Multi-scale analysis on eco-environmental quality patterns and associated driving factors: implications for the Henan section of the Yellow River Basin, China.

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Learn more: PMC Disclaimer | PMC Copyright Notice PeerJ . 2026 Apr 16;14:e21063. doi: 10.7717/peerj.21063 Search in PMC Search in PubMed View in NLM Catalog Add to search Multi-scale analysis on eco-environmental quality patterns and associated driving factors: implications for the Henan section of the Yellow River Basin, China Zhengtian Yin Zhengtian Yin 1 The Faculty of Architecture, The University of Hong Kong, Hong Kong, China 2 College of Landscape Architecture, Henan Agricultural University, Zhengzhou, China Find articles by Zhengtian Yin 1, 2, # , Huimin Geng Huimin Geng 2 College of Landscape Architecture, Henan Agricultural University, Zhengzhou, China Find articles by Huimin Geng 2, ✉, # , Wei Li Wei Li 3 Institute of Wetland Research, Chinese Academy of Forestry, Beijing, China Find articles by Wei Li 3 , Yakai Lei Yakai Lei 2 College of Landscape Architecture, Henan Agricultural University, Zhengzhou, China 4 The Key Laboratory of Blue-Green Space Patterns and Functions in Zhengzhou City, Henan Agricultural University, Zhengzhou, China 5 Henan Provincial Joint International Research Laboratory of Landscape Architecture, Henan Agricultural University, Zhengzhou, China Find articles by Yakai Lei 2, 4, 5 , Mingyao Hou Mingyao Hou 2 College of Landscape Architecture, Henan Agricultural University, Zhengzhou, China Find articles by Mingyao Hou 2 , Dan He Dan He 2 College of Landscape Architecture, Henan Agricultural University, Zhengzhou, China Find articles by Dan He 2 , Yu Gary Gao Yu Gary Gao 6 College of Food, Agricultural, and Environmental Sciences South Centers, Ohio State University, Piketon, United States of America Find articles by Yu Gary Gao 6 , Yang Cao Yang Cao 2 College of Landscape Architecture, Henan Agricultural University, Zhengzhou, China Find articles by Yang Cao 2, ✉ Editor: Huiting Wu Author information Article notes Copyright and License information 1 The Faculty of Architecture, The University of Hong Kong, Hong Kong, China 2 College of Landscape Architecture, Henan Agricultural University, Zhengzhou, China 3 Institute of Wetland Research, Chinese Academy of Forestry, Beijing, China 4 The Key Laboratory of Blue-Green Space Patterns and Functions in Zhengzhou City, Henan Agricultural University, Zhengzhou, China 5 Henan Provincial Joint International Research Laboratory of Landscape Architecture, Henan Agricultural University, Zhengzhou, China 6 College of Food, Agricultural, and Environmental Sciences South Centers, Ohio State University, Piketon, United States of America ✉ Corresponding author. # Contributed equally. Received 2025 Jul 22; Accepted 2026 Feb 20; Collection date 2026. ©2026 Yin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits using, remixing, and building upon the work non-commercially, as long as it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. PMC Copyright notice PMCID: PMC13092233  PMID: 42011363 Abstract Analyzing the spatiotemporal patterns and evolutionary dynamics within the ecological environment is vital for the sustainability of the Yellow River Basin. However, previous studies have not adequately addressed the scale effects on ecological environment patterns, which hampers the effective implementation of regional management strategies. This study aims to investigate the dynamic changes in eco-environmental quality within the Yellow River Basin of Henan Province since 1990, identifying patterns of variation in eco-environmental quality and its driving mechanisms across different spatial scales. Specifically, the Remote Sensing Ecological Index (RSEI) and Moran’s index were employed to quantify the spatiotemporal evolution of eco-environmental quality from 1990 to 2021. Spearman’s correlation analysis and hierarchical partitioning analysis were then applied to reveal the driving mechanisms underlying these variations. The results indicate significant differences in the eco-environmental quality distribution patterns between the western and eastern parts of the Yellow River Basin (Henan section). From 1990 to 2021, the driving mechanisms of eco-environmental quality showed increasing scale dependency over time, with different driving factors exhibiting varying responses to scale changes. The outcomes of this study provide a scientific foundation for the sustainable development and ecological environment conservation of the Yellow River Basin, thereby providing guidance for policymakers to implement targeted conservation strategies. Keywords: Ecological environment quality, Remote sensing ecological index (RSEI), Scale effect, Yellow River Basin (Henan Section), Ecological governance policy Introduction Against the backdrop of global environmental change, rapid urbanization, and industrialization, regional ecosystems are facing the risk of imbalance ( Li, Li & Lu, 2023 ). A scientifically robust and effective analysis of eco-environmental quality can provide valuable insights for addressing sustainable development challenges. The Chinese government has long been committed to optimizing eco-environmental quality through effective management policies. Advances in research have demonstrated that scale and scale effects play a crucial role in shaping ecological patterns and processes ( Xiong et al., 2024 ). Therefore, multi-scale analysis of eco-environmental quality is of key importance for linking scientific understanding with management and policy decisions. It is worth noting that although quite a few studies have shown that the layout and process, spatial and temporal distribution, and mutual coupling of geo-ecological research objects are scale-dependent, the inherent complexity of scale-related issues continues to hinder in-depth investigation in many cases ( Wang et al., 2022a ; Wang et al., 2022b ). Underlying natural conditions coupled with intensifying human activities result in significant heterogeneity within the ecological environment. Previous studies have often insufficiently accounted for the spatial non-stationary of ecological processes, thereby limiting the practical applicability of their findings to ecological policy making. Consequently, a major research challenge lies in effectively integrating information from multiple perspectives and scales ( Wu et al., 2025 ). Furthermore, the inherent hierarchical complexity of ecosystems leads to distinct scale-dependent patterns of drivers across different geographical units. Conducting in-depth attribution analysis for specific study areas while developing generalization analytical frameworks remains a significant obstacle ( Wu et al., 2025 ). Supported by remote sensing technology, the assessment of eco-environmental quality has evolved from single-indicator characterization to comprehensive multi-indicator evaluation. Correspondingly, the temporal scope of research has shifted from single-year snapshots to extended time series, while the analysis of driving factors has also advanced to encompass their pathways, interactions, and spatial distributions ( Boori et al., 2021 ; Dong et al., 2023 ; Wu et al., 2014 ). Xu (2013a) proposed the remote sensing ecological index (RSEI). Its comprehensive evaluation based on remote sensing images and multiple indicators can directly and continuously reflect the ecological conditions of terrestrial areas. Currently, the RSEI has gained widespread global application and is well-suited for the purposes of this study ( Bhattacharjee et al., 2024 ; Firozjaei et al., 2021a ; Firozjaei et al., 2021b ; Halder & Bose, 2024 ). The Yellow River Basin is a vital ecological buffer zone and an economic belt in China, but it is also recognized as one of the most vulnerable regions in the world ( Wang, 2020 ). Although the Chinese government has implemented various ecological protection and restoration measures, such as enacting the Yellow River