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Assessing eco-environmental quality and its drivers in the Shandong section of the Yellow River Basin with an improved remote sensing ecological index


Abstract

The Shandong section of the Yellow River Basin (SDYRB), a critical zone for ecological security in the lower reaches of the Yellow River, faces multiple ecological challenges including salinization, soil erosion, water scarcity, and anthropogenic pollution. These issues significantly hinder regional sustainable development. To assess eco-environmental quality in the SDYRB accurately, an Improved Remote Sensing Ecological Index (IRSEI) was developed by integrating the Composite Salinity Index (CSI) and Soil–Water Conservation Function Index (SWCFI). Utilizing multi-temporal imagery (2009–2023), this study analyzed spatio-temporal patterns of eco-environmental quality and their driving mechanisms. The results show that: (1) The overall eco-environmental quality exhibits a declining trend, with a spatial distribution pattern characterized as “superior in the west and poorer in the east”. High-quality areas were concentrated in western plains and Yellow River riparian zones, versus low-quality areas in eastern/northern coasts. (2) The global Moran’s I approached 1 and exhibited a gradual year-by-year decline, indicating persistent spatial agglomeration of ecological quality. Local spatial autocorrelation was predominantly characterized by High-High (H–H) and Low-Low (L–L) agglomerations, with low-value areas exhibiting an outward spread tendency. (3) Ecological quality fluctuated, declining significantly (2009–2014) before recovering (2019–2023). Degradation hotspots were identified in the northeast and southwest, whereas the improved areas were concentrated in the central region. (4) Ordinary Least Squares (OLS) regression and GeoDetector (GD) identified synergistic natural and anthropogenic driving factors: mean annual temperature, evapotranspiration, nighttime light intensity, and land use were dominant. This study improves the applicability and interpretability of IRSEI in salinized and soil-eroded regions by integrating CSI and SWCFI, offering a scientific foundation for ecological conservation and high-quality development in the SDYRB. The approach can also be extended to dynamic monitoring and evaluation of other similarly vulnerable ecological zones.

Data availability

The authors confirm that the data supporting the findings of this study are available within the article.

References

  1. Shi, H. et al. Spatio-temporal pattern of urban-rural income gap in the Yellow River Basin and its response to urbanization. Arid Land Geogr. 47, 1781–1793 (2024).

    Google Scholar 

  2. Zhang, C.; Chen, Y. Research on the protection and development of historical and cultural resources in the Yellow River Basin from the perspective of ecological civilization. 3C TIC. Cuadernos de desar rollo aplicados a las TIC 2023, 2, 156–171.

  3. Li, D., Xu, G., Tang, J., Chao, Xu. & Xia, R. Countermeasures of the Eco-environment Governance in the Lower Reach of the Yellow River. Environ. Prot. 51, 23–26 (2024).

    Google Scholar 

  4. Jiang, C., Zhang, H., Wang, X., Feng, Y. & Labzovskii, L. Challenging the land degradation in China’s Loess Plateau: Benefits, limitations, sustainability, and adaptive strategies of soil and water conservation. Ecol. Eng. 127, 135–150 (2019).

    Google Scholar 

  5. Fu, B. Ecological and environmental effects of land-use changes in the Loess Plateau of China (in Chinese). Chin. Sci. Bull. 67, 3768–3779 (2022).

    Google Scholar 

  6. Han, X., Gao, Q. & Yuan, B. Analysis and identification of pollution sources of comprehensive river water quality: Evidence from two river basins in China. Ecol. Indic. 135, 108561 (2022).

    Google Scholar 

  7. Chen, C.; Liu, R. On regional collaborative legislation in the governance of the Yellow River Basin. Proceedings of the 2021 National Symposium on Environmental and Resource Law, Taiyuan, China, 15–17 October 2021.

  8. Outline of the Ecological Protection and High-Quality Development Plan for the Yellow River Basin. Available online: http://www.gov.cn/zhengce/2021-10/08/content_5641438.htm (accessed on 8 October 2021).

  9. Ma, H., Lu, N., Jiang, Y., Huang, Q. & Wu, F. Study on green farmland construction and agricultural high quality development model in the Yellow River Basin of Shandong Province. Agric. Compr. Dev. China 06, 16–21 (2023).

    Google Scholar 

  10. Zhang, C. & Wang, G. Thoughts on ecological protection and high quality development of the Yellow River Basin. Yellow River 46, 1–7 (2024).

    Google Scholar 

  11. Nagendra, H. et al. Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecol. Indic. 33, 45–59 (2013).

    Google Scholar 

  12. Hu, X. & Xu, H. A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: A case from Fuzhou City. China. Ecol. Indic. 89, 11–21 (2018).

