logo
  • Home
  • Articles & Issues
    • Current
    • All Issues
  • About
    • Aims and Scope
    • Editorial Board
    • Indexing
  • For Authors
    • Submission
    • Terms of Publication
    • Formatting Guidelines
    • Peer Review Process
    • License Agreement
    • Charges and Financing
  • Ethics & Policies
    • Publication Ethics
    • Conflict of Interest
    • Open Access Policy
    • Archiving
    • Complaints Policy
    • Privacy Statement
    • Corrections and Retractions
    • Anti-plagiarism Policy
    • Generative AI Policy
  • Contacts
  • en
    • Українська

Ecological Safety and Balanced Use of Resources

  • Submit an article
  • Home
  • Articles & Issues
    • Current
    • All Issues
  • About
    • Aims and Scope
    • Editorial Board
    • Indexing
    • Sources of Financing
  • For Authors
    • Submission
    • Terms of Publication
    • Formatting Guidelines
    • Peer Review Process
    • Article Processing Charges
    • License Agreement
  • Ethics & Policies
    • Publication Ethics
    • Conflict of Interest
    • Open Access Policy
    • Archiving
    • Complaints Policy
    • Privacy Statement
    • Corrections and Retractions
    • Anti-plagiarism Policy
    • Generative AI Policy
  • Search
  • Contacts

Article

Vegetation cover dynamics of the Dniester basin under climate change influence in the 21st century

Volodymyr Salyha, Liudmyla Arkhypova
Abstract

The increasing climate instability in the Carpathian Region of Ukraine highlights the need for longterm monitoring and quantitative assessment of vegetation cover changes in the Dniester River basin. The aim of this study was to analyse vegetation cover change trends in the Dniester basin within Ivano-Frankivsk Region during 2001-2024 and to identify relationships between these trends and key climate variables. Research methods included time series analysis of median summer vegetation index values based on MODIS satellite data, application of the Mann-Kendall test to detect monotonic trends, calculation of Pearson correlation coefficients to assess linear relationships, and use of Random Forest regression to model the nonlinear impact of temperature, precipitation, land cover types, and elevation on vegetation dynamics. The main results showed an overall positive trend in vegetation index growth, with the lowest value in 2003 and the highest in 2023. Statistically significant summer trends cover 43.2% of the territory, of which 38.8% are positive and 4.4% are negative trends. The most pronounced positive changes were observed at medium elevations in the Carpathian foothills, where broadleaf and mixed forests dominate. The Random Forest model achieved a coefficient of determination of 0.718, identifying temperature as the primary predictor of vegetation dynamics, followed by land cover type, precipitation, and elevation. The practical value of the study lies in providing a scientific basis for planning conservation measures, adapting forestry to climate change, and developing sustainable ecosystem management strategies for the Carpathian Region

Download article

Received 15.04.2025

Revised 15.10.2025

Accepted 10.12.2025

https://doi.org/10.63341/esbur/2.2025.19
Retrieved from Vol. 16, No. 2, 2025
Pages 19-30

Suggested citation

Salyha, V., & Arkhypova, L. (2025). Vegetation cover dynamics of the Dniester basin under climate change influence in the 21st century. Ecological Safety and Balanced Use of Resources, 16(2), 19-30. https://doi.org/10.63341/esbur/2.2025.19

