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<p>Situated as a critical ecological barrier in northern China, the Mu Us Sandy Land faces escalating desertification, progressive vegetation loss, and intensified anthropogenic disturbances, predominantly driven by recurring drought events and the expansion of agricultural land. Understanding the spatiotemporal dynamics and underlying drivers of ecological environment quality is vital for informed ecosystem management and restoration planning. In this study, we utilized the Google Earth Engine cloud platform and multi-source remote sensing data to calculate the Remote Sensing-based Ecological Index (RSEI) for the Mu Us Sandy Land from 2000 to 2024. We investigated multiple-dimensional change in environmental quality and applied the Optimal Parameters-based Geographic Detector model to quantify the independent and interactive contributions of natural and anthropogenic drivers. Results showed: (1) Ecological environment quality exhibited a significant upward trend, with the average RSEI increasing from 0.26 to 0.35 (average annual rate = 0.002, p &lt; 0.05), and was spatially higher in the southeast than in the northwest. (2) Areas with “fair” ecosystem quality predominated, comprising 60.1% of the region annually. Improvement zones (67.5%) were mainly located in the northeast and south, aligned with large-scale ecological restoration efforts, while degraded areas (32.4%) were associated with dune encroachment, vegetation decline, and urban expansion. (3) Human activities were the dominant drivers of ecological change, with afforestation area (q = 0.34), large livestock numbers (q = 0.32), and GDP (q = 0.27) exerting the greatest influence. Significant interactions effects, particularly those between GDP, population density, and other natural or anthropogenic drivers such as wind speed and livestock numbers — exerted a markedly strong influence on ecological restoration effects in the Mu Us Sandy Land. This study provides a systematic approach integrating remote sensing and spatial analysis, offering critical insights for sustainable land management in arid and semi-arid ecosystems.</p> Graphical Abstract <p></p> <p>Based on the graphical workflow, this study investigates the spatiotemporal evolution of ecological environment quality in the Mu Us Sandy Land and quantifies the relative roles of climate and human activities. Multi-source remote sensing datasets on Google Earth Engine were processed to construct an RSEI time series (2000–2024) by integrating greenness (NDVI), humidity (Wet), dryness (NDBSI), and heat (LST) via Principal Component Analysis. Spatiotemporal patterns and trends were examined using the Theil-Sen slope and Mann-Kendall tests, while the Optimal Parameters-based Geographic Detector (OPGD) was employed to quantify the explanatory power of natural and anthropogenic drivers and their interactive effects. The time‑series bars/line in the figure show overall improvement, with mean RSEI rising from 0.26 to 0.35 (annual rate = 0.002, p &lt; 0.05) and a persistent southeast–northwest gradient. Annual RSEI maps shows “fair” quality dominates (~ 60.1%), with improvement zones (67.5%) concentrated in the northeast and south, and degradation (32.4%) linked to dune encroachment, vegetation decline, and urban expansion. The Theil-Sen/Mann-Kendall panel highlights statistically significant improvement and localized degradation hotspots. Driver analysis via OPGD identifies human activities as the dominant force shaping ecological change, with afforestation area (q = 0.34), large livestock population (q = 0.32), and GDP (q = 0.27) exhibiting the strongest explanatory power. Interaction detection reveals nonlinear enhancement effects, particularly those between GDP, population density, and other natural or anthropogenic drivers such as wind speed and large livestock numbers, underscoring the synergistic impacts of socioeconomic and climatic factors. The framework supports evidence‑based restoration and land management in arid and semi‑arid regions.</p>

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Spatiotemporal Trajectories of Eco-Environmental Quality in the Mu Us Sandy Land, China

  • 期刊论文

  • 发表日期：

    2025年12月24日

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  • Kang Yang,
  • Yanping Cao,
  • Yingjun Pang,
  • Shaokun Wang,
  • Chaolin Mu,
  • Dandan Liu

摘要

Situated as a critical ecological barrier in northern China, the Mu Us Sandy Land faces escalating desertification, progressive vegetation loss, and intensified anthropogenic disturbances, predominantly driven by recurring drought events and the expansion of agricultural land. Understanding the spatiotemporal dynamics and underlying drivers of ecological environment quality is vital for informed ecosystem management and restoration planning. In this study, we utilized the Google Earth Engine cloud platform and multi-source remote sensing data to calculate the Remote Sensing-based Ecological Index (RSEI) for the Mu Us Sandy Land from 2000 to 2024. We investigated multiple-dimensional change in environmental quality and applied the Optimal Parameters-based Geographic Detector model to quantify the independent and interactive contributions of natural and anthropogenic drivers. Results showed: (1) Ecological environment quality exhibited a significant upward trend, with the average RSEI increasing from 0.26 to 0.35 (average annual rate = 0.002, p < 0.05), and was spatially higher in the southeast than in the northwest. (2) Areas with “fair” ecosystem quality predominated, comprising 60.1% of the region annually. Improvement zones (67.5%) were mainly located in the northeast and south, aligned with large-scale ecological restoration efforts, while degraded areas (32.4%) were associated with dune encroachment, vegetation decline, and urban expansion. (3) Human activities were the dominant drivers of ecological change, with afforestation area (q = 0.34), large livestock numbers (q = 0.32), and GDP (q = 0.27) exerting the greatest influence. Significant interactions effects, particularly those between GDP, population density, and other natural or anthropogenic drivers such as wind speed and livestock numbers — exerted a markedly strong influence on ecological restoration effects in the Mu Us Sandy Land. This study provides a systematic approach integrating remote sensing and spatial analysis, offering critical insights for sustainable land management in arid and semi-arid ecosystems.

Graphical Abstract

Based on the graphical workflow, this study investigates the spatiotemporal evolution of ecological environment quality in the Mu Us Sandy Land and quantifies the relative roles of climate and human activities. Multi-source remote sensing datasets on Google Earth Engine were processed to construct an RSEI time series (2000–2024) by integrating greenness (NDVI), humidity (Wet), dryness (NDBSI), and heat (LST) via Principal Component Analysis. Spatiotemporal patterns and trends were examined using the Theil-Sen slope and Mann-Kendall tests, while the Optimal Parameters-based Geographic Detector (OPGD) was employed to quantify the explanatory power of natural and anthropogenic drivers and their interactive effects. The time‑series bars/line in the figure show overall improvement, with mean RSEI rising from 0.26 to 0.35 (annual rate = 0.002, p < 0.05) and a persistent southeast–northwest gradient. Annual RSEI maps shows “fair” quality dominates (~ 60.1%), with improvement zones (67.5%) concentrated in the northeast and south, and degradation (32.4%) linked to dune encroachment, vegetation decline, and urban expansion. The Theil-Sen/Mann-Kendall panel highlights statistically significant improvement and localized degradation hotspots. Driver analysis via OPGD identifies human activities as the dominant force shaping ecological change, with afforestation area (q = 0.34), large livestock population (q = 0.32), and GDP (q = 0.27) exhibiting the strongest explanatory power. Interaction detection reveals nonlinear enhancement effects, particularly those between GDP, population density, and other natural or anthropogenic drivers such as wind speed and large livestock numbers, underscoring the synergistic impacts of socioeconomic and climatic factors. The framework supports evidence‑based restoration and land management in arid and semi‑arid regions.