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Spatial heterogeneous relationship between environmental quality and human disturbances: a case study in Ili Valley, China
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  • Published: 12 March 2026

Spatial heterogeneous relationship between environmental quality and human disturbances: a case study in Ili Valley, China

  • Abudukeyimu Abulizi1,2,
  • Tingting Yu1,2 &
  • Amanzhuli Yerkenhazi1,2 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Ecology
  • Environmental sciences
  • Environmental social sciences

Abstract

Increased human disturbance poses a profound threat to ecosystem sustainability worldwide. The spatial heterogeneity of the relationship between environmental quality and human disturbance increases the complexity of this issue. Different regions experience varying degrees of human disturbances and environmental conditions, resulting in diverse ecological responses. However, the heterogeneous relationship between the mechanisms of human activity and environmental quality has not been fully investigated. Therefore, based on multi-source data, remote sensing ecological and human footprint indices were used to assess the environmental quality and the intensity of human disturbance in the Ili Valley during 2009–2021. After determining that environmental quality has strong spatial autocorrelation through Moran’s I, the Getis-Ord Gi* was used to identify spatially heterogeneous units of environmental quality distribution. Furthermore, LISA maps were plotted to observe the spatial aggregation relationship between environmental quality and human disturbances. Finally, the Spatial Error Model (SEM) was identified as the most appropriate model for measuring the spatial dependence between environmental quality and human disturbance, and it was used to explain this dependence across heterogeneous units. The results showed that: (1) the environmental quality in the valley was good, whereas the slopes on both sides of the valley had poor environmental quality. Hills at middle altitudes had good environmental quality, whereas mountains at high altitudes had poor environmental quality; (2) The human disturbance intensity was higher in the valley and lower at higher elevation areas, and high-intensity areas also showed a distribution along traffic roads; (3) four different patterns of local correlations between the two variables at each location were visualized; (4) SEM was more appropriate for assessing spatial dependence of environmental quality on human disturbance among heterogeneous units because it considered the effects of spatial autocorrelation; and (5) SEM results indicated that the effects of spatial dependence between environmental quality and human disturbance were different across heterogeneous units. These findings highlight the complex relationship between environmental quality and human activity, and provide valuable insights into the spatial dependence effects of environmental quality on human disturbances and potential guidance for coordinating environmental and human activities.

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Data availability

The data presented in this study are available on request from thecorresponding author.The data are not publicly available due to the nature of this research.

References

  1. Baldocchi, D. Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 56, 1–26. https://doi.org/10.1071/bt07151 (2008).

    Google Scholar 

  2. McDonnell, M. J. & MacGregor-Fors, I. The ecological future of cities. Science 352 https://doi.org/10.1126/science.aaf3630 (2016).

  3. Willis, K. S. Remote sensing change detection for ecological monitoring in United States protected areas. Biol. Conserv. (2015). https://doi.org/10.1016/j.biocon.2014.12.006

  4. Barbosa, C. C. A., Atkinson, P. M. & Dearing, J. A. Remote sensing of ecosystem services: A systematic review. Ecol. Ind. 52 https://doi.org/10.1016/j.ecolind.2015.01.007 (2015).

  5. Roberta, K. Ecology’s remote-sensing revolution. Nature 556 https://doi.org/10.1038/d41586-018-03924-9 (2018).

  6. 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. Ind. 89, 11–21. https://doi.org/10.1016/j.ecolind.2018.02.006 (2018).

    Google Scholar 

  7. Dubinin, V., Svoray, T., Dorman, M. & Perevolotsky, A. Detecting biodiversity refugia using remotely sensed data. Landscape Ecol. 33, 1815–1830. https://doi.org/10.1007/s10980-018-0705-1 (2018).

    Google Scholar 

  8. Mishra, N. B., Crews, K. A., Neeti, N., Meyer, T. & Young, K. R. MODIS derived vegetation greenness trends in African Savanna: Deconstructing and localizing the role of changing moisture availability, fire regime and anthropogenic impact. Remote Sens. Environ. 169, 192–204. https://doi.org/10.1016/j.rse.2015.08.008 (2015).