Protection Law, the coexistence of ecological degradation and restoration remains prevalent throughout the basin due to complex natural-human interactions ( Li et al., 2023 ). As a distinct terrestrial geographical unit, research on its eco-environmental quality can support the government in taking proactive interventions and scientifically managing local ecology, thereby promoting sustainable economic growth and regional development. To bridge these knowledge gaps, this study constructed a multi-level sequential grid scale system. Based on watershed natural boundaries, it systematically investigated the spatiotemporal dynamics of eco-environmental quality, with a specific focus on its driving mechanisms. The Henan section of the Yellow River Basin, characterized by significant physical geographical differentiation, was selected as the study area. The rapid urbanization in this region, which has led to dramatic changes in land use and landscape patterns, provided an ideal experimental condition for this research. To enable an effective long-term time series analysis of spatiotemporal dynamics, this study leveraged the Landsat dataset—a resource with a nearly 50-year archive. We comprehensively utilized remote sensing images with satisfactory data quality, setting the study period from 1990 to 2021. Through the integrated application of the RSEI and Moran’s index, we achieved a pattern analysis of eco-environmental quality. A key breakthrough of this research is the coupling of Spearman correlation analysis with a hierarchical partitioning algorithm, which precisely quantified the relative contributions of various driving factors at different spatial scales. This confirmed the scale-dependent nature of the driving mechanisms behind eco-environmental quality. Our main objectives were as follows: ① To explore the spatiotemporal distribution pattern of eco-environmental quality at multiple scales. ② To identify the driving mechanisms of eco-environmental quality at various spatial scales. ③ To provide valuable scientific bases for improving eco-environmental quality to support regional management. This study contributes to public understanding of scale effects in ecological processes, establishes a scientific basis for targeted ecological improvement and refined management, delivers decision support for ecological restoration and spatial planning in the Yellow River Basin, and provides a feasible pathway for embedding multi-scale mechanistic analysis results into the spatial planning system. Materials & Methods Study area The Yellow River Basin (Henan Section) is defined between 110° and 116° east longitude and 33° to 37° north latitude ( Fig. 1 ), covering an area of approximately thirty-six thousand square kilometers, as reported in the Yellow River Water Resources Bulletin 2021. Taking Taohuayu in Zhengzhou, Henan Province, as the dividing line, the study area encompasses both the middle and lower reaches of the Yellow River Basin, and the unique geographic location is confronted with the double challenges of serious soil erosion and low water flow ( Xiao et al., 2020 ). The elevation ranges from 19 to 2,372 m, exhibiting a descending trend from west to east and forming a sequential mountainous-hilly-plain landform in sequence ( Xiao et al., 2020 ). Land use within the basin exhibits a mosaic distribution of urban-rural settlements and cultivated areas in the east, with a continuous distribution of forests and grasslands in the west ( Niu et al., 2022 ). The area is identified as an agricultural zone, as well as a population center and a key economic and cultural belt within the Yellow River Basin ( Zhao & Yin, 2023 ). According to data from the National Bureau of Statistics of China (2023) , Henan Province recorded a total grain output of 244.01 million metric tons, accounting for 27.2% of the aggregate output from the nine provincial-level regions within the Yellow River Basin. Furthermore, Henan’s year-end permanent resident population stood at 98.15 million, and its gross regional product (GRP) reached CNY 6,062.77 billion, constituting 23.4% and 18.7% of the respective totals for the nine-region area. Figure 1. Study area. Open in a new tab (A) Elevation distribution of the Yellow River Basin (Henan Section); (B) land-use/land-cover of the Yellow River Basin (Henan Section) in 2021. Data sources and pre-processing (1) Remote sensing data The Pixel Information Expert (PIE)-Engine Studio, designed for spatiotemporal remote sensing cloud computation, enables efficient processing of large data sets through advanced scripting, providing data resources and computational capacity for terrestrial science research ( Fu et al., 2021 ). This study utilized the PIE-Engine platform to access the Landsat Collection 2 Level 2 (C2L2) product, including data from Landsat-5 TM (ID: LT05/02/SR) and Landsat-8 OLI (ID: LC08/02/SR). Disparities in band configurations, radiometric calibration, and atmospheric correction algorithms among different sensors may hinder direct data comparability. However, the internationally recognized processing workflow and algorithms of the Landsat Collection 2 Level 2 (C2L2) products provide long-term time series of surface reflectance and land surface temperature data generated through standardized processing, thereby establishing a benchmark for cross-temporal data comparison ( Crawford et al., 2023 ; Hemati et al., 2021 ; Wulder et al., 2022 ). A substantial body of literature employing multi-sensor Landsat data has successfully conducted long-term time-series assessments of eco-environmental quality, attesting to the viability and continuity of the C2L2 surface-reflectance products for such analyses ( Zhang et al., 2022 ; Geng et al., 2022 ; Zhou & Liu, 2022 ; Hemati et al., 2021 ). The selection process involved choosing remote sensing images with less than 10% cloud cover during the growing season (April-September) of the target year, as well as one year prior and one year subsequent ( Feng et al., 2022a ). Clouds and their shadows were removed using the pixel quality band (QA_PIXEL), and the median values of the images were extracted to produce a cloud-free median composite image. In cases of suboptimal image quality, images from the four adjacent years were combined to generate a synthesized composite. Specific information about the images used in this paper is shown in Table S1 . (2) Influencing factor data Eco-environmental quality is shaped by interactions among natural attributes, land use, and socio-economic attributes ( Chen, Chi & Li, 2020 ). Natural factors exert long-term direct effects on eco-environmental quality but are challenging to manage or regulate. High-intensity human activities can induce significant short-term impacts on ecosystems, ultimately shaping the long-term development of local ecological environment. Land use distribution is constrained by natural conditions while simultaneously reflecting the material and energy exchanges between human production activities and the natural environment. It is shaped by the dual influence of natural factors and human activities, serving as a dynamic indicator of ecosystem condition. The driving factors selected for this study are derived entirely from remote sensing data, leveraging its objectivity and authenticity. The driving factors of eco-environmental quality were categorized into three dimensions: physical-geographical factors, land utilization factors and socio-economic factors. This classification is grounded in the principles of sustainable development and is informed by previous research findings ( Feng et al., 2022b ). Considering data accessibility, twelve driving factors were initially identified ( Table S2 ). Among them, topography serves as a fundamental physical element, controlling the spatial distribution of human activities and landscapes. The Digital Elevation Model (DEM) influences eco-environmental quality by regulating latitudinal climatic conditions along vertical gradients, while Slope and the Relief