Assessment of ecological condition and anthropogenic impact in the Teris River basin based on remote sensing data (Zhambyl region)
Views: 23 / PDF downloads: 8
DOI:
https://doi.org/10.32523/3107-278X-2026-155-2-133-155Keywords:
RSEI, remote sensing of the Earth, anthropogenic impact, ecological condition, Teris River basinAbstract
The paper presents a spatiotemporal assessment of the ecological state and the level of anthropogenic impact in the Teris River basin (Zhambyl region) based on long-term data from remote sensing for 2015-2025. The information basis of the study was the satellite data of Landsat 8 and 9 (Collection 2 Level 2), processed in the Google Earth Engine environment using atmospheric correction procedures, cloud masking and normalization of spectral indicators. To quantify the key components of geosystems, the NDVI (vegetation), WET (moisture component of the Tasseled Cap transformation), LST (temperature of the Earth's surface) and NDBSI (dryness and degree of anthropogenic transformation) indices were calculated. The integration of standardized indicators was carried out using the principal component method, followed by the formation of an integrated environmental status index (RSEI). The results obtained indicate a pronounced spatial differentiation and high interannual variability of environmental conditions. Throughout the entire study period, the structure of the territory was dominated by the «below average» and «average» classes, the combined share of which exceeded 70 % in most years. The largest share of the «medium» class was recorded in 2016 (56.88%), while in 2025 there was a significant expansion of the «high» class (34.29 %), reflecting a local improvement in the environmental condition. Zones with low RSEI values occupy limited areas (up to 1.34 %), but are spatially confined to areas of intensive land use and degraded lands. The most favorable environmental conditions are typical for mountainous and floodplain landscapes, while central and agriculturally developed areas show reduced integral indicators. The results of the study confirm the methodological viability of RSEI as an integral tool for monitoring the dynamics of the ecological state and assessing anthropogenic load within the basin geosystems of semi-arid regions.
Downloads
References
Aizizi, Y., Kasimu, A., Liang, H., Zhang, X., Wei, B., Zhao, Y., & Ainiwaer, M. (2023). Evaluation of ecological quality status and changing trend in arid land based on the remote sensing ecological index: A case study in Xinjiang, China. Forests, 14(9), 1830. https://doi.org/10.3390/f14091830
Baig, M. H. A., Zhang, L., Shuai, T., & Tong, Q. (2014). Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters, 5(5), 423–431. https://doi.org/10.1080/2150704X.2014.915434
Crist, E. P., & Cicone, R. C. (1984). A physically-based transformation of Thematic Mapper data: The TM tasseled cap. IEEE Transactions on Geoscience and Remote Sensing, GE-22(3), 256–263. https://doi.org/10.1109/TGRS.1984.350619
De Araujo Barbosa, C. C., Atkinson, P. M., & Dearing, J. A. (2015). Remote sensing of ecosystem services: A systematic review. Ecological Indicators, 52, 430–443. https://doi.org/10.1016/j.ecolind.2015.01.007
European Space Agency. (2024). Sentinel-2 mission. https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2
Esri. (2024). An overview of the Hydrology tools: ArcGIS Desktop Documentation. https://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analyst-toolbox/an-overview-of-the-hydrology-tools.htm
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., & Alsdorf, D. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(2), RG2004. https://doi.org/10.1029/2005RG000183
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Hasan, M. M., Ferdous, M. T., Talha, M., Mojumder, P., Roy, S. K., Zim, M. N. F., Akter, M. M., Nasher, N. M. R., Hasher, F. F. B., Boltižiar, M., & Zhran, M. (2025). Analyzing ecological environmental quality trends in Dhaka through remote sensing based ecological index (RSEI). Land, 14(6), 1258. https://doi.org/10.3390/land14061258
Kendall, M. G. (1975). Rank correlation methods (4th ed.). Charles Griffin.
