Mid-Spine Belt of Beautiful China: future reversal of increasing vegetation greening in response to an evolving environment

SDG15 emphasizes the criticality of ecosystem sustainability. The interplay between vegetation and the environment plays a crucial role in maintaining ecological equilibrium. The Mid-Spine Belt of Beautiful China (MSBBC), a novel geographical designation, encompasses agro-pastoral production and liv...

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Veröffentlicht in:International journal of digital earth 2025-12, Vol.18 (1)
Hauptverfasser: Fu, Jing, Su, Baoling, Qin, Jianxin, Hu, Yong
Format: Artikel
Sprache:eng
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Zusammenfassung:SDG15 emphasizes the criticality of ecosystem sustainability. The interplay between vegetation and the environment plays a crucial role in maintaining ecological equilibrium. The Mid-Spine Belt of Beautiful China (MSBBC), a novel geographical designation, encompasses agro-pastoral production and living spaces in China. However, vegetation dynamics in the area remain incompletely characterized. Therefore, this study investigated spatiotemporal variations of NDVI in MSBBC from 2000 to 2022, introducing the V-statistic to simulate durations of future vegetation trends. Since 2000, a majority of the MSBBC has experienced vegetation improvement, with significant enhancement observed in 77.34% of the area. This trend is primarily driven by increased precipitation and wind speed under climate warming, coupled with increased afforestation efforts and reduced livestock populations. Conversely, areas presenting significant vegetation degradation account for only 1.07% of the total, mainly due to urbanization and economic progress, partially explained by decreased sunshine duration and relative humidity. Evidently, China’s long-standing commitment to environmental preservation and ecological restoration has greatly mitigated degradation. Importantly, vegetation greening is projected to stagnate over the next decade. These findings deepen our understanding of ecosystem enhancement influenced by climatic and anthropogenic factors and provide a valuable reference for devising innovative approaches to remote sensing time series predictions.
ISSN:1753-8947
1753-8955
DOI:10.1080/17538947.2024.2449183