Degree of Land Surface (RDLS) affect factors like soil erosion and soil type ( Liu et al., 2013 ). Temperature (TMP), precipitation (PRE), and the potential evapotranspiration (PET) are key climatic factors shaping eco-environmental quality through vegetation growth and atmospheric circulation ( Zhang, Jia & He, 2023 ). The percentage of ecological spaces area (Ecological P ) and the percentage of impervious surface area (Impervious P ) quantify ecological and living spaces in the Yellow River Basin (Henan section), reflecting land use and spatial development, while the Shannon’s Diversity Index (SHDI) measures regional land use patterns ( Li, Sun & Yu, 2020 ). Nighttime light (LIGHT), Population density (POP), and the distance from the urban built-up area (URABN D ) indicate the scale and intensity of human activities, capturing their impact on the ecological environment ( Geng et al., 2022 ). All data in this study were standardized to the Mercator coordinate system. To ensure the comprehensiveness of the driving factors and avoid potential correlations among indicators caused by strong multicollinearity, the variance inflation factor was utilized to test the data of each year ( Table S3 ). Ultimately, the retained driving factors are listed in Table 1 . Table 1. The classification and description of the independent variables. Variable category Variable Temporal characteristics Description Physical- geographical factors DEM (Elevation) Stable factor Elevation is a descriptor of topography and geomorphology, which influences the type, growth status and spatial distribution of vegetation ( Peng, Kuang & Tao, 2019 ). PRE (Total precipitation in the growing period) Non-stable factor Precipitation is a climatic indicator, relevant for vegetation growth, and associated with soil erosion and flooding ( Tian, Yin & Wang, 2023 ). PET (Total potential evapotranspiration in the growing period) Non-stable factor Potential evapotranspiration directly affects water resources and the ecological state of a region ( Chen, Yan & Lu, 2020 ). Land utilization factors SHDI (Landscape diversity index) Non-stable factor Shannon Diversity Index (SHDI) relates to land use patterns, with higher values indicating a diversity of land use types and increased fragmentation ( Wang et al., 2022a ; Wang et al., 2022b ). Ecological P (Percentage of ecological spaces area) Non-stable factor Ecological spaces including woodlands, grasslands, water bodies, and unused land, which is often distributed with native vegetation, and its ecological value is very important ( Liu & Jensen, 2018 ). Impervious P (Percentage of impervious surface area) Non-stable factor Impervious surfaces impact the composition and properties of the underlying surface, which have been proven to have negative ecological consequences ( Zhang et al., 2023 ). Socio- economic factors LIGHT (Nighttime light) Non-stable factor Nighttime illumination is used to assess economic activity ( Chen, Zeng & Guo, 2022 ). POP (Population density) Non-stable factor Population density serves as a measure of the intensity of human activity ( Tian, Yin & Wang, 2023 ). URBAN D (Distance from the urban built-up area) Non-stable factor The spatial distribution of urban built-up areas is important for sustainable economic development and effective environmental management ( Hu et al., 2022 ). Open in a new tab (3) Determination of analysis scale To better analyze and generalize spatial patterns of patches, it is often necessary to divide space into specific polygonal units ( Liu et al., 2023 ). The choice of partition size and shape can reveal different spatial patterns, relationships, and statistical correlations. To determine the optimal analytical scale, we conducted a systematic scale-effect analysis through preliminary experiments. Based on the 2021 RSEI, a grid sequence from 0.5 km to 10 km (20 levels at 0.5 km intervals) was constructed, with each level subjected to global spatial autocorrelation analysis. As shown in Table S4 , the results indicate that the global Moran’s I exhibit a unimodal trend with increasing scale, peaking at 2.5 km. This peak indicates the strongest spatial clustering of eco-environmental quality at this scale. Consequently, a 5 km analysis scale was selected to systematically capture the scale effects around this peak while encompassing representative scales for ecological processes. Simultaneously, the starting scale was similarly determined at 1 km based on the native resolution of the driving factors, thereby securing statistical reliability for a continuous, multi-scale analysis of the driving mechanisms ( Wen et al., 2022 ). The selection of a 1 km interval was guided by two principal considerations: First, this interval achieves a practical balance between computational cost and statistical robustness, while remaining sufficiently sensitive to identify key inflection points in the scale-dependent influence of driving factors. Second, the 1 km grid aligns with the typical spatial unit of China’s “15-minute life circle” policy, facilitating the translation of our findings into practical guidance for urban-rural spatial governance. In this study, grids covering more than 80% of the standard area were used to maintain the reliability of spatial statistics ( Wang et al., 2022a ; Wang et al., 2022b ). The scale effect analysis was calculated based on the mean value of all pixels contained within each grid unit. The analysis of scale effects is inherently a spatial aggregation process. Using the mean value of grid pixels proves more robust for interpreting long-term, macro-scale patterns and is relatively insensitive to short-term fluctuations at individual pixels. Consequently, this approach enhances the accuracy of long-term trend analysis in scale effects within our research findings. Methodology Our methodology encompassed four key steps: ① Obtaining the RSEI using the PIE-Engine Studio, and analyzing the spatiotemporal dynamics of eco-environmental quality from 1990 to 2021, within the study area. ② Evaluating the scale effects of eco-environmental quality using the Global Moran’s I index. ③ Analyzing the relationships between eco-environmental quality and various driving factors, including physical-geographical, land utilization, and socio-economic variables, to identify the predominant driving factors. ④ Investigating the scale-dependent effects on eco-environmental quality driving mechanisms. The detailed workflow is presented in Fig. 2 . Figure 2. Flow chart of the study. Open in a new tab (1) RSEI construction This investigation quantifies eco-environmental quality using the Remote Sensing Ecological Index (RSEI). The following steps were undertaken: •   Construction of indicator system; The RSEI index is constructed using principal component analysis, incorporating four key indicators: greenness (Normalized Difference Vegetation Index, NDVI), wetness (WET), heat (Land Surface Temperature, LST), and dryness (Normalized Differential Building-Soil Index, NDBSI). NDVI reflects vegetation coverage and vegetation type of the ecological environment ( Li, Liu & Fu, 2011 ); WET indicates the moisture conditions of the ecological environment ( Luo & Tao, 2017 ); LST denotes surface temperature, which is intricately linked to surface energy exchanges ( Zhou & Wang, 2020 ); NDBSI is calculated by averaging the Soil Index (SI) and the Index-based Built-up Index (IBI). SI is indicative of areas with scant vegetation, whereas IBI reflects land utilized for building structures ( Zong et al., 2024 ). The dryness metric is used to assess land desertification and degradation. The mathematical expressions for these indicators are detailed in Table 2 . Table 2. Formula and description for each remote-sensing index ( Tian, Yin & Wang, 2023 ; Xu, 2013b ; Yang et al., 2022 ). Index Formula Description NDVI NDVI = ( ρ NIR − ρ red )/( ρ NIR + ρ red ) ρ blue , ρ green , ρ red , ρ NIR , ρ sr 1 and ρ sr 2 are reflectance of the blue band, green band, red band, near-infrared