Kennedy, R. E., Andréfouët, S., Cohen, W. B., Gómez, C., Griffiths, P., Hais, M., Healey, S. P., Helmer, E. H., Hostert, P., Lyons, M. B., Meigs, G. W., Pflugmacher, D., Phinn, S. R., Powell, S. L., Scarth, P., Sen, S., Schroeder, T. A., Schneider, A., Sonnenschein, R., & Zhu, Z. (2014). Bringing an ecological view of change to Landsat-based remote sensing. Frontiers in Ecology and the Environment, 12(6), 339–346. https://doi.org/10.1890/130066
Kwok, R. (2018). Ecology’s remote-sensing revolution. Nature, 556(7699), 137–138. https://doi.org/10.1038/d41586-018-03924-9
Liu, P., Wen, T., Han, R., & Wu, S. (2025). Study on RSEI changes using remote sensing and Markov-FLUS modeling approach. Sustainability, 17(22), 10267. https://doi.org/10.3390/su172210267
Liu, S., Liu, L., Wu, X., Hou, X., Zhao, S., & Liu, G. (2018). Quantitative evaluation of human activity intensity on the regional ecological impact studies. Acta Ecologica Sinica, 38(19). https://doi.org/10.5846/stxb201711172048
Liu, Y., Zhou, T., & Yu, W. (2024). Analysis of changes in ecological environment quality and influencing factors in Chongqing based on a remote-sensing ecological index mode. Land, 13(2), 227. https://doi.org/10.3390/land13020227
Maimaitituersun, A., Yang, H., Aobuliaisan, N., Maimaitiaili, K., & Chenyu, O. (2025). Assessing subtle changes in arid land river basin ecological quality: A study utilizing the PIE engine platform and RSEI. Ecological Indicators, 170, 113035. https://doi.org/10.1016/j.ecolind.2024.113035
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259.
Mueller, L., Saparov, A., & Lischeid, G. (Eds.). (2014). Novel measurement and assessment tools for monitoring and management of land and water resources in agricultural landscapes of Central Asia. Springer International Publishing. https://doi.org/10.1007/978-3-319-01017-5
Ning, L., Jiayao, W., & Fen, Q. (2020). The improvement of ecological environment index model RSEI. Arabian Journal of Geosciences, 13(11), 403. https://doi.org/10.1007/s12517-020-05414-7
Reza, M. I. H., & Abdullah, S. A. (2011). Regional index of ecological integrity: A need for sustainable management of natural resources. Ecological Indicators, 11(2), 220–229. https://doi.org/10.1016/j.ecolind.2010.08.010
Rikimaru, A., Roy, P. S., & Miyatake, S. (2002). Tropical forest cover density mapping. ITTO Technical Report.
Saparov, K. T., Shakhantayeva, Z. R., & Yeginbayeva, A. Y. (2023). System of hydrographic terms of the Zhambyl region. Journal of Geography, Politics and Society, 13(4), 40–48. https://doi.org/10.26881/jpgs.2023.4.04
Saparov, K., Shakhantayeva, Z., & Yeginbayeva, A. (2023). Reflection of the names of artificial and natural water sources in the study of microhydronyms of Zhambyl region. Bulletin of the L. N. Gumilyov Eurasian National University. Chemistry. Geography. Ecology Series, 143(2), 79–89. https://doi.org/10.32523/2616-6771-2023-143-2-79-89
Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324), 1379–1389.
