| Summary: | Vegetation disturbance and recovery are key factors in shaping ecological governance policies in arid regions, especially under the accelerating impacts of global climate change. However, most studies focus on broad trends, often neglecting fine-scale spatial and temporal variations. In this study, we developed indices for vegetation disturbance and recovery based on kNDVI (kernel-based Normalized Difference Vegetation Index) and surface albedo, combined with the LandTrendr algorithm, to assess vegetation dynamics in China’s arid regions from 2000 to 2023. Our results show that compared to traditional indices, the new indices capture subtle changes in vegetation disturbance and recovery with greater accuracy. Approximately 63.26 % of the region experienced at least one disturbance, primarily in the north-central arid regions, while 27.64 % experienced multiple disturbances, and 36.74 % remained undisturbed. Around 80.14 % exhibited recovery, mostly in the northern Tibetan Plateau, the vegetated margins of the Tarim Basin, and western Mongolian Plateau. The innovative combination of kNDVIand Albedo enhances the accuracy of vegetation dynamics assessment, offering a transferable Indicator for monitoring dryland ecosystems globally. These findings provide valuable insights into ecosystem resilience and inform ecosystem restoration strategies in the context of climate change.
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