Spatio-temporal evolution and driving factors of ecological environment quality in Mu Us sandy land
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摘要:
［目的］ 探究毛乌素沙地生态环境质量的动态特征及驱动因素，为该生态脆弱区的治理与可持续发展提供科学依据。［方法］ 基于GEE（Google Earth Engine）平台，利用2000—2023年MODIS遥感数据构建遥感生态指数RSEI （remote sensing ecological index），通过BFAST（breaks for additive season and trend）突变检测和地理探测器方法，对毛乌素沙地生态环境质量的时空演变特征及驱动因素进行系统分析。［结果］ ①RSEI模型在毛乌素沙地表现出良好的适用性，主成分贡献率均大于70%，可有效反映研究区生态环境演变过程； ②BFAST检验结果表明，2000—2023年研究区生态环境质量在空间上存在显著异质性，其中单调型增加（无突变）占沙地总面积43.4%，集中分布在沙地东部，而单调型减少（无突变）则占33.6%，主要分布于沙地西部及南部边缘地带。RSEI突变年份在空间上呈现“局部聚集，整体分散”并存的分布特征，其中2019年为突变最频繁年份；③地理探测器分析表明土壤湿度是影响生态环境质量空间分异的主导因子，同时突变发生最多的2019年，气温和降水与其他因素的交互作用明显增强。［结论］ 毛乌素沙地的生态环境演变规律受气候变化与人类活动的双重驱动，区域生态治理与政策制定应考虑综合因素的影响。
Abstract:
［Objective］ The dynamic characteristics and driving factors of ecological environmental quality in the Mu Us sandy land are investigated， aiming to provide a scientific basis for the restoration and sustainable development of this ecologically fragile area. ［Methods］ Based on the Google Earth Engine （GEE） platform， MODIS remote sensing data from 2000 to 2023 were used to construct the remote sensing ecological index （RSEI）. Through breaks for additive season and trend （BFAST） abrupt change detection and geodetector method， the spatiotemporal evolution characteristics and driving factors of ecological environmental quality in the Mu Us sandy land were systematically analyzed. ［Results］ ① The RSEI model had good applicability in the Mu Us sandy land， with the principal component contribution rates all exceeding 70%， effectively reflecting the evolution process of ecological environment in the study area. ② The BFAST detection results showed that the ecological environmental quality in the study area exhibited significant spatial heterogeneity from 2000 to 2023. The monotonic increase type （without abrupt change） accounted for 43.4% of the total sandy land area， primarily distributed in the eastern part of the sandy land， while the monotonic decrease type （without abrupt change） accounted for 33.6%， mainly located in the western and southern marginal areas. The spatial distribution of RSEI abrupt change years demonstrated a pattern of “localized clustering and overall dispersion”， with 2019 being the year with the highest frequency of abrupt changes. ③ The geodetector analysis showed that soil moisture was the dominant factor affecting the spatial differentiation of ecological environmental quality. Meanwhile， in 2019， the year with the most frequent abrupt changes， the interaction between temperature， precipitation， and other factors was significantly enhanced. ［Conclusion］ The ecological environment evolution patterns of the Mu Us sandy land are driven by both climate change and human activities. Regional ecological restoration and policy formulation should consider the influence of multiple factors.