    Google Scholar 

  13. Li, S., Li, C. & Kang, X. Development status and future prospects of multi-source remote sensing image fusion. Natl. Remote Sens. Bull. 25, 148–166 (2021).

    Google Scholar 

  14. Zhang, Y. et al. Opportunities and challenges in remote sensing applications to ecosystem ecology. Chin. J. Ecol. 36, 809–823 (2017).

    Google Scholar 

  15. Rumora, L., Majić, I., Miler, M. & Medak, D. Spatial video remote sensing for urban vegetation mapping using vegetation indices. Urban Ecosystems. Urban Ecosyst. 24, 1–13 (2020).

    Google Scholar 

  16. Xu, H. A study on information extraction of water body with the modified normalized difference water index (MNDWI). Natl. Remote Sens. Bull. 9, 589–595 (2005).

    Google Scholar 

  17. Xu, H. A remote sensing index for assessment of regional ecological changes. China Environ. Sci. 33, 889–897 (2013).

    Google Scholar 

  18. Yan, Z. Assessment of ecological environment quality in Jiawang district based on remote sensing ecological index. Master’s Thesis, China University of Mining and Technology, Xuzhou, China, May 2023.

  19. Li, R. Study on the temporal and spatial changes of the ecological environment quality of the Shendong mining area in Shanxi, Shaanxi, and Inner Mongolia. Master’s Thesis, Inner Mongolia Agricultural University, Hohhot, China, June 2021.

  20. Zhang, Q., Wang, W. & Liu, J. Remote sensing monitoring and change analysis of ecological environment in Gulang County. Trop. Geomorphol. 42, 1–8 (2021).

    Google Scholar 

  21. Li, H., Huang, J., Liang, Y., Wang, H. & Zhang, Y. Evaluating the quality of ecological environment in Wuhan based on remote sensing ecological index. J. Yunnan Univ. Nat. Sci. Ed. 42, 81–90 (2020).

    Google Scholar 

  22. Kaixuan, Y. et al. Spatiotemporal changes of eco-environmental quality based on remote sensing-based ecological index in the Hotan Oasis, Xinjiang. J. Arid Land 03, 262–283 (2022).

    Google Scholar 

  23. Yang, X., Shi, Y., Feng, S., Wang, C. & Chai, J. Major ecological environment problems and countermeasures in the downstream of the Yellow River (Shandong Section). Shandong Land Resour. 26(01), 15–18 (2010).

    Google Scholar 

  24. Pang, Q. et al. Problems and Countermeasures of soil and water loss control in the Loess Plateau in the new period. Yellow River 44(S1), 73–74 (2022).

    Google Scholar 

  25. Dong, X. et al. Spatiotemporal evolution and influencing mechanism of urbanization and ecological environmental quality between 2000 and 2020 in Henan Province, China. Remote Sens. Appl. Soc. Environ. 37, 101492 (2025).

    Google Scholar 

  26. Xin, J., Yang, J., Yu, H., Ren, J. & Yu, W. Towards ecological civilization: Spatiotemporal heterogeneity and drivers of ecological quality transitions in China (2001–2020). Appl. Geogr. 173, 103439 (2024).

    Google Scholar 

  27. Liu, Q., Cai, J., Xie, M., Tian, J. & He, W. Assessment of ecosystem service value of national wetland parks along the Shandong section of the Yellow River. Wetl. Sci. 22, 697–706 (2024).

    Google Scholar 

  28. Zhou, C., Zhao, Y., Ren, M., Jin, Y. & Lu, S. Spatiotemporal differentiation, center of gravity evolution and driving factors of national wetland parks in the Yellow River Basin. Arid Land Geogr. 47, 506–514 (2024).

    Google Scholar 

  29. Zhang, Y. Effects of oil pollution and Spartina alterniflora invasion on Phragmites australis communities in the Yellow River Delta. Master’s Thesis, Shandong University, Qingdao, China, June 2023.

  30. Luo, P., Zhu, W. & Wang, S. Consideration on comprehensive legislation of high-quality development in the Yellow River Basin. People Yellow River 45, 14–18 (2023).

    Google Scholar 

  31. Lu, D. & Sun, D. Comprehensive Management and Sustainable Development of the Yellow River Basin. Acta Geogr. Sin. 74, 2431–2436 (2019).

    Google Scholar 

  32. Li, Y., Wang, Y. & Song, K. Analysis of spatiotemporal changes and driving forces of eco⁃ logical quality in the main stream of the Yangtze River (Jiangsu Section) from 2000 to 2020 based on RSEI and Geo Detector. Remote Sens. Technol. Appl. 39, 1478–1489 (2024).

