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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (12): 1785-1805    DOI: 10.1007/s40333-025-0035-5     CSTR: 32276.14.JAL.02500355
Research article     
Comparison of different vegetation indices for estimating vegetation changes and analyzing driving factors in a semi-arid area, China
MA Yutao1,2, GONG Jie1,2,*(), JIN Tiantian1,2, XU Tianyu1,2, KAN Guobin1,2
1Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
2Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, China
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Abstract  

Climate warming and humidification trends have significantly influenced vegetation growth patterns in Chinese semi-arid areas. Exploring vegetation dynamics is crucial for understanding regional ecosystem structure and improving the efforts of ecosystem restoration. However, the applicability of various vegetation indices (VIs) in these arid areas remains uncertain. Evaluating the applicability of multiple VIs for vegetation monitoring can elucidate the variability of VIs performance at regional scale. Therefore, this study selected the Zuli River Basin (ZLRB), a typical loess hilly watershed in the semi-arid areas of China. Using Landsat data, we calculated the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and kernel NDVI (kNDVI) for the ZLRB from 1990 to 2020. We analyzed the spatiotemporal variations of these VIs using trend analysis and the Mann-Kendall test, and quantified the contributions of climate change (considering time-lag effects) and human activities to VIs changes through wavelet and residual analyses. Results indicated that VIs generally exhibited an upward trend in the ZLRB, with significant improvements observed in 54.91% of the area for NDVI, 31.69% for EVI, and 33.71% for kNDVI. Among them, NDVI outperformed EVI and kNDVI in capturing vegetation changes in the semi-arid area. VIs responded to precipitation with 1-month time lag and no time lag to temperature during growing season. Moreover, precipitation had a stronger positive correlation with VIs than temperature. Climate change was identified as the dominant driver of vegetation dynamics in the ZLRB, accounting for 93.12% of NDVI variation, while human activities contributed only 6.88%. Comparative analysis of VIs suggests that NDVI was more suitable for describing vegetation changes in the typical arid area of the ZLRB. Our findings underscore the importance of selecting appropriate VIs for targeted ecological restoration and sustainable land management.

Key words： vegetation indices      spatiotemporal change      time-lag effect      climate change      human activities      the Zuli River Basin     
Received: 25 April 2025      Published: 31 December 2025
Corresponding Authors: *GONG Jie (E-mail: jgong@lzu.edu.cn)
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MA Yutao
GONG Jie
JIN Tiantian
XU Tianyu
KAN Guobin
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MA Yutao, GONG Jie, JIN Tiantian, XU Tianyu, KAN Guobin. Comparison of different vegetation indices for estimating vegetation changes and analyzing driving factors in a semi-arid area, China. Journal of Arid Land, 2025, 17(12): 1785-1805.

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http://jal.xjegi.com/10.1007/s40333-025-0035-5     OR     http://jal.xjegi.com/Y2025/V17/I12/1785

Classification Categorization criterion
Significant increase Slope>0, P<0.01
Insignificant increase Slope>0, 0.01≤P<0.05
Insignificant increase Slope>0, P≥0.05
Insignificant change Slope=0
Insignificant decrease Slope<0, P≥0.05
Insignificant decrease Slope<0, 0.01≤P<0.05
Significant decrease Slope<0, P<0.01
Table 1 Quantitative categorization criteria for dynamic changes in vegetation indices (VIs) of the Zuli River Basin (ZLRB)
Fig. 1 Analysis of inter-annual variation, anomalies, and the Mann-Kendall trends of three different vegetation indices (VIs) in the Zuli River Basin (ZLRB) from 1990 to 2020. (a), NDVI (Normalized Difference Vegetation Index); (b), EVI (Enhanced Vegetation Index); (c), kernel NDVI (kNDVI). UF, unformatted fit; UB, underidentification bias. The shaded area indicates significance at the 95.00% confidence level.
Fig. 2 Spatial change trends of VIs (a-c) and their proportions (d-f) to each land use type in the ZLRB from 1990 to 2020
Fig. 3 Spatiotemporal variation trends of climatic variables in the ZLRB from 1990 to 2020. (a), spatial variation trend in annual average temperature; (b), temporal variation trend of annual average temperature; (c), spatial variation trend in annual average precipitation; (d), temporal variation trend of annual average precipitation.
Fig. 4 Wavelet change analysis of monthly NDVI, precipitation, and temperature of the ZLRB from 1990 to 2020 and their corresponding lagged months. (a1-a3), wavelet power spectra of NDVI, precipitation, and temperature; (b1 and c1), cross wavelet transforms of NDVI-temperature and NDVI-precipitation; (b2 and c2), wavelet coherence transforms of NDVI-temperature and NDVI-precipitation; (b3 and c3), time-lagged response months of temperature and precipitation. The arrows in Figure b1, b2, c1, and c2 represent the relative phase relationship between two sequences. The negative lag values in Figure b3 and c3 indicate that NDVI leads climate variables, while the positive lag values indicate that NDVI lags behind climate variables.
Fig. 5 Spatial distribution of partial correlation coefficients of VIs with temperature (a-c) and precipitation (d-f) of the ZLRB from 1990 to 2020
Fig. 6 Effects of climatic variables on inter-annual variations of VIs in the ZLRB. (a-c), contribution of temperature to inter-annual variation of VIs; (d-f), contribution of precipitation to inter-annual variation of VIs.
Fig. 7 Contributions of climate change (a1, b1, and c1) and human activities (a2, b2, and c2) to inter-annual variations of VIs and proportion of each land use type (a3, b3, and c3) affected by precipitation-lag (Pre), temperature-lag (Tem), climate change (C), and human activities (H) on VIs in the ZLRB
Fig. 8 Linear fitting relationships between Landsat-8 VIs and Sentinel-2 VIs. (a), NDVI; (b), EVI; (c), kNDVI. RMSE, Root Mean Square Error.
Fig. 9 Comparison of localized characteristics of typical areas of VIs in the ZLRB from 1990 to 2020. (a), typical area and land use type; (b1-b3, c1-c3, d1-d3, and e1-e3), annual average of VIs; (b4-b6, c4-c6, d4-d6, and e4-e6), annual trend of VIs.
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