| 陕北黄土高原生态环境质量时空变化监测及驱动力分析 |
| 摘要点击 97 全文点击 3 投稿时间：2024-06-11 修订日期：2024-09-02 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 生态环境质量 时空变化 驱动因素 改进型遥感生态指数（RSEIA） 地理探测器 谷歌地球引擎（GEE） 陕北黄土高原 |
| 英文关键词 eco-environmental quality spatio-temporal change driving factor adjusted remote sensing ecological index（RSEIA） Geodetector Google Earth Engine（GEE） Loess Plateau of Northern Shaanxi |
| DOI 10.13227/j.hjkx.20250744 |
|
| 中文摘要 |
| 陕北黄土高原地处黄土高原核心地带，生态环境敏感而脆弱，水土流失、荒漠化和盐碱化等问题突出，开展区域生态环境质量时空变化综合定量监测与驱动力分析，对于指导生态环境保护工作意义重大. 基于谷歌地球引擎（GEE）平台，结合研究区生态特点，在经典遥感生态指数RSEI基础上，引入综合盐度指数（CSI）及荒漠化差值指数（DDI），构建改进型遥感生态指数RSEIA，对陕北黄土高原2000~2020年的生态环境质量进行动态监测，采用空间自相关，分析生态环境质量空间分布特征，并结合地理探测器，探讨生态环境质量变化的驱动因素. 结果表明：①RSEIA第一主成分（PC1）的贡献率超过91%，能够较为全面地反映各生态因子特征，对研究区生态环境质量评价具有较强适用性. ②过去20 a中，陕北黄土高原生态环境质量明显提升，表现出先增后降、最后再增长的趋势. 期间，极差和较差区域占比从72.83%下降至33.41%，而良好和极好区域占比则从15.93%上升至37.05%. ③生态环境质量改善区域面积大于退化区域，尤其是在2000~2005年期间改善情况最为显著，改善区域占比达到31.37%. 而在2010~2015年期间，退化状况最为严重，退化区域占比达10.14%. 改善区域主要集中在中部和东北部，而退化区域多位于西部. ④研究区生态环境质量的空间分布具有较强的空间正相关，联系较为紧密. 在构成RSEIA的因子中，NDVI对生态环境质量影响最为显著；而其余因素中，降水贡献最大. 研究成果可为该地区生态环境质量监测及保护提供基础数据及科学依据. |
| 英文摘要 |
| Located in the core area， the Loess Plateau of Northern Shaanxi is characterized by a sensitive and fragile eco-environment facing a series of ecological challenges such as soil erosion， desertification， and salinization. Therefore， comprehensive and quantitative monitoring and analysis of the spatio-temporal changes and driving factors of eco-environment quality are critically important for regional eco-environment protection. Based on Google Earth Engine （GEE）， and taking the ecological characteristics into account， an adjusted remote sensing ecological index（RSEIA）was constructed by introducing the composite salinity index （CSI） and desertification difference index （DDI） into the classical remote sensing ecological index （RSEI）. Dynamic monitoring of eco-environment quality in the Loess Plateau of Northern Shaanxi from 2000 to 2020 was carried out. Spatial distribution characteristics and driving factors of eco-environment quality were discussed using spatial autocorrelation analysis and a geo-detector model. The results show that： ① The contribution rate of the first principal component （PC1） of RSEIA generally exceeded 91%， which can be used to comprehensively reflect the characteristics of each ecological factor and has wide application in regional eco-environment quality evaluation. ② Over the past two decades， the regional eco-environment quality has improved distinctly， showing an increasing trend first， followed by a decline and then an increasing pattern. From 2000 to 2020， the area proportion of very poor and poor quality decreased from 72.83% to 33.41%， respectively， and the proportion of good and excellent quality increased from 15.93% to 37.05%. ③ Improved areas were greater than those of deterioration， and significant improvements accounting for 31.37% were observed particularly from 2000 to 2005. From 2010 to 2015， the most severe degradation occurred with a proportion of 10.14%. Improved areas were concentrated in the central and northeastern parts， whereas degraded areas were located mainly in the west. ④ The spatial distribution of the regional eco-environment quality had a strong and close positive spatial correlation. NDVI outperformed other ecological factors and contributed the most to RSEIA， while in other factors， precipitation was the most influential one. The results are expected to provide basic information and scientific foundation for the monitoring and protection of regional eco-environment quality. |