band, short-wave infrared band 1, and band 2, respectively. ρ is the temporal band in the Landsat product; λ and α are 0.003 418 02 and 149, respectively. WET WET TM = 0.0315 ρ blue + 0.2021 ρ green + 0.3102 ρ red + 0.1594 ρ NIR − 0.6806 ρ sr 1 − 0.6109 ρ sr 2 WET OLI = 0.1511 ρ blue + 0.1973 ρ green + 0.3283 ρ red + 0.3407 ρ NIR − 0.7117 ρ sr 1 − 0.4559 ρ sr 2 LST LST = λρ + α − 273.15 NDBSI NDBSI = ( IBI + SI )/2 I B I = 2 ρ s r 1 ρ s r 1 + ρ N I R − ρ N I R ρ r e d + ρ N I R + ρ g r e e n ρ s r 1 + ρ g r e e n 2 ρ s r 1 ρ s r 1 + ρ N I R + ρ N I R ρ r e d + ρ N I R + ρ g r e e n ρ s r 1 + ρ g r e e n SI = [( ρ sr 1 + ρ red ) − ( ρ blue + ρ NIR )]/[( ρ sr 1 + ρ red ) + ( ρ blue + ρ NIR )] Open in a new tab Computations and data downloads for the sub-index layers were performed using the PIE-Engine platform. Following established methodologies ( Tian, Yin & Wang, 2023 ), we utilized the Modified Normalized Difference Water Index (MNDWI) to minimize the distorting effects of large water bodies on the WET index and to ensure the comprehensive comparability of the RSEI. We combined the water bodies from various years using the MNDWI, and applied it as a consistent mask across all layers. The specific MNDWI thresholds used for each year are provided in Table S5 . An 8-year interval was adopted for the temporal framework, determined through preliminary experimentation and constrained by the availability of cloud-free imagery. Consequently, assessments were conducted for the years 1990, 1998, 2006, 2014, and 2021, covering key turning points in the eco-environmental quality of the Henan section of the Yellow River Basin. •   Percentile de-noising and normalization; The weight of the results may be unbalanced due to the non-uniform dimensions of the different indicators. To improve the comparability of the RSEI across different time periods, a combined approach of full-time percentile-based noise reduction and spatial–temporal unified principal component analysis was implemented. This method enhances the objectivity and validity of the RSEI assessments. Sub-indices were normalized to ensure dimensional consistency. The formula is as follows: I n = I i − I m i n I m a x − I m i n (1) where I n is the value of a metric after normalization, I i is the original value of the indicator in pixel i , I min and I max are the minimum and maximum values of this indicator, respectively. To establish uniform minimum ( I min ) and maximum ( I max ) values across different regions and time periods, we calculated a multi-year average for each sub-index. We then identified the 0.5% and 99.5% percentiles of these average layers as the global minimum and maximum thresholds, respectively. Values falling below or above these thresholds are adjusted to the global minimum or maximum ( Luo, Ming & Xu, 2022 ). The percentile thresholds employed for all indicators are provided in Table S6 . •   Principal component analysis (PCA); Following the methodology described in the relevant literature, this study employed a unified temporal-spatial PCA method. A total of 25,000 random sample points (5,000 per year across the five-year study period) were selected. A covariance matrix was constructed using Python to derive eigenvalues and eigenvectors, selecting the vector associated with the largest eigenvalue as the definitive factor weight matrix. The mean value of the sample set was used to centralize all sub-index layers, and the primary principal component (PC1) was calculated for all pixels across all images based on the matrix. Values outside the 0.5% to 99.5% percentile range of PC1 were eliminated ( Table S7 ). Through these steps, each pixel’s RSEI was assigned a uniform application of weights, achieving spatial–temporal consistency ( He et al., 2021 ). •   Classification of eco-environmental quality; The distribution of multi-year RSEI data was analyzed to establish global minima (RSEI min ) and maxima (RSEI max ). The RSEI values were categorized into five predefined classes: “excellent”, “good”, “moderate”, “fair”, and “poor”, using the equal interval classification method. Comparisons of RSEI values across different periods were conducted to assess changes in eco-environmental quality, quantifying these changes on a defined scale from −4 to +4. (2) Spatial pattern delineation Global Moran’s I was used to analyze the overall spatial dependency within the dataset. Additionally, Local Indicators of Spatial Association (LISA) were employed to identify localized spatial association patterns. The formulas for these metrics are: Global Moran ′ s I = N Σ i Σ j w i j x i − μ x j − μ Σ i Σ j w i j Σ i x i − μ 2 (2) Local Moran ′ s I = x i − μ Σ i x i − μ 2 Σ j w i j x j − μ (3) where x i and x j are the RSEI values of grid i and grid j , respectively; µ is the mean value of RSEI; w ij is the spatial weight matrix that measures the spatial correlation between grid i and grid j . Global Moran ′ s I takes values within the range of [−1, 1]. Global Moran ′ s I < 0 indicates a negative spatial correlation, with smaller values representing greater spatial dispersion. Global Moran ′ s I = 0 indicates no spatial correlation. Global Moran ′ s I >  0 indicates a positive spatial correlation, with larger values representing stronger spatial agglomeration. LISA clustering identifies five types of spatial clustering patterns: clustering of high values (High-High, H–H type), high values surrounded by low values (High-Low, H–L type), low values surrounded by high values (Low-High, L–H type), clustering of low values (Low-Low, L–L type), and random distribution of the origin (non-significant). (3) Analysis of driving factors This study integrates the Spearman’s correlation analysis and hierarchical partitioning analysis to investigate the relationship between RSEI and its driving factors at various analytical scales. As a non-parametric method, Spearman’s correlation analysis does not assume a specific data distribution. It aims to identify monotonic relationships between variables ( Mohammadifar, Gholami & Golzari, 2023 ). Its robustness to extreme outliers makes it particularly suitable for analyzing the non-linear or irregularly distributed data typical of remote sensing statistics. Consequently, it was applied to examine the relationships between the RSEI and various driving factors across different scales, providing an initial screening for the presence of scale effects. Hierarchical partitioning emphasizes the relative importance of individual predictors (or groups of predictors) across all possible model subsets (including the full model). The relative importance of any individual predictor can be estimated as its unique contribution to the total model R 2 plus its average shared contributions with the other predictors ( Yao et al., 2023 ). We implemented hierarchical partitioning using the “rdacca.hp” package in R 4.5.2, obtaining the individual effects and relative contributions of the driving factors. The independent contribution of each variable was evaluated using the adjusted R 2 , with all calculations based on 999 permutations. It should be emphasized that the relative importance of individual predictors is an exploratory framework, not an inferential tool. Accordingly, the permutation test results are interpreted within this exploratory context ( Pecuchet et al., 2022 ). To facilitate comparison across study periods and analytical scales, we ranked all driving factors according to their relative contributions. Results Spatiotemporal patterns of eco-environmental quality in the study area The results of the PCA revealed that PC1 contributed the most among the principal components, accounting for 75.40%, and integrated the characteristics of the four remote sensing indices to a greater extent. Specifically, the loadings values of greenness, wetness, heat, and dryness are 0.5223, 0.5881, −0.2141, and −0.5793, respectively ( Table S8 ). The load of greenness and wetness is positive, and the load of heat and dryness is negative, which is consistent with the