    Google Scholar 

  33. Fu, K., Jia, G., Yu, X. & Wang, X. Ecological environment assessment and driving mechanism analysis of Nagqu and Amdo sections of Qinghai-Xizang highway based on improved remote sensing ecological index. Environ. Sci. 45, 1586–1597 (2024).

    Google Scholar 

  34. Gao, Y., Liu, Y. & Xu, H. Analysis of the spatio-temporal evolution and driving factors of urban ecological quality based on long-term Landsat image time series. Acta Geod. Cartogr. Sin. 54, 510–522 (2025).

    Google Scholar 

  35. Quan, W., Zhou, H. & Wang, W. Characteristics of ecological environment quality dynamic of the urban agglomeration in Guanzhong plain from 2000 to 2023. Res. Soil Water Conserv. 32, 336–346 (2025).

    Google Scholar 

  36. Duo, L. et al. Assessing the spatiotemporal evolution and drivers of ecological environment quality using an enhanced remote sensing ecological index in Lanzhou City. China. Remote Sens. 15, 4704 (2023).

    Google Scholar 

  37. Ji, J. et al. Spatiotemporal and multiscale analysis of the coupling coordination degree between economic development equality and eco-environmental quality in China from 2001 to 2020. Remote Sens. 14, 737 (2022).

    Google Scholar 

  38. Xiong, Y. et al. Assessment of spatial–temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan province. China. Ecol. Indic. 125, 107518 (2021).

    Google Scholar 

  39. Xu, H., Li, C. & Lin, M. Should RSEI Use PCA or kPCA?. Geomat. Inf. Sci. Wuhan Univ. 4, 506–513 (2023).

    Google Scholar 

  40. Liu, Z., Xu, H., Li, L., Tang, F. & Lin, Z. Ecological change in the Hangzhou area using the remote sensing based ecological index. J. Basic Sci. Eng. 23, 728–739 (2015).

    Google Scholar 

  41. Luo, Q., Liu, F. & Zhu, K. Spatiotemporal differentiation and spatial autocorrelation analysis of landscape ecological risk in the Chishui River Basin (Guizhou section). Res. Soil Water Conserv. 32, 282–290 (2025).

    Google Scholar 

  42. Shao, Z., Zhang, F., Peng, K. & Zhang, F. Ecological environment quality monitoring of Qitai oasis based on land use/cover change and remote sensing ecological index. Environ. Sci. 45, 5890–5899 (2024).

    Google Scholar 

  43. Tian, K. et al. Spatio-temporal evolution characteristics of cultivated land and its driving force in Karst mountainous area: A case study of Bijie City. Guizhou Agric. Sci. 51, 113–125 (2023).

    Google Scholar 

  44. Xiong, Y. et al. Assessment of spatial-temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan Province, China. Ecol. Indic. 125, 107518 (2021).

    Google Scholar 

  45. Chen, Z., Wang, X. & Guo, J. Analysis of spatial and temporal variations and driving factors of ecological environment quality in Huangshi city. Bull. Surv. Mapp. 04, 145–151 (2025).

    Google Scholar 

  46. Liu, M. Geometric analysis of ordinary least square method. Stat. Decis. 04, 90–92 (2012).

    Google Scholar 

  47. Wang, J. & Xu, C. Geo detector: Principle and prospect. Acta Geogr. Sin. 72, 116–134 (2017).

    Google Scholar 

  48. Su, Y. et al. Pervasive but biome-dependent relationship between fragmentation and resilience in forests. Nat. Ecol. Evol. 9, 1670–1684 (2025).

    Google Scholar 

  49. Liu, X. et al. Spatial patterns of soil polycyclic aromatic hydrocarbon contamination and source-specific stratified probabilistic health risk assessment at a decommissioned coking plant in North China. J. Environ. Chem. Eng. 13, 120242 (2025).

    Google Scholar 

  50. Wu, Q. et al. Tracing threats across the land–marine transition areas: Decoding heavy metal sources and risks in soil-water-sediment systems of northern and eastern coastal China. Mar. Pollut. Bull. 223, 118969 (2026).

    Google Scholar 

  51. Wang, S. et al. Anthropogenic impacts on the Yellow River Basin. Nat. Rev. Earth Environ. 6, 656–671 (2025).

    Google Scholar 

  52. Ma, X. Construction and improvement of water pollution prevention mechanism: Comment on “water pollution prevention action plan”. Yellow River 43(05), 165 (2021).

    Google Scholar 

  53. Li, Y. On systematic governance of mountain-river-forest-Farmland-Lake-Grassland-Sand in the Yellow River Basin: Logic and paths. Adm. Reform 03, 56–64 (2025).

    Google Scholar 

  54. Chen, D. & Huang, Q. The new policy for innovative transformation in regional industrial chains, the conversion of new and old kinetic energy, and energy poverty alleviation. Energies 17, 2667 (2024).

