GEODETIC SUPPORT AND PROSPECTS FOR SPECTRAL MONI-TORING OF CONTOUR SWALES ON SLOPING TERRITORIES OF EASTERN KAZAKHSTAN
DOI:
https://doi.org/10.54668/2789-6323-2026-123-3-230-239Keywords:
Contour swales, UAV photogrammetry, Structure-from-Motion (SfM), digital elevation model (DEM), remote sensing, Sentinel-2, topographic wetness index (TWI), erosion control, soil erosion, slope terrains, Eastern KazakhstanAbstract
The article examines the potential application of high-resolution digital elevation models and satellite data for the design and remote monitoring of contour swales on sloping territories of Eastern Kazakhstan. The morphometric basis of the study was developed using UAV survey data processed with Structure-from-Motion (SfM) algorithms and the generation of a digital elevation model with a spatial resolution of 0.1 m. In addition, ALOS AW3D30 data and Sentinel-2 Level-2A multispectral imagery were used.
The study included an analysis of morphometric terrain parameters and topographic indices characterizing moisture conditions and slope erosion susceptibility. It was established that slopes exceeding 5° increase the risk of surface runoff concentration and local erosion, requiring highly accurate alignment of contour swales along contour lines. A comparison of the Topographic Wetness Index (TWI) with the distribution of vegetation cover derived from satellite data revealed relationships between terrain features, moisture conditions, and vegetation patterns within the study area.
The study also demonstrated that the use of Sentinel-2 data for monitoring contour swales is complicated by the mixed-pixel effect caused by the mismatch between swale dimensions and the spatial resolution of satellite imagery. In this regard, the use of spectral mixture analysis methods and further investigation of soil and vegetation spectral characteristics are considered promising.
The research demonstrates the potential for integrated use of high-resolution terrain models and satellite data for the remote assessment of anti-erosion structures under the environmental conditions of Eastern Kazakhstan.
References
Шынбергенов Е.А., Сиханова Н.С. Методические проблемы определения смыва почв в Казахстане // Вестник КазНУ. Серия географическая и экологическая. – 2024. – Т. 73. – № 2. – С. 45–56. – URL: https://bulletin-geography.kaznu.kz/index.php/1-geo/article/view/1391
Кужинов М., Абдрахманов А., Сарсенова Г. Влияние экспозиции склонов на проявление эрозионных про-цессов // Вестник Костанайского регионального университета. – 2022. – № 3. – С. 112–119. – URL: https://ojs.ksu.edu.kz/index.php/3i/article/view/397
Айкешев Б.М., Айнакулов Ж.Ж., Нурышев М.Ж. Роль валоканав в сельском хозяйстве: мелиорация, борьба с эрозией и сохранение влаги // Вестник науки Казахского агротехнического исследовательского университета им. С. Сейфуллина. – 2025. – № 1(124). – С. 13–26. https://doi.org/10.51452/kazatu.2025.1(124).1807
Appels W.M., Bogaart P.W., van der Zee S.E.A.T.M. Influence of spatial variations of microtopography and infil-tration on surface runoff and field scale hydrological connectivity // Advances in Water Resources. – 2011. – Vol. 34. – No. 2. – P. 303–313. – https://doi.org/10.1016/j.advwatres.2010.12.003
Liang X., Feng J., Ye Z. et al. The effect of soil surface mounds and depressions on runoff // Sustainability. – 2023. – Vol. 15. – No. 1. – 175. – DOI: https://doi.org/10.3390/su15010175
Li P., Li D., Hu J. et al. Improving the application of UAV-LiDAR for erosion monitoring through accounting for uncertainty in DEM of difference // CATENA. – 2024. – Vol. 234. – 107534. – DOI: https://doi.org/10.1016/j.catena.2023.107534
Medeiros B.M., Cândido B., Jimenez P.A.J. et al. UAV-Based Soil Water Erosion Monitoring: Current Status and Trends // Drones. – 2025. – Vol. 9. – No. 4. – 305. – DOI: https://doi.org/10.1016/j.catena.2023.107534
Varghese D., Radulović M., Stojković S., Crnojević V. Reviewing the potential of Sentinel-2 in assessing the drought // Remote Sensing. – 2021. – Vol. 13. – No. 17. – 3355. – DOI: https://doi.org/10.3390/rs13173355
СП РК 3.04-101-2012. Гидротехнические сооружения: основные положения. – Астана, 2012.
Hardie M. Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture // Sensors. – 2020. – Vol. 20. – No. 23. – 6934. – DOI: https://doi.org/10.3390/s20236934
Louis J., Debaecker V., Pflug B. et al. Sentinel-2 Sen2Cor: L2A processor for users // Proceedings of the Living Planet Symposium 2016. – 2016. – URL: http://esamultimedia.esa.int/multimedia/publications/SP-740/SP-740_toc.pdf
Gao B.-C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space // Remote Sensing of Environment. – 1996. – Vol. 58. – No. 3. – P. 257–266. – DOI: https://doi.org/10.1016/S0034-4257(96)00067-3
European Space Agency (ESA). Sentinel-2 mission overview. – URL: https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2 (accessed: 18.05.2026).
Keshava N., Mustard J.F. Spectral unmixing // IEEE Signal Processing Magazine. – 2002. – Vol. 19. – No. 1. – P. 44–57. – DOI: https://doi.org/10.1109/79.974727
Ochoa F., Brodrick P.G., Okin G.S. et al. Soil and vegetation cover estimation for global imaging spectroscopy using spectral mixture analysis // Remote Sensing of Environment. – 2025. – Vol. 324. – 114746. – DOI: https://doi.org/10.1016/j.rse.2025.114746
Li Z., Chen J., Rahardja S. Graph construction for hyperspectral data unmixing // Hyperspectral Imaging in Agri-culture, Food and Environment. – IntechOpen, 2018. – DOI: https://doi.org/10.5772/intechopen.73158
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Айнур Каранеева, Жанат Толеубекова, Забида Курмангалиева, Толкын Қуанышбек

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.