Tang, Q., Hua, L., Tang, J., Jiang, L., Wang, Q., Cao, Y., Wang, T., & Cai, C. (2024). Advancing ecological quality assessment in China: Introducing the ARSEI and identifying key regional drivers. Ecological Indicators, 163, 112109. https://doi.org/10.1016/j.ecolind.2024.112109
Main Administration of Geodesy and Cartography of the USSR. (n.d.). Topographic map at a scale of 1:200,000: Sheets K-42-11, K-42-12, K-42-17, K-42-18. https://maps.vlasenko.net/smtm200/k-42-12.jpg
United Nations. (2024). Sustainable Development Goals Report 2024. United Nations. https://unstats.un.org/sdgs/report/2024/
U.S. Geological Survey. (2024). Landsat Collection 2 Level-2 Science Products. https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products
U.S. Geological Survey. (2024). Landsat 8–9 Collection 2 Level-2 Science Product Guide. https://www.usgs.gov/media/files/landsat-8-9-collection-2-level-2-science-product-guide
Wang, H., Tang, L., Qiu, Q., & Chen, H. (2020). Assessing the impacts of urban expansion on habitat quality by combining the concepts of land use, landscape, and habitat in two urban agglomerations in China. Sustainability, 12(11), 4346. https://doi.org/10.3390/su12114346
Wang, J., Lin, Y., Zhai, T., He, T., Qi, Y., Jin, Z., & Cai, Y. (2018). The role of human activity in decreasing ecologically sound land use in China. Land Degradation & Development, 29(3), 446–460. https://doi.org/10.1002/ldr.2874
Wang, L., Tian, F., Wang, X., Yang, Y., & Wei, Z. (2020). Attribution of the land surface temperature response to land-use conversions from bare land. Global and Planetary Change, 193, 103268. https://doi.org/10.1016/j.gloplacha.2020.103268
Wikipedia contributors. (2024). Teris River. Wikipedia. https://ru.wikipedia.org/wiki/Терис_(река)
Wikipedia contributors. (2024). Asy River. Wikipedia. https://ru.wikipedia.org/wiki/Асы_(река)
Willis, K. S. (2015). Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation, 182, 233–242. https://doi.org/10.1016/j.biocon.2014.12.006
Wolters, M. L., Sun, Z., Huang, C., & Kuenzer, C. (2016). Environmental awareness and vulnerability in the Yellow River Delta: Results based on a comprehensive household survey. Ocean & Coastal Management, 120, 1–10. https://doi.org/10.1016/j.ocecoaman.2015.11.009
Wu, S., Gao, X., Lei, J., Zhou, N., Guo, Z., & Shang, B. (2022). Ecological environment quality evaluation of the Sahel region in Africa based on remote sensing ecological index. Journal of Arid Land, 14(1), 14–33. https://doi.org/10.1007/s40333-022-0057-1
Xu, H. (2008). A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing, 29(14), 4269–4276. https://doi.org/10.1080/01431160802039957
Xu, H. (2013). A remote sensing urban ecological index and its application. Acta Ecologica Sinica, 33, 7853–7862. https://doi.org/10.5846/stxb201208301223
Xu, H., Wang, M., Shi, T., Guan, H., Fang, C., & Lin, Z. (2018). Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecological Indicators, 93, 730–740. https://doi.org/10.1016/j.ecolind.2018.05.055
Xu, H., Wang, Y., Guan, H., Shi, T., & Hu, X. (2019). Detecting ecological changes with a remote sensing based ecological index (RSEI) produced time series and change vector analysis. Remote Sensing, 11(20), 2345. https://doi.org/10.3390/rs11202345
Xu, H., Shi, T., Wang, M., & Lin, Z. (2017). Land cover changes in the Xiong’an New Area and a prediction of ecological response to forthcoming regional planning. Acta Ecologica Sinica. https://doi.org/10.5846/stxb201705210941
Yang, H., Yu, J., Xu, W., Wu, Y., Lei, X., Ye, J., Geng, J., & Ding, Z. (2023). Long-time series ecological environment quality monitoring and cause analysis in the Dianchi Lake Basin, China. Ecological Indicators, 148, 110084. https://doi.org/10.1016/j.ecolind.2023.110084
Yue, H., Liu, Y., Li, Y., & Lu, Y. (2019). Eco-environmental quality assessment in China’s 35 major cities based on remote sensing ecological index. IEEE Access, 7, 51295–51311. https://doi.org/10.1109/ACCESS.2019.2911627
Zhang, T., Yang, R., Yang, Y., Li, L., & Chen, L. (2021). Assessing the urban eco-environmental quality by the remote-sensing ecological index: Application to Tianjin, North China. ISPRS International Journal of Geo-Information, 10(7), 475. https://doi.org/10.3390/ijgi10070475
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Қ. Қырғызбай, Б. Дүйсебек, Т. Усманов, И. Рахметов, Е. Муканов (Автор)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