actual situation that greenness and wetness have a positive effect on the ecological environment, and heat and dryness have a negative effect on the ecological environment. At the same time, the correlation analysis results show that the absolute values of the correlation coefficients between RSEI and the sub-indicators are all greater than 0.7 ( Table S9 ). Therefore, it can be concluded that the RSEI calculated in this study accurately represents the overall eco-environmental quality of the Yellow River Basin in Henan Province. Based on the spatiotemporal variations in the RSEI from 1990 to 2021, pronounced spatial disparities are manifest between the eastern and western sectors of the study area ( Fig. 3 , Table S10 ). Regions exhibiting superior RSEI are predominantly situated in the eastern plains, characterized by extensive agricultural cultivation, and the mountainous western territories adorned with forests and grasslands. Conversely, zones of inferior RSEI are chiefly located in the western undulating terrains and throughout all urbanized territories. It is salient to observe that from 2006 to 2021, the regions categorized with an excellent RSEI grade gradually increased, with the most pronounced increase observed in the eastern areas. Figure 3. Spatial distribution patterns of RSEI in the Yellow River Basin (Henan Section) from 1990 to 2021. Open in a new tab Based on the changes in RSEI presented in Fig. 4 and Table S11 , the RSEI demonstrates significant fluctuations from 1990 to 2021, with an overarching upward trend. During the interval from 1990 to 1998, RSEI in proximate riverine locales markedly enhanced, whereas the eastern agricultural zones and elevated western regions witnessed modest deterioration. From 2006 to 2014, a substantial augmentation in RSEI was observed across an expansive area. In the subsequent interval from 2014 to 2021, enhancements predominantly occurred adjacent to the main channel of the Yellow River and on the peripheries of urban areas, and the RSEI of rural eastern regions experienced a significant degradation. Figure 4. Change detection of RSEI in the Yellow River Basin (Henan Section) from 1990 to 2021. Open in a new tab Analysis of spatial agglomeration effects at multiple scales Global Moran’s I analysis indicated that the intensity of spatial clustering remained high within the 1–3 km scale range ( Tables 3 and 4 ). Specifically, the spatial clustering of RSEI peaked at the 1 km scale (in 1990, 1998, and 2006) and the 2 km scale (in 2014 and 2021), while the RSEI change peaked at the 2 km scale (during 1990–1998, 1998–2006, and 2006–2014) and the 3 km scale (during 2014–2021). Table 3. Moran’s index of RSEI at different scales. Year 1 km 2 km 3 km 4 km 5 km Moran’s I Z P Moran’s I Z P Moran’s I Z P Moran’s I Z P Moran’s I Z P 1990 0.876 388.858 0.000 0.862 187.887 0.000 0.837 119.325 0.000 0.805 84.639 0.000 0.780 64.677 0.000 1998 0.840 372.663 0.000 0.829 180.570 0.000 0.802 114.299 0.000 0.764 80.326 0.000 0.736 61.018 0.000 2006 0.859 381.122 0.000 0.853 185.928 0.000 0.829 118.244 0.000 0.796 83.654 0.000 0.761 63.142 0.000 2014 0.828 367.502 0.000 0.842 183.434 0.000 0.834 118.884 0.000 0.813 85.408 0.000 0.797 66.078 0.000 2021 0.818 362.988 0.000 0.849 184.949 0.000 0.846 120.655 0.000 0.831 87.285 0.000 0.814 67.554 0.000 Open in a new tab Table 4. Moran’s index of changes in the RSEI levels at different scales. Year 1 km 2 km 3 km 4 km 5 km Moran’s I Z P Moran’s I Z P Moran’s I Z P Moran’s I Z P Moran’s I Z P 1990–1998 0.773 342.999 0.000 0.799 174.11 0.000 0.798 113.751 0.000 0.78 82.038 0.000 0.766 63.583 0.000 1998–2006 0.753 333.973 0.000 0.765 166.681 0.000 0.755 107.693 0.000 0.736 77.398 0.000 0.708 58.754 0.000 2006–2014 0.774 343.308 0.000 0.781 170.13 0.000 0.778 110.974 0.000 0.758 79.654 0.000 0.733 60.834 0.000 2014–2021 0.660 292.993 0.000 0.688 149.842 0.000 0.692 98.710 0.000 0.678 71.301 0.000 0.687 57.034 0.000 Open in a new tab RSEI exhibits consistent spatial clustering patterns across diverse scales ( Fig. 5 , Table S12 ). Over time, L-L clusters tended to concentrate within urban areas, whereas H-H clusters prevail in the eastern cultivated territories and the elevated western regions of the study area. Nonetheless, the spatial clustering of changes in RSEI displays notable instability, as depicted in Fig. 6 and Table S13 . It is noteworthy that both the L-L clustering of RSEI and its changes are observable in urbanized regions. As the analytical scale is increased, areas of non-significant progressively encroach upon adjacent significant areas, engendering a gradual attenuation of this information. Figure 5. Agglomeration patterns of RSEI in the Yellow River Basin (Henan Section) from 1990 to 2021. Open in a new tab Figure 6. Agglomeration patterns of changes in the RSEI levels in the Yellow River Basin (Henan Section) from 1990 to 2021. Open in a new tab Relationships between eco-environmental quality and driving factors across multiple scales (1) Spearman correlation analysis As indicated in Table 5 and Table S14 , across the 1–5 km scale range, all driving factors exhibited statistically significant monotonic relationships with RSEI ( P < 0.05). The analysis shows that regions with higher elevations, greater precipitation, and lower potential evapotranspiration during the vegetative growth period tend to exhibit better RSEI. As the scale of analysis increases, the correlation between RSEI and physical-geographic determinants also strengthens, although the correlation with DEM initially shows a decline at the beginning of the study period. In terms of land utilization determinants, increased fragmentation and a higher density of impervious surfaces are associated with decreased RSEI. Therefore, appropriately increasing the proportion of ecological areas can effectively improve the RSEI within the grid unit, which was a trend observed throughout the study period. The influence of land utilization determinants on RSEI is more apparent at smaller scales. For socio-economic determinants, regions with higher economic development, greater human activity, and proximity to urban areas are associated with lower RSEI, and this correlation strengthens as the scale of study increases. Table 5. Spearman correlation coefficients between RSEI and influencing factors across five analysis scales. Analysis scale DEM PRE PET SHDI Ecological P Impervious P LIGHT POP URBAN D Scales Year 1 km 1990 0.066 0.534 −0.420 −0.169 0.266 −0.142 −0.180 −0.150 0.296 1998 0.102 0.252 −0.265 −0.303 0.252 −0.196 −0.150 −0.165 0.299 2006 0.151 0.147 −0.483 −0.306 0.397 −0.330 −0.299 −0.241 0.396 2014 0.115 0.053 −0.357 −0.392 0.312 −0.434 −0.412 −0.265 0.456 2021 0.230 0.369 −0.327 −0.407 0.432 −0.533 −0.450 −0.346 0.421 2 km 1990 0.059 0.563 −0.436 −0.117 0.226 −0.115 −0.186 −0.152 0.305 1998 0.099 0.267 −0.272 −0.263 0.215 −0.156 −0.154 −0.171 0.314 2006 0.145 0.146 −0.504 −0.266 0.360 −0.296 −0.312 −0.244 0.413 2014 0.123 0.057 −0.379 −0.371 0.278 −0.401 −0.442 −0.285 0.487 2021 0.240 0.394 −0.340 −0.383 0.393 −0.503 −0.480 −0.371 0.450 3 km 1990 0.055 0.580 −0.445 −0.095 0.213 −0.101 −0.193 −0.156 0.314 1998 0.097 0.276 −0.277 −0.247 0.198 −0.141 −0.157 −0.172 0.322 2006 0.145 0.146 −0.517 −0.250 0.341 −0.286 −0.321 −0.251 0.426 2014 0.125 0.058 −0.389 −0.374 0.251 −0.391 −0.459 −0.297 0.507 2021 0.245 0.404 −0.346 −0.380 0.365 −0.491 −0.497 −0.387 0.467 4 km 1990 0.052 0.591 −0.449 −0.090 0.205 −0.097 −0.194 −0.157 0.315 1998 0.092 0.281 −0.274 −0.250 0.182 −0.133 −0.154 −0.172 0.326 2006 0.138 0.143 −0.520 −0.254 0.323 −0.277 −0.321 −0.250 0.431 2014 0.128 0.061 −0.391 −0.388 0.238 −0.397 −0.469 −0.309 0.519 2021 0.247 0.409 −0.347 −0.388 0.353 −0.490 −0.506 −0.396 0.481 5 km 1990 0.058 * 0.608 −0.464 −0.081 0.209 −0.100 −0.209 −0.163 0.321 1998 0.097 0.298 −0.293 −0.246 0.189 −0.138 −0.167 −0.181 0.335 2006 0.147 0.157 −0.545 −0.243 0.325 −0.286 −0.336 −0.261 0.442 2014 0.134 0.069 −0.402 −0.382 0.235 −0.398 −0.478 −0.311 0.531 2021 0.260 0.424 −0.361 −0.380 0.354 −0.490 −0.513 −0.404 0.493 Open in a new tab Notes. * represent correlation coefficient significance at the 0.05 level, while unmarked values represent correlation coefficient significance at the 0.01 level. (2) Analysis of relative contributions of determinants As illustrated in Fig. 7 and Table S15 , the relative contributions of various determinants reveal that as time progresses, the variations in the relative importance of each driving factor become increasingly pronounced with the expansion of the analytical scale. During the period from 1990 to 2006, the ranking of the relative importance of these drivers exhibited minimal variation, with PET identified as the most influential factor influencing the RSEI of the study area. By the year 2014, alterations in the rankings of the driving factors were observed at spatial scales of 1 km and 2 km, with PRE emerging as the predominant factor. By 2021, a discernible shift in the rankings of the driving factors’ relative importance was noted across all examined scales, with Ecological P became the dominant factor. Specifically, in the years 2014 and 2021, the ranking of the relative importance of factors such as DEM, PET, LIGHT, and URBAN D escalated with the enlargement of the analytical scale. Conversely, the land use determinant Impervious P demonstrated significant importance at smaller scales, with its ranking decreasing as the research scale increased. Figure 7. The individual importance and relative rank of significant factors affecting eco-environmental quality. Open in a new tab Discussion Spatial and temporal patterns of eco-environmental quality at various spatial scales The spatiotemporal pattern of eco-environmental quality in the Yellow River Basin (Henan section) is characterized by macro-scale, cross-scale stability governed by underlying natural conditions, alongside micro-scale dependency driven by localized human activities. On one hand, the overall eco-environmental quality of the basin demonstrates a stable east–west differentiation pattern across analytical scales. This spatial stability is determined by inherent differences in ecosystems such as topography and climate, and is further reinforced by long-established human-land use patterns. This observation aligns with existing knowledge: vegetation-rich areas like cropland and forests contribute positively to regional RSEI ( Jing et al., 2025 ), where well-managed farmland can yield higher RSEI values than some densely vegetated areas ( Yuan et al., 2021 ); regions with severe soil and water erosion often exhibit lower RSEI ( Yuan et al., 2021 ); and construction land has a negative impact on the ecological environment ( Muñoz & Kravchenko, 2012 ). As a distinct geographical unit, the highly heterogeneous natural and anthropogenic mosaic within the basin has led to the gradual solidification of the existing spatial distribution of ecosystems. This indicates that any policy planning aimed at fundamentally improving the eco-environmental quality of the basin must respect and adapt to this macro-scale, cross-scale stability. On the other hand, the eco-environmental quality in localized areas exhibits fluctuations due to socioeconomic practices, resulting in scale-dependent variation. For instance, the improvement in eco-environmental quality brought by ecological restoration policies in the western riverside area of the study area, and the circular degradation pattern of eco-environmental quality caused by the urban expansion in the eastern part of the study area ( Zhang et al., 2023 ). These ecological process signals observed at fine scales weaken or even disappear as the analytical scale expands. This finding aligns with discoveries from other regions exhibiting high human–environment coupling intensity ( Blumstein & Thompson, 2015 ; Holt et al., 2015 ; Raudsepp-Hearne & Peterson, 2016 ; Shriner, Wilson & Flather, 2006 ), serving as direct evidence of strong scale sensitivity in the study of watershed ecological processes. It emphasizes the importance of appropriate analytical scales for studying specific ecological processes, and cautions that managers must be prudent when making conservation decisions based on information collected at a single scale ( O’Farrell et al., 2010 ). Furthermore, this study also found that the optimal clustering scale characterizing spatial autocorrelation showed an expanding trend during the study period, indicating that changes in the basin’s eco-environmental quality tend to propagate to larger scales, evolving into regional, continuous areal processes. This directly points to the dynamic nature of the ecological process scale itself, further underscoring the necessity of multi-scale analysis for designing multi-level governance system in basin ecosystems ( Anderson et al., 2009 ; Blumstein & Thompson, 2015 ). Driving forces of RSEI and its scale effects The driving mechanisms of eco-environmental quality in the Yellow River Basin of Henan section exhibit complex spatiotemporal cross-scale dynamics. Analyzing this multi-scale driving framework extends beyond traditional single-scale attribution analysis and provides a critical scientific basis for differentiated and precise management of the basin’s ecological environment. Among the physical-geographical factors, the natural background factors centered on terrain and climate play a dominant role in shaping the eco-environmental quality of the basin. This influence stems from their inherent constraints on human activity patterns and the restructuring of ecosystem distributions driven by the redistribution of hydrothermal resources ( Liao, Wu & Zhang, 2023 ). This dominance is evidenced by the strong correlation between the RSEI and physical-geographical factors across all analytical scales, which is further reflected in the stable spatial pattern of eco-environmental quality distribution. In essence, natural conditions function as intrinsic spatial planning elements, exerting a long-term locking and framing effect on the spatial distribution of eco-environmental quality in the Yellow River Basin (Henan Section). Regarding land utilization factors, intensified natural resource utilization and development pressure resulting from population growth and migration within the basin during the study period led to significant cross-scale variations in land use, particularly in the latter phase of the research ( Zhang et al., 2023 ; Zhang et al., 2020 ; Pickett et al., 2017 ). Unlike other studies that directly link land use types to eco-environmental quality, our multi-scale analysis reveals that the relative importance of land utilization factors exhibits distinct local characteristics: it gradually increases over time but diminishes as the spatial scale expands. Previous research suggests that when the spatial extent of land use surpasses a certain critical threshold, it exerts compound effects on eco-environmental quality ( Muñoz & Kravchenko, 2012 ). At the same time, analyses conducted at larger scales encompass more diverse ecosystems and socioeconomic activities, thereby diluting the land use signal within broader natural geographical patterns ( Anderson et al., 2009 ). This is validated by the multi-scale analytical results for land use factors, particularly the Impervious P factor. These findings emphasize that when managing land use to optimize the quality of the ecological environment, the issue of scale must be taken into account. Avoiding mismatches in management scale is crucial to prevent policy failure. Among socio-economic factors, compared to the indirect control exerted by physical-geographical factors and the direct impact of land utilization factors, the driving effect is relatively weaker. However, their influence shows a trend of gradual intensification over time and synergistic amplification with the expansion of the analytical scale, making scale-dependent variability increasingly pronounced. According to Krugman’s first geographical nature theory, natural endowments have a locking