A trend toward shorter duration and higher intensity of grassland degradation in Chinese oases

Degradation events have been widely observed in grassland ecosystems, yet how their patterns shift under climate change and human activities remains poorly understood. This is especially important for China’s oasis grasslands, characterised by high ecological vulnerability and strong human pressure. Here, we combine temporal segmentation of spectral trajectories with machine learning to examine the evolution, impacts and potential drivers of China’s oasis grassland degradation events during 1990–2021. We find that degradation events are widespread, affecting 68.16% of oasis grasslands. Short-term events (3–9 years) exhibit significantly higher degradation intensity than long-term events (≥10 years) and continue to expand in area. Legacy effects differ among event types, with long-term events reducing ecosystem stability and short-term events causing larger productivity declines. The expansion of short-term events is associated with warming, wetting, greater climate variability and stronger human pressure. These findings suggest that grassland management should place greater emphasis on short-term, high-intensity degradation events and their legacy effects on productivity. China’s oasis grasslands have experienced widespread degradation affecting 68% of the area, with short-term events causing stronger degradation and greater productivity losses than long-term events, according to temporal segmentation of spectral trajectories and machine learning of 1990-2021 data.

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Publication Details

Journal
Communications Earth & Environment
Published
2026-09-09
DOI
https://doi.org/10.1038/s43247-026-04017-6
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

A trend toward shorter duration and higher intensity of grassland degradation in Chinese oases

Xutong Wu, Yuchuan Luo, Shuai Wang, Linhai Cheng et al.
Communications Earth & Environment
Remote Sensing in Agriculture
article

A trend toward shorter duration and higher intensity of grassland degradation in Chinese oases

Xutong Wu, Yuchuan Luo, Shuai Wang, Linhai Cheng, Yanxu Liu, Changjia Li, Peng Chen, Qiang Tang, Bojie Fu, Bixia Hu, Fuyou Tian, Wenxuan Xu, Ya Gao
article en

Abstract

Degradation events have been widely observed in grassland ecosystems, yet how their patterns shift under climate change and human activities remains poorly understood. This is especially important for China’s oasis grasslands, characterised by high ecological vulnerability and strong human pressure. Here, we combine temporal segmentation of spectral trajectories with machine learning to examine the evolution, impacts and potential drivers of China’s oasis grassland degradation events during 1990–2021. We find that degradation events are widespread, affecting 68.16% of oasis grasslands. Short-term events (3–9 years) exhibit significantly higher degradation intensity than long-term events (≥10 years) and continue to expand in area. Legacy effects differ among event types, with long-term events reducing ecosystem stability and short-term events causing larger productivity declines. The expansion of short-term events is associated with warming, wetting, greater climate variability and stronger human pressure. These findings suggest that grassland management should place greater emphasis on short-term, high-intensity degradation events and their legacy effects on productivity. China’s oasis grasslands have experienced widespread degradation affecting 68% of the area, with short-term events causing stronger degradation and greater productivity losses than long-term events, according to temporal segmentation of spectral trajectories and machine learning of 1990-2021 data.

Communications Earth & Environment
Qinghai University (CN), Southwest University (CN), Chinese Academy of Sciences (CN), Beijing Normal University (CN), Aerospace Information Research Institute (CN), Qinghai Meteorological Institute (CN), Research Center for Eco-Environmental Sciences (CN), State Key Laboratory of Remote Sensing Science (CN)
Climate action
Openalex Percentile: Top 10%
Remote Sensing in Agriculture
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