Possibilities of GIS technologies for predictive detection of areas of solid flow discharge within water protection zones

DOI: 10.35595/2414-9179-2022-2-28-583-596

View or download the article (Rus)

About the Author

Arseniy O. Poletaev

Belgorod National Research University, Institute of Earth Sciences,
Pobedy str., 85, 308015, Belgorod, Russia;
E-mail: poletaev@bsu.edu.ru

Abstract

The article considers the modern possibilities of GIS technologies for monitoring the state of the soil cover and water erosion processes. The possibilities of using the Normalized Diference Vegetation Index (NDVI) to assess various types of vegetation cover are shown. The substantiation of the choice of a key site, which includes both water protection zones and landscape positions associated with them in terms of material and energy flows, is presented. A method for obtaining a vector layer of NDVI values calculated from 9 Sentinel-2 satellite images for the period from March to November 2021 is presented. NDVI values are classified and the cells of the vector layer are combined into classes. Methods for obtaining rasters (with formula reduction) of the Topographical Wetness Index (TWI) and the Stream Power Index (SPI) on the territory of a key area are presented. The vector layer of NDVI values was compared with the TWI and SPI rasters, as well as with the average daily air temperature values. The dynamics of NDVI values for March–November 2021 is shown in the key area, a schematic map of the vector layer of NDVI values, ranked by class, is shown. The calculation of the ratio of areas of different classes in the key area was carried out. Topographical Wetness Index (TWI) and Stream Power Index (SPI) rasters are shown. Examples of queries to databases of layers obtained as a result of intersection of vector layers are given: TWI and NDVI, SPI and NDVI. Schematic maps have been obtained based on a combination of NDVI, TWI, SPI values, showing potentially erosion-hazardous areas. When comparing the average daily air temperature values with the average NDVI values, it was found that the correlation between them is 0.89. Possible measures aimed at reducing the environmental load on the water protection zone are proposed.

