Evaluation of indicators for desertification risk assessment of Oleshky sands desertification based on Landsat data time series


  • Mykola Lubskyi Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Olesia Honchara Str., 55-b, Kyiv, 01054, Ukraine https://orcid.org/0000-0002-3545-0007
  • Tetiana Orlenko Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Olesia Honchara Str., 55-b, Kyiv, 01054, Ukraine https://orcid.org/0000-0002-4933-7750
  • Iryna Piestova Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Olesia Honchara Str., 55-b, Kyiv, 01054, Ukraine https://orcid.org/0000-0003-2981-7826
  • Artem Andreiev Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Olesia Honchara Str., 55-b, Kyiv, 01054, Ukraine https://orcid.org/0000-0002-6485-449X
  • Artur Lysenko Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Olesia Honchara Str., 55-b, Kyiv, 01054, Ukraine https://orcid.org/0000-0003-2923-8648




desertification, satellite monitoring, land cover classification, regression analysis, spectral index, biophysical indicator, climate change


Earth's surface monitoring allows the assessment of the dynamics and mapping of desertification indicators and is currently one of the priority research regions in remote sensing. In addition to the fact that desertification is a serious global threat to economic, social and food security, the risks of desertification have also become relevant for southern Ukraine. In recent years, climate change has become more and more noticeable in Ukraine. As a result, the time frames of the seasons are blurred, the boundaries of natural zones are shifting, abnormal values of meteorological indicators are increasingly observed, and the intensity of natural disasters is increasing. Since southern Ukraine is most vulnerable to adverse climate and landscape changes, including desertification, the article considered long-term landscape changes on the right bank of the Dnieper River in the Kherson region, mainly represented by sandy massifs of the Oleshky region, which are called the Oleshky Sands. Arid landscapes, which include the vast majority of Oleshky, are especially vulnerable to degradation processes, so space monitoring this territory is an urgent task given the current global climate changes. Within the framework of the presented study, the long-term dynamics of several indicators of desertification within the sandy arenas of the Oleshkovsky sands were mapped. Based on a series of multispectral space images obtained by Landsat satellites for the period 1986-2020 was considered. Spectral ranges characterize the key biophysical aspects of arid landscapes: the degree of soil moisture, vegetation density and sand distribution. Regression analysis was used to illustrate the dynamics of each indicator, which showed a general increase in each indicator over the experimental period. Furthermore, we determined the relationship between the values of the rise in desertification indicators and landscape changes that occurred in the research region, the classification of several images for different years was also carried out, and spatial and quantitative changes in the distribution of land cover classes were characterized.


Afrasinei, G. M., Melis, M. T., & Buttau, C. (2017). Classification methods for detecting and evaluating changes in desertification-related features in arid and semiarid environments. Euro-Mediterr. J. Environ. Integr., 2, 14. doi:10.1007/s41207-017-0021-1.

Al-Bakri, J. T., Brown, L., Gedalof, Z., Berg, A., Nickling, W., Khresat, S., Salahat, M. & Saoub, H. (2016). Modelling desertification risk in the north-west of Jordan using geospatial and remote sensing techniques. Geomatics Nat. Hazards Risk, 7(2), 531–549. doi:10.1080/19475705.2014.945102.

Apostolov, O. A., Yelistratova, L. A., Romanciuc, I. F., Chekhniy, V. M. (2020). Identification of of deserted areas in Ukraine based on calculations of water indices based on remote sensing of the Earth. Ukrainian Geographical Journal, 1, 16–25. doi:10.15407/ugz2020.01.016. (In Ukrainian).

Bakr, N., Weindorf, D. C., Bahnassy, M., & El-Badawi, M. M. (2012). Multi-temporal assessment of land sensitivity to desertification in a fragile agro-ecosystem: environmental indicators. Ecol. Indic., 15, 271–280. doi:10.1016/j.ecolind.2011.09.034.

Barzani, N. M. & Khairulmaini O. S. (2013). Desertification risk mapping of the Zayandeh Rood Basin in Iran. J. Earth Syst. Sci., 122(5), 1269–1282. doi:10.1007/s12040-013-0348-1.

Crist, E. P. & Cicone, R. C. (1984). A physically based transformation of Thematic Mapper data – The TM Tasseled Cap. IEEE Trans. Geosci. Remote Sens., 22(3), 256–263. doi:10.1109/TGRS.1984.350619.

Dalezios, N. R. & Eslamian, S. (2017). Environmental Impacts of Drought to Desertification Classification. In: S. Eslamian, & F. Eslamian (Eds.). Handbook of Drought and Water Scarcity, 1st ed., (vol. 2), 45-63. Taylor and Francis: Boca Raton, FL, USA.

