[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2535":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"view_count":15,"doi":24,"paper":25,"created_at":48},2535,"Development of an ecological greenness index (EGI) for urban environmental monitoring: a remote sensing and multivariate analytical approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-70643-x","Abstract Several remote sensing-based indices have been proposed. While most measures are disjointed, several integrated ecological indices are offered. The purpose of this study is to introduce an Ecological Greenness Index (EGI) as a composite and integrated ecological index. This index was established by combining eight remote sensing-derived indices, including NDVI, NDSI, NDBI, LST, LAI, and three Tasseled Cap indices. Principal Component Analysis (PCA) was used to reduce dimensionality among RS-based derived components. The PCA eigenvalues were used as weights in the fuzzy logic method. The fuzzy logic overlay method was used to combine the retained components and produce the final EGI map. The results showed that four principal components (SIPC1–SIPC4), which explained about 99.99% of the total variance, were used for the final EGI construction. The EGI was implemented and internally assessed in Tehran, Iran, as a case study. The eigenvalue (λ) of each retained SIPC was used to derive the component weights for EGI construction. The results revealed substantial regional heterogeneity: the mean quantity of EGI in Tehran’s northern area was 0.29 (e.g., District 1), while it reduced to 0.076 in southern districts (e.g., District 10). A north–south transect analysis confirmed a steep ecological gradient across the city. EGI integrated vegetation structure, soil exposure, moisture, thermal conditions, and built-up characteristics within a unified PCA–fuzzy framework. It provides a broader diagnostic representation of urban ecological greenness. Zonal statistics depicted the spatial variation of EGI across Tehran’s 22 administrative districts. Despite the limitations of this study, such as using single-season and 30 m resolution satellite data, the EGI provides a quantitative framework for monitoring urban ecology. It represents a potentially useful tool for cities facing rapid urbanization and ecological greenness changes. Based on this case study, the EGI framework has the potential to be used as an integrated environmental quality assessment tool. The EGI integrating multiple ecological dimensions that may complement established indices such as the Remote Sensing-based Ecological Index (RSEI) in complex urban environments. Generalization of the EGI requires independent validation across additional urban contexts.","摘要 目前已提出多种基于遥感的指数。尽管大多数指标彼此独立，但也出现了一些综合生态指数。本研究的目的是引入生态绿度指数（EGI）作为一种复合综合生态指数。该指数通过整合八个遥感衍生指数建立，包括NDVI、NDSI、NDBI、LST、LAI以及三个缨帽变换指数。采用主成分分析（PCA）对遥感衍生组分进行降维。将PCA特征值作为模糊逻辑方法中的权重。利用模糊逻辑叠加方法将保留的主成分进行组合，生成最终的EGI图。结果表明，四个主成分（SIPC1–SIPC4）解释了约99.99%的总方差，被用于最终EGI的构建。EGI在伊朗德黑兰作为案例研究进行了实施和内部评估。每个保留的SIPC的特征值（λ）被用于推导EGI构建的组分权重。结果揭示了显著的区域异质性：德黑兰北部地区（如第1区）EGI的均值为0.29，而南部地区（如第10区）降至0.076。南北向样带分析证实了城市范围内存在陡峭的生态梯度。EGI在统一的PCA–模糊框架内整合了植被结构、土壤暴露、湿度、热力条件和建成区特征。它为城市生态绿度提供了更广泛的诊断性表征。分区统计描绘了德黑兰22个行政区的EGI空间变化。尽管本研究存在局限性，如使用单季节和30 m分辨率卫星数据，但EGI为监测城市生态提供了定量框架。它代表了应对快速城市化和生态绿度变化城市的一个潜在有用工具。基于本案例研究，EGI框架有潜力作为综合环境质量评估工具使用。EGI整合了多个生态维度，可在复杂城市环境中补充遥感生态指数（RSEI）等已有指数。EGI的推广需要在更多城市背景下进行独立验证。",null,"Scientific Reports","2026-09-13T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦城市生态绿度遥感监测，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"遥感监测","城市生态","生态指数","10.1038\u002Fs41598-026-70643-x",{"doi":24,"openalex_id":26,"authors":27,"venue":10,"cited_by_count":15,"oa_url":40,"card":41,"direction":45,"ingested_from":47},"W7212454298",[28,31,34,36,38],{"name":29,"orcid":30},"Amir Houshang Ehsani","https:\u002F\u002Forcid.org\u002F0000-0002-5483-1163",{"name":32,"orcid":33},"Hassan Darabi","https:\u002F\u002Forcid.org\u002F0000-0002-9353-4639",{"name":35,"orcid":9},"Shakila Abedi",{"name":37,"orcid":9},"Seydeh Roya khamooshian",{"name":39,"orcid":9},"Faezeh pouyanejad","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-70643-x_reference.pdf",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"构建融合八个遥感指标的生态绿度指数EGI，并在德黑兰验证其城市生态监测能力。","整合NDVI、NDSI、NDBI、LST、LAI等八指标，用PCA降维加权与模糊","四个主成分解释99.99%方差，EGI呈显著南北梯度，北部0.29、南部0.076。","农业遥感与作物表型","可将EGI框架迁移至农业区，结合多季与高分辨率数据验证其跨区域泛化性。","openalex","2026-09-15T23:30:21.213654Z"]