    Google Scholar 

  9. White, D. C., Lewis, M. M., Green, G. & Gotch, T. B. A generalizable NDVI-based wetland delineation indicator for remote monitoring of groundwater flows in the Australian Great Artesian Basin. Ecol. Ind. 60, 1309–1320. https://doi.org/10.1016/j.ecolind.2015.01.032 (2016).

    Google Scholar 

  10. Chakraborty, T. & Lee, X. A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability. Int. J. Appl. Earth Observation Geoinf. 74 https://doi.org/10.1016/j.jag.2018.09.015 (2019).

  11. Keeratikasikorn, C. & Bonafoni, S. Urban Heat Island Analysis over the Land Use Zoning Plan of Bangkok by Means of Landsat 8 Imagery. Remote Sens. 10, 440. https://doi.org/10.3390/rs10030440 (2018).

    Google Scholar 

  12. Li, H. et al. A new method to quantify surface urban heat island intensity. Sci. Total Environ. 624 https://doi.org/10.1016/j.scitotenv.2017.11.360 (2018).

  13. Meng, Q. et al. Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: A 12-year case study in Beijing, China. Remote Sens. Environ. 204, 826–837. https://doi.org/10.1016/j.rse.2017.09.019 (2018).

    Google Scholar 

  14. Nurwanda, A. & Honjo, T. Analysis of land use change and expansion of surface urban heat island in Bogor City by remote sensing. ISPRS Int. J. Geo-information. 7, 165 (2018).

    Google Scholar 

  15. Sekertekin, A., Abdikan, S. & Marangoz, A. M. The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis. Environ. Monit. Assess. 190 https://doi.org/10.1007/s10661-018-6767-3 (2018).

  16. Liu, X. et al. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sens. Environ. 209, 227–239. https://doi.org/10.1016/j.rse.2018.02.055 (2018).

    Google Scholar 

  17. Xu, H. A new index for delineating built-up land features in satellite imagery. Int. J. Remote Sens. 29, 4269–4276. https://doi.org/10.1080/01431160802039957 (2008).

    Google Scholar 

  18. Xu, H. Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI). Photogrammetric Eng. Remote Sensing: J. Am. Soc. Photogrammetry. 76 https://doi.org/10.14358/PERS.76.5.557 (2010).

  19. Xu, H. Q. Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI). Photogram. Eng. Remote Sens. 76, 557–565. https://doi.org/10.14358/pers.76.5.557 (2010).

    Google Scholar 

  20. Yang, X., Qin, Q., Grussenmeyer, P. & Koehl, M. Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery. Remote Sens. Environ. 219 https://doi.org/10.1016/j.rse.2018.09.016 (2018).

  21. Xu, H. A remote sensing urban ecological index and its application. Acta Ecol. Sin. 33, 7853–7862. https://doi.org/10.5846/stxb201208301223 (2013).

    Google Scholar 

  22. Wang, Z. et al. A new remote sensing ecological index for simulating the land surface eco-environment. J. Environ. Manage. 326, 116851. https://doi.org/10.1016/j.jenvman.2022.116851 (2023).

    Google Scholar 

  23. Hu, X. & Xu, H. A new remote sensing index based on the pressure-state-response framework to assess regional ecological change. Environ. Sci. Pollut. Res. 26 https://doi.org/10.1007/s11356-018-3948-0 (2019).

  24. Zhu, D., Chen, T., Wang, Z. & Niu, R. Detecting ecological spatial-temporal changes by Remote Sensing Ecological Index with local adaptability. J. Environ. Manage. 299, 113655. https://doi.org/10.1016/j.jenvman.2021.113655 (2021).

    Google Scholar 

  25. Liu, S. et al. Quantitative evaluation of human activity intensity on the regional ecological impact studies. Acta Ecol. Sin. 38, 6797–6809. https://doi.org/10.5846/stxb201711172048 (2018).

    Google Scholar 

  26. Buckley, R., Zhou, R. & Zhong, L. How pristine are China’s parks? Front. Ecol. Evol. 4, 136. https://doi.org/10.3389/fevo.2016.00136 (2016).