References

  1. Al-Kindi, K.M., Al Nadhairi, R., & Al Akhzami, S. (2023). Dynamic change in normalised vegetation index (NDVI) from 2015 to 2021 in Dhofar, Southern Oman in response to the climate change. Agriculture, 13(3), article number 592. doi: 10.3390/agriculture13030592.
  2. Chang, L., Li, Y., Zhang, K., Zhang, J., & Li, Y. (2023). Temporal and spatial variation in vegetation and its influencing factors in the Songliao River basin, China. Land, 12(9), article number 1692. doi: 10.3390/land12091692.
  3. CHIRPS Pentad: Climate Hazards Center infrared precipitation with station data (Version 2.0 Final). (n.d.). Retrieved from https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD.
  4. Cui, L., Pang, B., Zhao, G., Ban, C., Ren, M., Peng, D., Zuo, D., & Zhu, Z. (2022). Assessing the sensitivity of vegetation cover to climate change in the Yarlung Zangbo River basin using machine learning algorithms. Remote Sensing, 14(7), article number 1556. doi: 10.3390/rs14071556.
  5. Didan, K. (2021). MODIS/Terra vegetation indices 16-day l3 global 250m SIN grid V061 [data set]. doi: 10.5067/ MODIS/MOD13Q1.061.
  6. Eisfelder, C., et al. (2023). Seasonal vegetation trends for Europe over 30 years from a novel normalised difference vegetation index (NDVI) time-series – the TIMELINE NDVI product. Remote Sensing, 15(14), article number 3616. doi: 10.3390/rs15143616.
  7. Fathollahi, L., Wu, F., Melaki, R., & Jamshidi, P. (2023). Global NDVI forecasting from air temperature, soil moisture and precipitation using a deep neural network. SSRN. doi: 10.2139/ssrn.4598952.
  8. Feng, X., Zeng, Z., & He, M. (2023). A 20-year vegetation cover change and its response to climate factors in the Guangdong-Hong Kong-Macao greater bay area under the background of climate change. Frontiers in Ecology and Evolution, 10, article number 1080734. doi: 10.3389/fevo.2022.1080734.
  9. Friedl, M., & Sulla-Menashe, D. (2022). MODIS/Terra+Aqua land cover type yearly L3 global 500m SIN grid V061 [data set]. doi: 10.5067/MODIS/MCD12Q1.061.
  10. Glibovytska, N., Rashevska, H., Arkhypova, L., Adamenko, Y., & Orfanova, M. (2024). Impact of electric power facilities on natural phytocenotic diversity. Ukrainian Journal of Forest and Wood Science, 15(2), 8-22. doi: 10.31548/ forest/2.2024.08.
  11. Glukh, O.S., Symkanych, O.I., Kachaiev, V.M., & Hliudzyk, E.I. (2023). The NDVI index change of the Carpathian Region of Ukraine during 2000-2022. Scientific Bulletin of the Uzhhorod University. Series “Chemistry”, 49(1), 62-67. doi: 10.24144/2414-0260.2023.1.62-67.
  12. Google/Xee. (n.d.). Retrieved from https://github.com/google/Xee/tree/main/docs.
  13. Huang, S., Zheng, X., Ma, L., Wang, H., Huang, Q., Leng, G., Meng, E., & Guo, Y. (2020). Quantitative contribution of climate change and human activities to vegetation cover variations based on GA-SVM model. Journal of Hydrology, 584, article number 124687. doi: 10.1016/j.jhydrol.2020.124687.
  14. Hussain, M., & Mahmud, I. (2019). pyMannKendall: A python package for non parametric Mann Kendall family of trend tests. ResearchGate. doi: 10.21105/joss.01556.
  15. Ivanyshyn, V., & Kasiyanchuk, D. (2024). Analysis of the impact of climate change on the vegetation of the Perehinsk Territorial Community in Ukraine. Grassroots Journal of Natural Resources, 7(2), 199-215. doi: 10.33002/ nr2581.6853.070210.
  16. Klimavičius, L., Rimkus, E., Stonevičius, E., & Mačiulytė, V. (2022). Seasonality and long-term trends of NDVI values in different land use types in the eastern part of the Baltic Sea basin. Oceanologia, 65(1), 171-181. doi: 10.1016/j. oceano.2022.02.007.
  17. Krakovska, S., & Kryshtop, L. (2024). Overall climate change impact assessment for Ukraine. Kyiv: Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH.
  18. Kravchynskyi, R.L., Khilchevskyi, V.K., Korchemluk, M.V., Arkhypova, L.M., & Plichko, L.V. (2021). Criteria for identification of landslides in the upper Prut River basin on satellite images. In Geoinformatics (pp. 1-6). Kyiv: European Association of Geoscientists & Engineers. doi: 10.3997/2214-4609.20215521003.
  19. Lyalko, V.I., Romanciuc, I.F., Yelistratova, L.A., Apostolov, A.A., & Chekhniy, V.M. (2020). Detection of changes in terrestrial ecosystems of Ukraine using remote sensing data. Journal of Geology, Geography and Geoecology, 29(1), 102-110. doi: 10.15421/112010.
  20. Mann, H.B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245-259. doi: 10.2307/1907187.
  21. Mao, K., Li, Z., Chen, J., Ma, Y., Liu, G., Tan, X., & Yang, K. (2016). Global vegetation change analysis based on MODIS data in recent twelve years. High Technology Letters, 22(4), 343-349. doi: 10.3772/j.issn.1006-6748.2016.04.001.
  22. Marod, D., Thinkampheang, S., Phumphuang, W., Yarnvudhi, A., Thongsawi, J., Kachina, P., Nakashizuka, T., Kurokawa, H., & Hermhuk, S. (2025). Relationship between climate changes and forest dynamics along altitudinal gradients at Doi Suthep-Pui National Park, Northern Thailand. Forests, 16(1), article number 114. doi: 10.3390/ f16010114.
  23. Matiyiv, K., Klymchuk, I., Arkhypova, L., & Korchemlyuk, M. (2022). Surface water quality of the Prut River basin in a tourist destination. Ecological Engineering & Environmental Technology, 23(4), 107-114. doi: 10.12912/27197050/150311.
  24. NASA SRTM digital elevation 30m. (n.d.). Retrieved from https://developers.google.com/earth-engine/datasets/ catalog/USGS_SRTMGL1_003.
  25. Prăvălie, R., et al. (2022). NDVI-based ecological dynamics of forest vegetation and its relationship to climate change in Romania during 1987-2018. Ecological Indicators, 136, article number 108629. doi: 10.1016/j.ecolind.2022.108629

Ivano-Frankivsk National Technical University of Oil and Gas 76019, 15 Karpatska Str., Ivano-Frankivsk, Ukraine

  • mail@esbur.com.ua publisher@nung.edu.ua