effect on human activities, which strengthens with an increase in scale ( Hu et al., 2020 ). This mechanism underlies the synergistic control of eco-environmental quality patterns by both physical-geographical and socio-economic factors at large scales in the Yellow River Basin of Henan section. However, existing studies indicate that the positive indirect effects generated through ecological restoration and environmental investment can offset the negative impacts of socioeconomic activities, an effect more evident at larger scales ( Runting et al., 2017 ; Hasan et al., 2025 ). Therefore, strategically leveraging the scale effects of socio-economic factors in basin planning can maximize developmental benefits within natural constraints. Management suggestions, research deficiencies and prospects (1) Policy implications By utilizing driving factors that exhibit different driving effects at different scales to regulate the quality of the ecological environment and adjust the spatial scale of policy implementation, efficient management of the ecological environment can be achieved. The high-quality development of the Yellow River Basin requires an approach that focuses on hierarchical management of eco-environmental quality. Strategies for hierarchical management should incorporate the following dimensions: ① The cross-regional collaborative development strategy for the Yellow River Basin (Henan section) under the background of high-quality development of the Yellow River Basin should be vigilant against the increasing influence of socio-economic factors on eco-environmental quality as the scale enlarges. It should reasonably avoid potential pressures from concentrated ecological risks. Efforts should focus on implementing cross-regional ecological environment governance and ecological function linkage projects to promote the coordinated development of social economy and ecosystem across the broader region. ② Managers involved in the regional development strategy for the Yellow River Basin (Henan section) must acknowledge the uneven distribution of ecological foundations, and respect the consistently high relative importance of physical-geographical factors in determining eco-environmental quality, along with their intensifying influence at larger scales. Their stable regulatory function should be strategically utilized to inform differentiated practical pathways. In the western high-quality mountainous regions, efforts should persist on constructing the Qinling Mountain ecological barrier. Implementing strict conservation measures and refining ecological compensation mechanisms to stabilize the agglomeration of high-value ecological zones and maximize ecological benefits. In the central-western hilly regions with lower eco-environmental quality, priorities should include implementing long-term soil and water conservation projects and guiding the establishment of ecological agriculture and forestry systems to mitigate pressures from the concentration of low-value areas. In the eastern high-quality plains, pathways for sustainable development that harmonize human and land use relationships should be explored. The ecological benefits of large-scale farmland and construct artificial ecological networks should be unlocked to optimize the ecological landscape. ③ Community-level initiatives for urban and rural construction in the Yellow River Basin (Henan section) must account for the chain reactions triggered by land use attribute changes. It is crucial to recognize the growing relative importance of land use factors in shaping eco-environmental quality, as well as their pronounced influence at fine scales. Strict control must be enforced over the conversion of ecological land to non-ecological uses, and standards for constructing ecological spaces within community life-circle planning should be refined. Simultaneously, green infrastructure such as ecological roofs, permeable pavement, and urban greenways should be incorporated. The mixed ecological benefits of land should be developed to enhance the ecological service efficiency per unit area within resource constraints. This approach will help resolve the current challenge of low-value eco-environmental quality regions clustering in urban and rural communities. (2) Research limitations Compared to short-term studies, long-term research maintains greater robustness against the bias effects of short-term temporary factors ( Nusa et al., 2025 ). The RSEI index employed in this study has gained widespread application in the field of eco-environmental quality research. However, its complete reliance on remote sensing imagery for calculation inevitably introduces limitations. Firstly, to ensure a long-term time series, we utilized both Landsat-5 and Landsat-8 datasets. Although these Landsat Collection 2 Level-2 products have undergone official correction and are considered analysis-ready. During the index calculation process, we applied distinct formulas for different sensors and performed percentile normalization on the calculated metrics. However, the global-scale uniform adjustment across sensors inevitably introduces data deviations in RSEI calculations at the local level. This results in greater variability in non-vegetated areas compared to vegetated areas within this study. Furthermore, differences between sensors, such as the potentially stronger ability of Landsat-8 to capture spatial heterogeneity compared to Landsat-5, may introduce inconsistencies. Moreover, the acquisition of high-quality, extensive-coverage remote sensing imagery presents considerable challenges. While the multi-year composite imagery effectively mitigates cloud contamination, it may also smooth out short-term, intense fluctuations in the ecological environment. Therefore, the findings of this study are better suited to reflecting the long-term overall spatiotemporal trends of eco-environmental quality in the Yellow River Basin of Henan Province, and should be interpreted with caution when examining short-term, local-scale dynamics. (3) Future prospects This study has established a multi-scale analytical framework to examine the relationship between eco-environmental quality and its driving factors within the Yellow River Basin (Henan Section), incorporating an analysis of the scale-dependent characteristics of the driving mechanisms. However, the current research does not explicitly define the pathways of nonlinear interactions among multiple factors, nor does it fully explore the spatial heterogeneity characterizing the effects of key drivers. Future research can build upon this multi-scale framework by employing spatial analysis tools such as Geodetector and Geographically Weighted Regression (GWR) models. By focusing on the coupling mechanisms of multiple driving pathways and the differential intensity of dominant factors across distinct analytical units, it can integrate global patterns with local characteristics. This approach will effectively unravel the complex relational network among driving factors, thereby providing a robust theoretical foundation for formulating differentiated ecological management strategies in the Yellow River Basin. Conclusions Given the differences in the spatiotemporal distribution of eco-environmental quality and its driving mechanisms across scales, conducting multi-scale analysis is crucial for gaining deeper insights into eco-environmental quality. This study established a multi-level continuous grid scale system, conducting a robust case study on the scale effects of eco-environmental quality and its driving mechanisms based on the natural watershed boundaries of the Yellow River Basin (Henan section). Specifically, the RSEI index and spatial autocorrelation analysis were used to delineate the multi-scale spatiotemporal distribution characteristics of eco-environmental quality in the Henan section of the Yellow River Basin, while Spearman’s correlation analysis and hierarchical partitioning algorithm were applied to analyze the driving mechanisms of