    Google Scholar 

  27. Dong, X. et al. Quantization of the coupling mechanism between eco-environmental quality and urbanization from multisource remote sensing data. J. Clean. Prod. 321, 128948. https://doi.org/10.1016/j.jclepro.2021.128948 (2021).

    Google Scholar 

  28. Fan, Y., Fang, C. & Zhang, Q. Coupling coordinated development between social economy and ecological environment in Chinese provincial capital cities-assessment and policy implications. J. Clean. Prod. 229, 289–298. https://doi.org/10.1016/j.jclepro.2019.05.027 (2019).

    Google Scholar 

  29. Geng, J. et al. Spatio-temporal evolution of eco-environment quality and the response to climate change and human activities in Hainan Island. Acta Ecol. Sin. 42, 4795–4806 (2022).

    Google Scholar 

  30. Chen, H. et al. Spatiotemporal correlation between human activity intensity and surface temperature on the north slope of Tianshan Mountains. Acta Geogr. Sin. 77, 1244–1259. https://doi.org/10.1007/s11442-022-2030-5 (2022).

    Google Scholar 

  31. Zhou, Y., Liu, S., Xie, M., Sun, Y. & An, Y. Dynamics of regional vegetation changes under the disturbance of human activities: A case study of Xishuangbanna. Acta Ecol. Sin. 41, 565–574. https://doi.org/10.5846/stxb201904030645 (2021).

    Google Scholar 

  32. Zhou, T., Chen, W., Li, J. & Liang, J. Spatial relationship between human activities and habitat quality in Shennongjia Forest Region from 1995 to 2015. Acta Ecol. Sin. 41, 6134–6145. https://doi.org/10.5846/stxb202006131538 (2021).

    Google Scholar 

  33. Yu, T. et al. Evolution of environmental quality and its response to human disturbances of the urban agglomeration in the northern slope of the Tianshan Mountains. Ecol. Ind. 153, 110481. https://doi.org/10.1016/j.ecolind.2023.110481 (2023).

    Google Scholar 

  34. Getis, A. & Ord, J. K. The analysis of spatial association by use of distance statistics. Geographical Anal. 24, 189–206 (1992).

    Google Scholar 

  35. Anselin, L. Local Indicators of Spatial Association—LISA. Geographical Anal. 27, 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x (1995).

    Google Scholar 

  36. Wu, Y., Shi, K., Chen, Z., Liu, S. & Chang, Z. Developing improved time-series DMSP-OLS-like data (1992–2019) in China by integrating DMSP-OLS and SNPP-VIIRS. IEEE Trans. Geosci. Remote Sens. 60, 1–14. https://doi.org/10.1109/TGRS.2021.3135333 (2021).

    Google Scholar 

  37. Yang, J. & Huang, X. The 30 m Annual Land Cover Datasets and Its Dynamics in China From 1990 to 2021 (1.0. 1)[Data set]. https://doi.org/10.5194/essd-13-3907-2021

  38. Jin, A. B., Wang, P. H., Zhang, G. C., Shi, H. T. & Li, H. Ecological quality and spatial structure dynamics under future scenarios: A topological perspective from the Yellow River Basin. J. Clean. Prod. 522 https://doi.org/10.1016/j.jclepro.2025.146346 (2025).

  39. Tong, B. Z. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sens. Lett. 5, 423–431. https://doi.org/10.1080/2150704X.2014.915434 (2014).

  40. Crist, E. P. A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sens. Environ. 17, 301–306 (1985).

    Google Scholar 

  41. A, A. D. & H, C. W. Survey of emissivity variability in thermography of urban areas. Remote Sens. Environ. (1982). https://doi.org/10.1016/0034-4257(82)90043-8

  42. Weng, Q., Lu, D. & Schubring, J. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 89 https://doi.org/10.1016/j.rse.2003.11.005 (2003).

  43. Sobrino, J. A., Jiménez-Muñoz, J. C. & Paolini, L. Land surface temperature retrieval from LANDSAT TM 5. Remote Sens. Environ. 90, 434–440. https://doi.org/10.1016/j.rse.2004.02.003 (2004).

