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<p>The water source area (WSA) of the central route of the South-to-North Water Diversion Project (SNWD-C) is crucial for China’s water and ecological security. However, vegetation dynamics and associated driving mechanisms in this region remain understudied. This study integrates Moderate Resolution Imaging Spectroradiometer (MODIS)–derived Normalized Difference Vegetation Index (NDVI) data (2000–2022) with climatic and anthropogenic factors using the Google Earth Engine platform. To quantify vegetation dynamics, we employed Sen’s slope estimator, the Mann–Kendall test, and the coefficient of variation. Furthermore, the Optimal Partition Geographical Detector (OPGD) model was applied to identify the dominant drivers of vegetation change and to explore their interactive effects. Results show that 98.8% of the WSA (excluding water bodies) maintained a high average NDVI (&gt; 0.5), with significant greening trends (+ 0.0038/a) in 81.4% of the area, mainly in mid-low altitude grasslands. Urban areas showed localized vegetation decline (1.8%). Land surface temperature (LST), Digital Elevation Model (DEM), human footprint (HFP), and land cover (LC) were major drivers. Interactions among these factors significantly improved explanatory power. Cooling trends promoted greening, while warming and intense human activities were linked to vegetation decline. This study provides insights into the mechanisms of vegetation change in the WSA and offers scientific guidance for ecological restoration and sustainable water resource management under the SNWD-C framework.</p>

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Vegetation dynamics and associated driving mechanisms in the water source area of the central route of the South-to-North Water Diversion, China during 2000–2022

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  • 发表日期：

    2025年11月26日

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  • Limei Wang,
  • Yongtao Jiang

摘要

The water source area (WSA) of the central route of the South-to-North Water Diversion Project (SNWD-C) is crucial for China’s water and ecological security. However, vegetation dynamics and associated driving mechanisms in this region remain understudied. This study integrates Moderate Resolution Imaging Spectroradiometer (MODIS)–derived Normalized Difference Vegetation Index (NDVI) data (2000–2022) with climatic and anthropogenic factors using the Google Earth Engine platform. To quantify vegetation dynamics, we employed Sen’s slope estimator, the Mann–Kendall test, and the coefficient of variation. Furthermore, the Optimal Partition Geographical Detector (OPGD) model was applied to identify the dominant drivers of vegetation change and to explore their interactive effects. Results show that 98.8% of the WSA (excluding water bodies) maintained a high average NDVI (> 0.5), with significant greening trends (+ 0.0038/a) in 81.4% of the area, mainly in mid-low altitude grasslands. Urban areas showed localized vegetation decline (1.8%). Land surface temperature (LST), Digital Elevation Model (DEM), human footprint (HFP), and land cover (LC) were major drivers. Interactions among these factors significantly improved explanatory power. Cooling trends promoted greening, while warming and intense human activities were linked to vegetation decline. This study provides insights into the mechanisms of vegetation change in the WSA and offers scientific guidance for ecological restoration and sustainable water resource management under the SNWD-C framework.