Ecological vulnerability assessment and its driving force based on ecological zoning in the Loess Plateau, China

Ecological Indicators(2024)

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摘要
The imbalance of natural, social, and economic systems has engendered a multitude of environmental challenges, resulting in the increasingly vulnerable ecological environments. To mitigate the adverse effects of external disturbances on ecosystems, ecological vulnerability assessment (EVA) has emerged as a pivotal research domain in environmental science. Therefore, EVA of the Loess Plateau from 2000 to 2020 was conducted, utilizing a sensitivity-resilience-pressure (SRP) model based on ecological zoning, and identifying its driving factors. The findings revealed an overall ecological vulnerability index (EVI) of 0.53, signifying indicating a moderate vulnerability, and it exhibited a decreasing trend, with EVI of 0.58, 0.53, and 0.49, respectively. The EVI in the five ecological zones was: S5 (sandy desert region, 0.80) > S3 (agricultural irrigation regions, 0.66) > S1 (loess hilly and gully region, 0.52) > S4 (river valley plain region, 0.43) > S2 (Earth-rocky mountainous region, 0.38). Notably, a declining pattern in EVI was observed in S5, S1, and S3, while S2 and S4 experienced an increase. Moreover, the grade of EV reduced from the northwest to the southeast during all three periods, with a distinct positive spatial correlation and the presence of hot spots, sub-hot spots, sub-cold spots, and cold spots. Vegetation cover, humidity, and dryness were the main driving factors of EV, displaying significant interrelationships among all indicators. Finally, targeted protective strategies were proposed to enhance EV in different ecological zones of the Loess Plateau. Overall, EV in five ecological zones exhibited distinct spatiotemporal variation. This study offered a valuable framework of EVA applicable to arid areas, thereby promoting the ecological management and healthy development in vulnerable regions.
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关键词
Ecological vulnerability assessment,Ecological zones,SRP model,Multi-source remote sensing,Driving factors
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