Assessment of the influence of soil erosion potential indicators according to UAV data on humus content under experimental field conditions

DOI: 10.35595/2414-9179-2024-2-30-181-191

View or download the article (Rus)

About the Authors

Natalya M. Mudrykh

FSBEI HE “Perm State Agro-Technological University named after academician D.N. Pryanishnikov”,
23, Petropavlovskaya str., Perm, 614990, Russia,
E-mail: nata020880@hotmail.com

Iraida A. Samofalova

FSBEI HE “Perm State Agro-Technological University named after academician D.N. Pryanishnikov”,
23, Petropavlovskaya str., Perm, 614990, Russia,
E-mail: samofalovairaida@mail.ru

Aleksey N. Chashchin

FSBEI HE “Perm State Agro-Technological University named after academician D.N. Pryanishnikov”,
23, Petropavlovskaya str., Perm, 614990, Russia,
E-mail: chascshin@mail.ru

Abstract

The article is devoted to studying the possibilities of digital relief modeling based on UAV survey data when analyzing the spatial distribution of humus in the conditions of an experimental field. The scale of cartographic models was 1:2 000. A key site with an area of 2.62 ha, located within the boundaries of the educational and scientific experimental field of the Perm State Agricultural and Technological University on the territory of the Perm Municipal District of the Perm Territory, was selected as the object of research. The purpose of the study is to assess the spatial distribution of humus using UAV data based on indicators of soil erosion potential. The soil cover of the surveyed area is represented by soddy-podzolic soils of heavy granulometric composition. To obtain a digital terrain model using photogrammetry methods, UAV photography was carried out using a DJI mini 2 quadcopter. Photogrammetric image processing was performed in the Drone Deploy web application. The spatial distribution of humus content in the soils of the key area was determined at 45 points. Soil samples were taken from the depth of the arable layer (0–20 cm) and analyzed in the laboratory of the Department of Agrochemistry and Soil Science of the Perm State Agricultural and Technological University according to GOST 26213-84 with colorimetric finishing. On the territory of the experimental site under study, the humus content has a pronounced spatial autocorrelation. The map of humus content was constructed using the ordinary kriging method. When visually comparing the results of digital relief modeling with a map of the spatial distribution of humus in a key area, the dependence of the increase in the content of organic matter with the values of the elevation of the area, as well as with the distance to the thalwegs (removal zone), is clearly visible. To establish the influence of relief on the humus content in soils, a raster correlation was carried out in the SAGA program, which showed a close relationship between the humus content and the distance to the thalweg and the elevation of the area (r = 0.75). Indicators characterizing moisture content and surface curvature have little effect on the variation of humus content in space.

Keywords

digital modeling, relief, UAV, humus, soil

References

  1. Buryak Z.A., Ukrainsky P.A., Gusarov A.V., Lukin S.V., Beylich A.A. Geomorphic factors influencing the spatial distribution of eroded Chernozems in automated digital soil erosion mapping. Geomorphology, 2023. V. 439. P. 108863. DOI: 10.1016/j.geomorph.2023.108863.
  2. Chashchin A.N., Pankova A.A. Modeling soil erosion based on UAV survey data. Current problems of effective use of agrochemicals and reproduction of soil fertility: Proceedings of the International Scientific and Practical Conference dedicated to the 90th anniversary of Doctor of Agricultural Sciences, Honored Worker of Agriculture of the Udmurt Republic, Honorary Worker of Higher School of the Russian Federation, Professor Alexander Stepanovich Bashkov (Izhevsk, November 15–18, 2022). Izhevsk: Udmurt State Agrarian University, 2022. P. 237–241 (in Russian).
  3. Chinilin A.V., Naumov V.D., Mikhaltsov V.S. Digital mapping of soil properties by using regression kriging: the case of RSAU-MTAA forest experimental district. News of the Timiryazev Agricultural Academy, 2018. No. 4. P. 20–31 (in Russian). DOI: 10.26897/0021-342X-2018-4-20-31.
  4. Gopp N.V., Nechaeva T.V., Savenkov O.A., Smirnova N.V., Smirnov V.V. The methods of geomorphometry and digital soil mapping for assessing spatial variability in the properties of agrogray soils on a slope. Eurasian Soil Science, 2017. V. 50. No. 1. P. 20–29 (in Russian). DOI: 10.1134/S1064229317010082.
  5. Kashtanov A.N., Vernyuk Y.I., Savin I.Y., Shchepot’ev V.V., Dokukin P.A., Sharychev D.V., Li K.A. Mapping of rill erosion of arable soils based on unmanned aerial vehicles survey. Eurasian Soil Science, 2018. V. 51. No. 4. P. 479–484 (in Russian). DOI: 10.1134/S1064229318040051.
  6. Kuznetsova A.S., Erunova M.G., Yakubailik O.E. Technologies for creating a bank of geospatial data of agricultural experimental production facility of the Federal research center KSC SB RAS. Modern problems and prospects for the development of agrochemistry, agriculture and related sciences on soil fertility and productivity of field crops in Siberia: Proceedings of the International Scientific and Production Conference with international participation (Krasnoyarsk, July 20–22, 2022). Krasnoyarsk: Federal Research Center KSC SB of Russian Academy of Sciences, 2023. P. 239–244 (in Russian). DOI: 10.52686/9785604525050_376.
  7. Larkin M.A., Gubarev D.I., Nesvetayev M.Yu., Vaigant A.A. Variation and dynamics of soil properties of Ordinary Chernozem in the Saratov Region. The Agrarian Scientific Journal, 2023. No. 10. P. 47–53 (in Russian). DOI: 10.28983/asj.y2023i10pp47-53.
  8. Samofalova I.A., Mudrykh N.M. Spatial heterogeneity of humus formation. AgroEcoInfo, 2017. No. 4 (30). Web resource: http://agroecoinfo.narod.ru/journal/STATYI/2017/4/st_434.doc (accessed 20.04.2024) (in Russian).
  9. Shikhov A.N., Gerasimov A.P., Ponomarchuk A.I., Perminova E.S. Thematic interpretation and interpretation of satellite images of medium and high spatial resolution. Perm: Perm State National Research University, 2020. 191 p. Web resource: http://www.psu.ru/files/docs/science/books/uchebnie-posobiya/shikhov-gerasimov-ponomarchuk-perminova-tematicheskoe-deshifrirovanie-i-interpretaciya-kosmicheskih-snimkov.pdf (accessed 20.04.2024) (in Russian).
  10. Skryabina O.A. The structure of the soil cover, methods of its study. Perm: Perm State Agricultural Academy, 2007. 206 p. (in Russian).
  11. Turk G.G., Karachev N.K. Use of unmanned aerial vehicles (UAVS) in geodesy. Vector of GeoSciences, 2023. V. 6. No. 2. P. 56–60 (in Russian). DOI: 10.24412/2619-0761-2023-2-56-60.

For citation: Mudrykh N.M., Samofalova I.A., Chashchin A.N. Assessment of the influence of soil erosion potential indicators according to UAV data on humus content under experimental field conditions. InterCarto. InterGIS. Moscow: MSU, Faculty of Geography, 2024. V. 30. Part 2. P. 181–191. DOI: 10.35595/2414-9179-2024-2-30-181-191 (in Russian)