Scholarlink

<p>The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region. To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors, a modified remote sensing ecological index (MRSEI) was developed by incorporating evapotranspiration. Spatial and temporal patterns were analyzed using the coefficient of variation, spatial autocorrelation, and semi-variogram methods, while influencing factors were explored via the optimal parameter geographical detector model. The MRSEI’s first principal component loadings and rankings aligned with those of RSEI (average contribution: 81.31%), effectively reflecting spatiotemporal variations. At sub-irrigation district and landscape scales, ecological quality was slightly lower than at the district level but remained stable. Moderate and good ecological grades accounted for 36.28% and 33.38% of the area, respectively, at the district scale, and the moderate grade reached 70.48% on smaller scales. Spatial heterogeneity intensified with decreasing scale, and human activity lost explanatory power below a 5 km range. Human factors mainly drove ecological differentiation at the district scale, while natural factors dominated at finer scales. The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-scale analysis of spatiotemporal evolution and driving factors of eco-environmental quality in a Ningxia irrigation district, China

  • 期刊论文

  • 发表日期：

    2026年2月13日

查看全文
  • Zequan Li,
  • Mingtang Chai,
  • Lei Zhu,
  • Junjie He,
  • Yimin Ding,
  • Fengkun Xu,
  • Xiyuan Xu

摘要

The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region. To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors, a modified remote sensing ecological index (MRSEI) was developed by incorporating evapotranspiration. Spatial and temporal patterns were analyzed using the coefficient of variation, spatial autocorrelation, and semi-variogram methods, while influencing factors were explored via the optimal parameter geographical detector model. The MRSEI’s first principal component loadings and rankings aligned with those of RSEI (average contribution: 81.31%), effectively reflecting spatiotemporal variations. At sub-irrigation district and landscape scales, ecological quality was slightly lower than at the district level but remained stable. Moderate and good ecological grades accounted for 36.28% and 33.38% of the area, respectively, at the district scale, and the moderate grade reached 70.48% on smaller scales. Spatial heterogeneity intensified with decreasing scale, and human activity lost explanatory power below a 5 km range. Human factors mainly drove ecological differentiation at the district scale, while natural factors dominated at finer scales. The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies.