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The aim is to quantify vegetation cover dynamics and identify key driving factors in Qinghai Province over a 22-year period.

February 19, 2026LandOpen Access

Spatiotemporal Evolution of Vegetation Cover and Identification of Driving Factors Based on kNDVI and XGBoost-SHAP: A Study from Qinghai Province, China

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The aim is to quantify vegetation cover dynamics and identify key driving factors in Qinghai Province over a 22-year period.
Analyzed MOD13Q1V6 dataset from Google Earth Engine (GEE) from 2001 to 2023.
Constructed kNDVI dataset to examine vegetation cover changes.
Employed Theil–Sen–Mann–Kendall trend test and centroid migration model for trend analysis.
Utilized XGBoost-SHAP model to assess relationships between vegetation cover and driving factors.
kNDVI showed a fluctuating growth trend with an annual increase of 0.0016, spatially higher in the southeast.
Stable areas dominated kNDVI, making up 52.75%, with sustainable improvement zones at 39.91%.
Key driving factors identified include annual mean precipitation and digital elevation model, with specific threshold inflection points.

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Reveals vegetation cover trends in Qinghai Province, identifying driving factors and their thresholds.

Yang et al. (2026) studied this question.

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