geographic information elements on eco-environmental quality across scales. This study confirms the scale effects of eco-environmental quality and its driving mechanisms, offering a scientific basis for ecological protection, restoration, and spatial planning in the Yellow River Basin. The main conclusions are as follows: ① Spatiotemporal patterns and scale effects of eco-environmental quality: The eco-environmental quality in the Henan section of the Yellow River Basin exhibits a spatially heterogeneous pattern, characterized by a fundamental structure of “high value areas in the western mountainous region–low value areas in the central-western hilly region–high value areas in the eastern plains”. The optimal clustering scale for its spatial distribution and dynamic changes has progressively increased. ② Quantitative analysis of multi-scale driving mechanisms: The driving mechanisms of eco-environmental quality in the Henan section of the Yellow River Basin demonstrate scale dependency, with this effect intensifying annually. Driving factors of eco-environmental quality exhibit differentiated responses to scale variations: physical-geographical factors dominate the baseline pattern of eco-environmental quality at macro scales, land utilization factors play a critical role at fine scales, and the influence of socio-economic factors strengthens as the scale expands. ③ Scientific policy implications and hierarchical governance strategies: High-quality development in the Henan section of the Yellow River Basin requires establishing an implementation pathway centered on hierarchical management of eco-environmental quality. Macro-level development must guard against the spatial concentration of potential ecological risks. The coordinated development of various regions should adopt differentiated implementation pathways aligned with the natural geographic patterns. Current urban-rural community unit construction must enforce strict oversight of land use practices. With the in-depth implementation of “the ecological protection and high-quality development of the Yellow River basin” of China, achieving effective protection of the basin under the long-term coexistence of human-nature contradictions has become particularly crucial. This study contributes to expanding the cognitive framework of eco-environmental quality, informing the improvement of ecological protection and restoration policies in the Yellow River Basin (Henan section), and supporting the sustainable development of regional ecosystems. Future in-depth research can be based on the multi-scale analytical framework to integrate the global coupling pathways of driving mechanisms with local spatial characteristics, thereby further revealing the complex relationship network between the driving factors and facilitating the effective incorporation of multi-scale mechanistic analysis into spatial planning. Supplemental Information Supplemental Information 1. Supplementary Tables. Table S1 Basic information of Landsat data sources; Table S2 Data sources for variables used to study the influencing factors of eco-environmental quality; Table S3 Test results of variance inflation factor; Table S4 Moran’s index of RSEI in 2021 under different scales; Table S5 Water body extraction threshold based on MNDWI; Table S6 Percentile normalization threshold of sub-indicators; Table S7 Percentile normalization threshold of PC1; Table S8 Principal component analysis results; Table S9 Correlation coefficient r between between RSEI and each index from 1990 to 2021; Table S10 The graduation statistics of RSEI from 1990 to 2021; Table S11 Change detection of RSEI level from 1990 to 2021; Table S12 LISA Cluster Statistics of RSEI from 1990 to 2021; Table S13 LISA Cluster Statistics of RSEI changes from 1990 to 2021; Table S14 Spearman correlation coefficients between RSEI and influencing factors across five analysis scales (95 % confidence interval); Table S15 Hierarchical partitioning analysis between RSEI and influencing factors across five analysis scales. peerj-14-21063-s001.xlsx (62.8KB, xlsx) DOI: 10.7717/peerj.21063/supp-1 Supplemental Information 2. RSEI Calculation Code. The code can be executed on the PIE-Engine platform ( https://engine.piesat.cn/engine/home ). The ROI file can be used to upload the vector data of the study area to the PIE-Engine platform, which is used to run the RSEI calculation code. The data can be opened using the ArcGIS software. peerj-14-21063-s002.zip (62.1KB, zip) DOI: 10.7717/peerj.21063/supp-2 Acknowledgments We thank the editor and reviewers for their constructive comments which have greatly helped improve the quality of our manuscript. At the same time, we are also grateful to the Henan Provincial Joint International Research Laboratory of Landscape Architecture, for their infinite help. Funding Statement This research was supported by the 2023 Henan Province Federation of Social Science Research Project (SKL-2023-2277); 2023 Henan Province Key R&D and Promotion Special (Soft Science Research) Project (232400410293); 2023 General Project of Humanities and Social Sciences Research in Henan Universities (2023-ZDJH-534); Henan Province International Cooperation Research Project (HNGD2021035); 2021 Research and Practice Project of Higher Education Teaching Reform in Henan Province (2021SJGLX162Y); 2020 Henan Province Higher Education Institutions Young Backbone Teachers Funding Project (2020GGJS049). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Contributor Information Huimin Geng, Email: [email protected]. Yang Cao, Email: [email protected]. Additional Information and Declarations Competing Interests The authors declare there are no competing interests. Author Contributions Zhengtian Yin conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft. Huimin Geng conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft. Wei Li conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft. Yakai Lei conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft. Mingyao Hou performed the experiments, authored or reviewed drafts of the article, and approved the final draft. Dan He conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft. 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Table S1 Basic information of Landsat data sources; Table S2 Data sources for variables used to study the influencing factors of eco-environmental quality; Table S3 Test results of variance inflation factor; Table S4 Moran’s index of RSEI in 2021 under different scales; Table S5 Water body extraction threshold based on MNDWI; Table S6 Percentile normalization threshold of sub-indicators; Table S7 Percentile normalization threshold of PC1; Table S8 Principal component analysis results; Table S9 Correlation coefficient r between between RSEI and each index from 1990 to 2021; Table S10 The graduation statistics of RSEI from 1990 to 2021; Table S11 Change detection of RSEI level from 1990 to 2021; Table S12 LISA Cluster Statistics of RSEI from 1990 to 2021; Table S13 LISA Cluster Statistics of RSEI changes from 1990 to 2021; Table S14 Spearman correlation coefficients between RSEI and influencing factors across five analysis scales (95 % confidence interval); Table S15 Hierarchical partitioning analysis between RSEI and influencing factors across five analysis scales. peerj-14-21063-s001.xlsx (62.8KB, xlsx) DOI: 10.7717/peerj.21063/supp-1 Supplemental Information 2. RSEI Calculation Code. The code can be executed on the PIE-Engine platform ( https://engine.piesat.cn/engine/home ). The ROI file can be used to upload the vector data of the study area to the PIE-Engine platform, which is used to run the RSEI calculation code. The data can be opened using the ArcGIS software. peerj-14-21063-s002.zip (62.1KB, zip) DOI: 10.7717/peerj.21063/supp-2 Data Availability Statement The following information was supplied regarding data availability: The ROI date and RSEI Calculation Code are available in the Supplemental Files . 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