    Google Scholar 

  44. Carlson, T. N. & Ripley, D. A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 62, 241–252. https://doi.org/10.1016/S0034-4257(97)00104-1 (1997).

    Google Scholar 

  45. Williams, B. A. et al. Change in terrestrial human footprint drives continued loss of intact ecosystems. One Earth. 3, 371–382. https://doi.org/10.1016/j.oneear.2020.08.009 (2020).

    Google Scholar 

  46. Duan, W. Y., Jin, A. B., Liu, X. & Li, H. Seasonal variations and spatial mechanisms of 2D and 3D green indices in the central urban area. Ecol. Ind. 178 https://doi.org/10.1016/j.ecolind.2025.113828 (2025).

  47. Luc Anselin. Spatial Econometrics: Methods and Models Springer.

  48. Anselin, L. Exploring spatial data with GeoDaTM: A workbook 165–223 (Center for Spatially Integrated Social Science, 2005).

  49. Anselin, L., Li, X. & Koschinsky, J. GeoDa, From the Desktop to an Ecosystem for Exploring Spatial Data. Geographical Anal. 54, 439–466. https://doi.org/10.1111/gean.12311 (2022).

  50. Xu, H. et al. Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecol. Ind. 93 https://doi.org/10.1016/j.ecolind.2018.05.055 (2018).

  51. Zhang, Y. et al. On the spatial relationship between ecosystem services and urbanization: A case study in Wuhan, China. Sci. Total Environ. 637, 780–790. https://doi.org/10.1016/j.scitotenv.2018.04.396 (2018).

    Google Scholar 

  52. Du, F., Chen, S. & Pu, J. Spatiotemporal Coupling Analysis of Urbanization and Ecosystem Services in Southeastern Fujian. Huanjing Kexue. 45, 4152–4163. https://doi.org/10.13227/j.hjkx.202308118 (2024).

    Google Scholar 

  53. Ren, J., Bai, H., Zhong, S. C. & Wu, Z. F. Prediction of CO2 emission peak and reduction potential of Beijing-Tianjin-Hebei urban agglomeration. J. Clean. Prod. 425 https://doi.org/10.1016/j.jclepro.2023.138945 (2023).

  54. Chen, B. M., Jing, X., Liu, S. S., Jiang, J. & Wang, Y. G. Intermediate human activities maximize dryland ecosystem services in the long-term land-use change: Evidence from the Sangong River watershed, northwest China. J. Environ. Manage. 319 https://doi.org/10.1016/j.jenvman.2022.115708 (2022).

  55. Xu, S., Liu, Y., Wang, X. & Zhang, G. Scale effect on spatial patterns of ecosystem services and associations among them in semi-arid area: A case study in Ningxia Hui Autonomous Region, China. Sci. Total Environ. 598, 297–306 (2017).

    Google Scholar 

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Funding

This research was supported by the Third Xinjiang Scientific Expedition Program (Grant No. 2022xjkk1100).

Author information

Authors and Affiliations

  1. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, 830046, China

    Abudukeyimu Abulizi, Tingting Yu & Amanzhuli Yerkenhazi

  2. Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, 830046, China

    Abudukeyimu Abulizi, Tingting Yu & Amanzhuli Yerkenhazi

Authors
  1. Abudukeyimu Abulizi
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  2. Tingting Yu
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  3. Amanzhuli Yerkenhazi
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Contributions

Tingting Yu: Conceptualization, Data curation, Formal analysis, Writing – original draft. Abudukeyimu Abulizi: Funding acquisition, Supervision. Amanzhuli Yerkenhazi: Software, Methodology, Investigation, Conceptualization.

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Correspondence to Tingting Yu.

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Abulizi, A., Yu, T. & Yerkenhazi, A. Spatial heterogeneous relationship between environmental quality and human disturbances: a case study in Ili Valley, China. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42477-0

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  • Received: 31 October 2025

  • Accepted: 25 February 2026

  • Published: 12 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-42477-0

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Keywords

  • remote sensing ecological index
  • environmental quality
  • human disturbance
  • heterogeneity
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