Keywords

NDVI, TWI, SPI, water protection zones, erosion risk assessment

References

  1. Buryak Z., Lisetskii F., Gusarov A., Narozhnyaya A., Kitov M. Basin-Scale Approach to Integration of Agro- and Hydroecological Monitoring for Sustainable Environmental Management: A Case Study of Belgorod Oblast, European Russia. Sustainability (Switzerland). 2022. Vol. 14. No. 2. P. 927. DOI: 10.3390/su14020927.
  2. Buryak Z., Marinina O. Using GIS technology for identification of agricultural land with an increased risk of erosion. E3S Web of Conferences. 2020. Vol. 176. P. 04007. DOI: 10.1051/e3sconf/202017604007.
  3. Buryak Zh.A. Improvement of approaches to assessing the risk of erosion in agricultural landscapes using GIS technology. Regional Geosystems. 2014. Vol. 29. No. 23 (194). P. 140–146 (in Russian).
  4. Dubrova Yu.N., Myslyva T.N., Tkacheva T.N. Geomorphometric analysis of the relief of the territory of the Gorki district using remote sensing data. Bulletin of the Belarusian State Agricultural Academy. 2021. No. 1. P. 209–216 (in Russian).
  5. Ganasri B.P., Ramesh H. Assessment of soil erosion by RUSLE model using remote sensing and GIS—A case study of Nethravathi Basin. Geoscience Frontiers. 2016. Vol. 7. No. 6. P. 953–961. DOI: 10.1016/j.gsf.2015.10.007.
  6. Gitas I.Z., Douros K., Minakou C., Silleos G.N., Karydas C.G. Multi-temporal soil erosion risk assessment in N. Chalkidiki using a modified USLE raster model. EARSel eProceedings. 2009. Vol. 8. No. 1. P. 40–52.
  7. Glotov A.A. The use of DEM for effective environmental management. Geomatics. 2013. No. 4. P. 32–36 (in Russian).
  8. Grigoriev I.I., Rysin I.I. The creation and use of gully-erosion geoinformation system. Proceedings of the 2nd All-Russian scientific and practical conference with international participation, dedicated to the Year of Ecology and the 55th anniversary of higher geographical education in the Udmurt Republic “Problems of regional ecology and geography”, Izhevsk, October 9–13. 2017. P. 278–282 (in Russian).
  9. Huang S., Tang L., Hupy J.P., Wang Y., Shao G. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research. 2021. Vol. 32. P. 1–6. DOI: 10.1007/s11676-020-01155-1.
  10. Kovaleva T.N., Lisetsky F.N. Land management of agrolandscapes of Volga Upland using modern software tools and data of space monitoring. Regional geosystems. 2012. Vol. 19. No. 9 (128). P. 166–172 (in Russian).
  11. Lisetskii F.N. Estimates of Soil Renewal Rates: Applications for Anti-Erosion Arrangement of the Agricultural Landscape. Geosciences. 2019. Vol. 9. No. 6. P. 266. DOI: 10.3390/geosciences9060266.
  12. Lisetskii F.N., Zemlyakova A.V., Terekhin E.A., Naroznyaya A.G., Pavlyuk Y.V., Ukrainskii P.A., Kirilenko Z.A., Marinina O.A., Samofalova O.M. New opportunities of geoplanning in the rural area with the implementing of geoinformational technologies and remote sensing. Advances in Environmental Biology. 2014. Vol. 8. No. 10. P. 536–539.
  13. Lisetsky F.N., Martsinevskaya L.V. Assessment of development of linear erosion and soil erosion as a result of aerial photo shooting. Land management, cadastre and land monitoring. 2009. No. 10. P. 39–43 (in Russian).
  14. Moore I.D., Grayson R.B., Ladson A.R. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes. 1991. Vol. 5. P. 3–30. DOI: 10.1002/hyp.3360050103.
  15. Mudrykh N.M., Samofalova I.A., Chashchin A.N. Forecasting soil erosional losses using the RUSLE model. AgroEcoInfo. 2020. No. 4. P. 1–16 (in Russian).
  16. Mukharamova S., Saveliev A., Ivanov M., Gafurov A., Yermolaev O. Estimating the soil erosion cover-management factor at the European part of Russia. ISPRS International Journal of GeoInformation. 2021. Vol. 10. No. 10. P. 645. DOI: 10.3390/ijgi10100645.
  17. Opletaev A.S., Zhigulin E.V., Kosov V.A. Using the NDVI vegetation index to assess the state of forest plantations on disturbed land. Forests of Russia and economy in them. 2019. No. 3 (70). P. 15–23 (in Russian).
  18. Pavlova A.I. Application of digital elevation modeling methods for mapping eroded lands. Siberian Journal of Life Sciences and Agriculture. 2016. No. 2 (74). P. 159–169 (in Russian). DOI: 10.12731/wsd-2016-2-12.
  19. Rychagov G.I. General geomorphology. Moscow: Nauka, 2006. 416 p. (in Russian).
  20. Savelyeva D.A., Kalichkin V.K. Intraseasonal monitoring of water erosion of arable soils in subtaiga of Western Siberia. Achievements of Science and Technology of AIC. 2021. Vol. 35. No. 5. P. 15–21. DOI: 10.24411/0235-2451-2021-10502 (in Russian).
  21. Smirnova L.G., Narozhnaya A.G., Shamardanova E.Yu. Comparison of two methods of soil ablation calculation in catchments with GIS technology. Achievements of Science and Technology of AIC. 2012. No. 9. P. 10–12 (in Russian).
  22. Terekhin E.A. Estimation of seasonal NDVI values for the detection and analysis of crop conditions. Earth Observation and Remote Sensing. 2015. No. 1. P. 23–31. DOI: 10.7868/S0205961415010108 (in Russian).
  23. Terekhin E.A. Recognition of abandoned agricultural lands using seasonal NDVI values. Computer Optics. 2017. Vol. 41. No. 5. P. 719–725. DOI: 10.18287/2412-6179-2017-41-5-719–725 (in Russian).

For citation: Poletaev A.O. Possibilities of GIS technologies for predictive detection of areas of solid flow discharge within water protection zones. InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: MSU, Faculty of Geography, 2022. V. 28. Part 2. P. 583–596. DOI: 10.35595/2414-9179-2022-2-28-583-596 (in Russian)