Wang Jing-Yu, Yang Li-Ping, Wang Mei, Li Kai-Xuan, Yang Jia-Jia, Yao Jia-Qi
School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China.
School of Earth Science and Resources, Chang'an University, Xi'an 710054, China.
Huan Jing Ke Xue. 2025 Jul 8;46(7):4522-4533. doi: 10.13227/j.hjkx.202406111.
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（RSEI）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 RSEI 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 RSEI， 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.
陕北黄土高原位于核心区域，其生态环境敏感且脆弱，面临着水土流失、沙漠化和盐碱化等一系列生态挑战。因此，对生态环境质量的时空变化及驱动因素进行全面、定量的监测与分析，对于区域生态环境保护至关重要。基于谷歌地球引擎（GEE），并考虑生态特征，通过将综合盐碱化指数（CSI）和沙漠化差异指数（DDI）引入经典遥感生态指数（RSEI），构建了调整后的遥感生态指数（RSEI）。对2000年至2020年陕北黄土高原的生态环境质量进行了动态监测。利用空间自相关分析和地理探测器模型探讨了生态环境质量的空间分布特征及驱动因素。结果表明：①RSEI第一主成分（PC1）的贡献率普遍超过91%，可用于综合反映各生态因子特征，在区域生态环境质量评价中具有广泛应用。②在过去二十年中，区域生态环境质量有明显改善，呈现出先上升、后下降、再上升的趋势。2000年至2020年，极差和差质量区域的面积占比分别从72.83%降至33.41%，良好和优质量区域的占比从15.93%增至37.05%。③改善区域大于恶化区域，特别是在2000年至2005年期间，显著改善区域占比达31.37%。2010年至2015年，退化最为严重，占比为10.14%。改善区域集中在中部和东北部，而退化区域主要位于西部。④区域生态环境质量的空间分布具有强烈且紧密的正空间相关性。归一化植被指数（NDVI）优于其他生态因子，对RSEI的贡献最大，而在其他因子中，降水的影响最为显著。研究结果有望为区域生态环境质量的监测与保护提供基础信息和科学依据。