Dawelbait, M. & Morari, F. (2012). Monitoring desertification in a Savannah region in Sudan using Landsat images and spectral mixture analysis. J. Arid. Environ., 80, 45–55. doi:10.1016/j.jaridenv.2011.12.011.

Djeddaoui, F., Chadli, M. & Gloaguen, R. (2017). Desertification Susceptibility Mapping Using Logistic Regression Analysis in the Djelfa Area, Algeria. Remote Sens., 9(10), 1031. doi:10.3390/rs9101031.

Fathizad, H., Ali Hakimzadeh Ardakani, M., Taghizadeh Mehrjardi, R. & Sodaiezadeh, H. (2018). Evaluating desertification using remote sensing technique and object-oriented classification algorithm in the Iranian central desert. J. Afr. Earth. Sci., 145, 115–130. doi:10.1016/j.jafrearsci.2018.04.012.

Food and Agriculture Organization of the United Nations. United Nations Environment Programme. (1984). Provisional Methodology for Assessment and Mapping of Desertification. Rome, FAO, UNEP. ISBN: 9789251014424.

Gao, B. C. (1996). NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ., 58, 257–266. doi:10.1016/S0034-4257(96)00067-3.

Granovska L. M. (2019). Hydrological and hydrogeological features of formation and use of Nizhny Dnieper sands. Ecological Sciences, 3(26), 40–45. doi:10.32846/2306-9716-2019-3-26-8. (In Ukrainian).

Huete, A., Didan, K., Miura, T., Rodriguez E. P., Gao X. & Ferreira, L. G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices (2002). Remote Sens. Environ., 83, 195–213. doi:10.1016/S0034-4257(02)00096-2.

Kim, K., Wang, M. C., Ranjitkar, S., Liu, S., Xu, L. C. & Zomer, R. J. (2017). Using leaf area index (LAI) to assess vegetation response to drought in Yunnan province of China. J. Mountain Sci., 14, 1863–1872. doi:10.1007/s11629-016-3971-x.

Kryvulchenko А. (2019). Oleshky sands as a hierarchically constructed natural system. Bulletin of Lviv University. The geographical series, 53, 197–209. URL: http://publications.lnu.edu.ua/bulletins/index.php/geography/article/view/10666. (In Ukrainian).

Laity, J. (2008). Deserts and Desert Environments. Wiley-Blackwell, Oxford.

Lauwaet, D., van Lipzig, N. P. M. & De Ridder, K. (2009). The effect of vegetation changes on precipitation and Mesoscale Convective Systems in the Sahel. Clim. Dyn., 33, 521–534. doi:10.1007/s00382-009-0539-2.

Li, B. L. (2001). Sandy desertification trend in western Northeast China Plain in the past 10 years. Acta Geographica Sinica, 2, 54–61. doi:10.1007/BF02888688.

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, 1(29), 102–110. doi:10.15421/112010.

Mariano, D. A., Santos, C. A. C., Wardlow, B. D., Anderson, M. C., Schiltmeyer, A. V., Tadesse, T. & Svoboda, M. D. (2018). Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in Northeastern Brazil. Remote Sens. Environ., 231, 129–143. doi:10.1016/j.rse.2018.04.048.

Markham, B. L., Storey, J. C., Williams, D. L. & Irons, J. R. (2004). Landsat sensor performance: history and current status. IEEE Trans. Geosci. Remote Sens., 42(12), 2691-2694. doi:10.1109/TGRS.2004.840720.

Middleton, N. & Thomas, D. (1997). World atlas of desertification. London, New York, Sydney, Auckland, Arnold, UNEP.

O'Leary, D. P. (1990). Robust regression computation using iteratively reweighted least squares. SIAM J. Matrix Anal. Appl., 11(3), 466–480. doi:10.1137/0611032.

Richards, J. (1999). Remote Sensing Digital Image Analysis. Berlin: Springer-Verlag.

Rivera-Marin, D., Dash, J. & Ogutu, B. (2022). The use of remote sensing for desertification studies: A review. J. Arid. Environ., 206, 104829. doi:10.1016/j.jaridenv.2022.104829.

Safriel, U. & Adeel, Z. (2005). Dryland systems. In Millennium Ecosystem Assessment, Chapter 22, World Resources Institute, Island Press, Washington, DC.

Zanchetta, A., Bitelli, G. & Karnieli, A. (2016). Monitoring desertification by remote sensing using the Tasselled Cap transform for long-term change detection. Nat. Hazard., 83, 223–237. doi:10.1007/s11069-016-2342-9.

Zeng, N. & Yoon, J. (2009). Expansion of the world's deserts due to vegetation-albedo feedback under global warming. Geophys. Res. Lett., 36(17), L17401. doi:10.1029/2009GL039699.





Techniques for Earth observation data acquisition, processing and interpretation