'e. Dynamic evolution of the NDVI and driving factors in the Mu Us Sandy Land of China from 2002 to 2021. Journal of Arid Land, 2025, 17(5): 605-623.

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http://jal.xjegi.com/10.1007/s40333-025-0052-4     OR     http://jal.xjegi.com/Y2025/V17/I5/605

Fig. 1 Overview of the Mu Us Sandy Land (MUSL) based on land use types. It is noted that the land use type data were sourced from the Resource and Environmental Science Data Platform (https://www.resdc.cn/DOI/DOI.aspx?DOIID=54).
Slope
(NDVIOBS)		 Influential factor Determination of influential factor Contribution of the driver (%)
Slope(NDVICC) Slope(NDVIHA) Climate change Human activities
>0 Climate change and human activities >0 >0 Slope(NDVICC) Slope(NDVIOBS) Slope(NDVIHA) Slope(NDVIOBS)
Climate change >0 <0 100 0
Human activities <0 >0 0 100
<0 Climate change and human activities <0 <0 Slope(NDVICC) Slope(NDVIOBS) Slope(NDVIHA) Slope(NDVIOBS)
Climate change <0 >0 100 0
Human activities >0 <0 0 100
Table 1 Identification criteria and calculations of the contribution of the drivers to the NDVI changes
Fig. 2 Temporal changes in the annual (a) and seasonal (b) NDVI and annual precipitation and temperature (c) in the MUSL from 2002 to 2021
Fig. 3 Spatial trend (a) and significance (b) of the annual NDVI in the MUSL during 2002-2021
Fig. 4 Spatial trends (a1-d1) and significance (a2-d2) of the seasonal NDVI in the MUSL during 2002-2021. (a1 and a2), spring; (b1 and b2), summer; (c1 and c2), autumn; (d1 and d2), winter.
Fig. 5 Spatial distributions of the Pearson correlation coefficients between the annual NDVI and climate factors in the MUSL during 2002-2021. (a), correlation between the annual NDVI and temperature; (b), correlation between the annual NDVI and precipitation.
Fig. 6 Spatial distributions of the Pearson correlation coefficients between the NDVI and temperature in spring (a), summer (b), autumn (c), and winter (d) in the MUSL during 2002-2021
Fig. 7 Spatial distributions of the Pearson correlation coefficients between the NDVI and precipitation in spring (a), summer (b), autumn (c), and winter (d) in the MUSL during 2002-2021
Fig. 8 Spatial distribution of the drivers of the NDVI changes in the MUSL during 2002-2021
Fig. 9 Spatial distributions of the contribution rates of climate change (a) and human activities (b) to the NDVI changes in the MUSL during 2002-2021. It is noted that the positive contribution rate indicates that climate change or human activities have a positive effect on the NDVI, whereas the negative contribution rate indicates that climate change or human activities have a negative effect on the NDVI.
Fig. 10 Spatial distributions of the Hurst index of the NDVI (a) and the future NDVI trend (b) in the MUSL. It is noted that the trend analysis of NDVI was conducted only for regions that passed the statistical significance test.
Direction of the NDVI development Future NDVI trend Area proportion (%)
Continuous degradation Strong continuous degradation 0.00
Weak continuous degradation 0.05
Degradation in the past but improvement in the future Anti-strong continuous improvement 6.27
Anti-weak continuous improvement 48.19
Improvement in the past but degradation in the future Anti-weak continuous degradation 0.11
Anti-strong continuous degradation 0.02
Continuous improvement Weak continuous improvement 39.79
Strong continuous improvement 5.57
Table 2 NDVI trend in the MUSL in the future
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