    Google Scholar 

  55. Zheng, Y., Ge, C. & Yu, F. Implementation mechanism of agricultural intensification, greening and resource-recycling: From a Low-carbon perspective. J. Huazhong Agric. Univ. (Soc. Sci. Ed.) 01, 32–44 (2022).

    Google Scholar 

  56. Zhou, G., Zhai, X. & Zhang, S. Study on the spatial-temporal pattern and influencing factors of agricultural green development in the Yellow River Basin. Yellow River 47(04), 7–14 (2025).

    Google Scholar 

  57. Zhao, K. & Ma, R. Research on the integration of urban and rural eco-environments under the contexts of industrialization and urbanization a case study of Taiyuan City of Shanxi Province. J. North Univ. China (Soc. Sci. Ed.) 34(02), 51–58 (2018).

    Google Scholar 

  58. Zhu, T., Yao, W. & Zhu, L. Research on the coupling between industrialization and ecotope. Environ. Pollut. Control 36(06), 78–83 (2014).

    Google Scholar 

  59. Yao, G. et al. The coupling coordination degree of ecological environment and urbanization in ecological civilization demonstration area based on remote sensing ecological index and compounded night light index: A case study of the Yangtze River delta. J. Ecol. Rural Environ. 39, 1386–1398 (2023).

    Google Scholar 

  60. Zhang, J. & Yang, Y. Remote sensing evaluation on the change of ecological status of Pearl River delta urban agglomeration. J. Northwest For. Univ. 34, 184–191 (2019).

    Google Scholar 

  61. Martín-López, B. et al. Delineating boundaries of social-ecological systems for landscape planning: A comprehensive spatial approach. Land Use Policy 66, 90–104 (2017).

    Google Scholar 

  62. Xu, H. & Yang, S. Urban heat island effect and urban ecosystems. J. Beijing Norm. Univ. (Nat. Sci.) 54(06), 790–798 (2018).

    Google Scholar 

  63. Bai, Y. et al. The coupled effect of soil and atmospheric constraints on the vulnerability and water use of two desert riparian ecosystems. Agric. For. Meteorol. 311, 108701 (2021).

    Google Scholar 

  64. Dehghan Rahimabadi, P. et al. The Nexus between land use/cover changes and land surface temperature: Remote sensing based two-decadal analysis. J. Arid Environ. 225, 105269 (2024).

    Google Scholar 

  65. Zhang, H. et al. An empirical analysis of tourism eco-efficiency in ecological protection priority areas based on the DPSIR-SBM model: A case study of the Yellow River Basin, China. Ecol. Inform. 70, 101720 (2022).

    Google Scholar 

  66. Hu, B. et al. Coupling strength of human-natural systems mediates the response of ecosystem services to land use change. J. Environ. Manage. 344, 118521 (2023).

    Google Scholar 

  67. Wang, Y. et al. Evaluating the ecological conservation effectiveness and strategies in Nanling key ecological function zone of China: A CHANS perspective. Ecol. Front. 44(6), 1140–1148 (2024).

    Google Scholar 

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Funding

The research was funded by the Jinan Municipal School-Integration Development Strategy Program (Phase II) (Grant No. JNSX2023107), Science and Technology Program of Shandong Provincial Department of Housing and Urban–Rural Development (Grant No. 2025KYKF-CSAQ182), Research Program of Qilu Institute of Technology (Grant No. QIT24TP006 and QIT24NN085) and Shandong Provincial Natural Science Foundation (Grant No. ZR2024QE385).

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Contributions

Conceptualization, P.W. and Q.W.; methodology, P.W. and C.T.; software, P.W.; validation, P.W., C.T. and P.W.; formal analysis, Q.W. and M.Q.; investigation, Y.F.; resources, P.W.; data curation, P.W. and Q.W.; writing—original draft preparation, P.W. and Q.W.; writing—review and editing, P.W. and C.T.; visualization, C.T., and M.Q.; supervision, X.L., and R.A.; project administration, R.A.; funding acquisition, C.T. All authors have read and agreed to the published version of the manuscript.

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Correspondence to
Chun-Pin Tseng.

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Wang, P., Tseng, CP., Fan, Y. et al. Assessing eco-environmental quality and its drivers in the Shandong section of the Yellow River Basin with an improved remote sensing ecological index.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-31580-3

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  • DOI: https://doi.org/10.1038/s41598-025-31580-3

Keywords

  • The Shandong section of the Yellow River Basin (SDYRB)
  • Eco-environmental quality
  • Improved remote sensing ecological index (IRSEI)
  • Spatial autocorrelation
  • Driving factors


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