Unveiling grassland dynamics: trends and drivers of degradation and improvement in the Eurasian Steppe since 2000

As the most extensive temperate grassland in the world, the Eurasian Steppe provides various ecological services that support the environment and human well-being. However, grassland degradation has become a serious environmental issue. Most of the traditional degradation assessments ignore the sensitivity of grassland ecosystems to climatic conditions. In response, our study introduces a new comprehensive identification framework that integrates vegetation growth and climate change, using a novel long-term monitoring methodology to detect grassland degradation and improvement. The framework quantifies the area and degree of degradation and improvement in the Eurasian Steppe using long time-series data from 2000 − 2020. Then, the driving factors of grassland change were analyzed using a quantitative model. Our findings reveal a clear trend of improvement in the Eurasian Steppe was identified, with the improved area being 4.72 times larger than the degraded area (221.4 × 104 and 46.92 × 104 km2, respectively). The Tibetan Plateau and Loess Plateau led to the improvement. Simultaneously, the area surrounding the northern Caspian Sea has been severely degraded. The three areas correspond to frigid humid and semi-humid grassland, temperate humid and semi-humid grassland, and temperate arid and semi-arid grassland, respectively. Globally, the combined effects of climate change and human activities dominated the observed grassland degradation and improvement, accounting for 77.13% and 89.64%, respectively. Our method provides a robust tool for detecting grassland degradation and improvement across large scales, offering scientific support for achieving the United Nations’ Sustainable Development Goals (SDGs), particularly land degradation neutrality (LDN).

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Publication Details

Journal
GIScience & Remote Sensing
Published
2024-11-21
DOI
https://doi.org/10.1080/15481603.2024.2430638
Citations
1
Primary Topic
Rangeland Management and Livestock Ecology
Type
article
Field-Weighted Citation Impact
0.71

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article

Unveiling grassland dynamics: trends and drivers of degradation and improvement in the Eurasian Steppe since 2000

Ziyu Yan, Pengyao Qin, Bin Sun, Ting Gao et al.
1 citations
GIScience & Remote Sensing
Rangeland Management and Livestock Ecology
0.71
article

Unveiling grassland dynamics: trends and drivers of degradation and improvement in the Eurasian Steppe since 2000

Ziyu Yan, Pengyao Qin, Bin Sun, Ting Gao, Yifu Li, Zhihai Gao
article en
1 citations

Abstract

As the most extensive temperate grassland in the world, the Eurasian Steppe provides various ecological services that support the environment and human well-being. However, grassland degradation has become a serious environmental issue. Most of the traditional degradation assessments ignore the sensitivity of grassland ecosystems to climatic conditions. In response, our study introduces a new comprehensive identification framework that integrates vegetation growth and climate change, using a novel long-term monitoring methodology to detect grassland degradation and improvement. The framework quantifies the area and degree of degradation and improvement in the Eurasian Steppe using long time-series data from 2000 − 2020. Then, the driving factors of grassland change were analyzed using a quantitative model. Our findings reveal a clear trend of improvement in the Eurasian Steppe was identified, with the improved area being 4.72 times larger than the degraded area (221.4 × 104 and 46.92 × 104 km2, respectively). The Tibetan Plateau and Loess Plateau led to the improvement. Simultaneously, the area surrounding the northern Caspian Sea has been severely degraded. The three areas correspond to frigid humid and semi-humid grassland, temperate humid and semi-humid grassland, and temperate arid and semi-arid grassland, respectively. Globally, the combined effects of climate change and human activities dominated the observed grassland degradation and improvement, accounting for 77.13% and 89.64%, respectively. Our method provides a robust tool for detecting grassland degradation and improvement across large scales, offering scientific support for achieving the United Nations’ Sustainable Development Goals (SDGs), particularly land degradation neutrality (LDN).

GIScience & Remote SensingVol. 61(1)
Institute of Forest Resource Information Techniques (CN), Chinese Academy of Forestry (CN), State Key Laboratory of Remote Sensing Science (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 20%
Rangeland Management and Livestock Ecology
0.71
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