Monitoring Long-Term Vegetation Dynamics in the Hulun Lake Basin of Northeastern China Through Greening and Browning Speeds from 1982 to 2015

TL;DR

This study tracks vegetation changes in the Hulun Lake Basin from 1982 to 2015, finding that greening is increasing and browning is slowing, likely due to climate and environmental factors.

Contribution

The study introduces VNDVI, a new metric that captures greening and browning speeds, offering process-level insights beyond traditional NDVI metrics.

Findings

Vegetation in over 70% of the basin showed a significant greening trend of +0.0028 yr−1.

Spring greening accelerated at +0.8% yr−1, while autumn browning slowed at −0.6% yr−1.

Temperature, precipitation, and solar radiation were key drivers of seasonal vegetation dynamics.

Abstract

Vegetation dynamics in the Hulun Lake Basin (HLB), a vulnerable grassland–wetland–forest transition zone in Northeastern Inner Mongolia, North China, are sensitive to climate change, but traditional greenness metrics like the normalized difference vegetation index (NDVI) lack process-level insights. Using the GIMMS NDVI3g dataset (1982–2015) and meteorological data, this study analyzed the spatiotemporal dynamics of the NDVI and vegetation NDVI change rate (VNDVI)—a metric quantifying greening and browning speeds via NDVI temporal variation—employing linear regression and partial correlation analyses. The NDVI exhibited an overall significant upward trend of +0.0028 yr−1 (p < 0.05) across more than 70% of the basin, indicating a persistent greening tendency. The VNDVI revealed an accelerated spring greening rate of +0.8% yr−1 (p < 0.05) and a slowed autumn browning rate of −0.6% yr−1 (p < 0.05), reflecting an extended growing season. Spatial correlation analysis showed that the temperature dominated spring greening (r = 0.52), precipitation governed summer growth (r = 0.64), and solar radiation modulated autumn senescence (r = 0.38). Compared with the NDVI, the VNDVI was more sensitive to both climatic fluctuations and anthropogenic disturbances, highlighting its utility in capturing process-level vegetation dynamics. These findings provide quantitative insights into the mechanisms of vegetation change in the HLB and offer scientific support for ecological conservation in North China’s grassland–forest ecotone.

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