The scale levels identification for the plowland topography organization
https://doi.org/10.19047/0136-1694-2019-96-3-21
Abstract
The identification of factor and indicational features, which are characterized by the high informativity and field of view in relation to the soil cover organization, plays a very important role in the soil mapping. Such characteristics are more common for Unmanned Aerial Vehicles (UAV), which include spectrazonal imagery and digital elevation model (DEM) with ultrahigh spatial resolution, necessary for obtaining fine and large scale images. However, the agrogenic micro- and nanotopography is considered as a noise during the studies of the soil cover topographic differentiation under the conditions of plowland, as the genetic soil properties correlate with natural micro- and mesotopography. A filtration algorithm for the land surface roughness, which is not related to the spatial organization of the objective soil properties, is suggested in the paper. The stages of linear dimension identification for self-similar structures of the glacial and agrogenic topography based on two-dimensional Fourier decomposition are demonstrated using the example of a field topography digital model for the area of 125 hectares. Filtering in the frequency domain allowed restoring the natural field topography and substantiating the effective resolution of the DEM and the size of the area to calculate local morphometric specificities of the topography for digital soil mapping.
About the Authors
N. V. Minayev
Russian State Agrarian University - Moscow Timiryazev Agricultural Academy
Russian Federation
Russian Federation
127550, Moscow, Timiryazevskaya str., 49
A. A. Nikitin
Skolkovo Institute of Science and Technology
Russian Federation
Russian Federation
143026, Moscow, Nobel’ str., 3
D. N. Kozlov
V.V. Dokuchaev Soil Science Institute
Russian Federation
Russian Federation
119017, Moscow, Pizhevskiyper., 7, build. 2
References
1. Alifanov V.M., Gugalinskaya L.A., Paleokriogenez i struktura pochvennogo pokrova Russkoi ravniny (Paleocryogenesis and soil cover structure of the Russian Plain), Pochvovedenie, 1993, No. 7, pp. 65-75.
2. Vedyushkin M.A., O fraktal'nom podkhode k opisaniyu prostranstvennoi struktury rastitel'nykh soobshchestv (On the fractal approach to the description of the spatial structure of plant communities), In: Problemy monitoringa i modelirovaniya dinamiki lesnykh ekosistem (Problems of monitoring and modeling the dynamics of forest ecosystems), Moscow: AO “Zhumal Ekos-inform”, 1995, pp. 182-201.
3. Davis J.C., Statisticheskii analiz dannykh v geologii (Statistics and data analysis in geology), Vol. 2, Moscow: Nedra, 1990, 427 p.
4. Eremenko E.A., Panin A.V., Lozhbinnyi mezorel'ef Vostochno-Evropeiskoi ravniny (Hollow mezorelief of the East European plain), Moscow: MIROS, 2010, 192 p.
5. Zakharov V.S., Samopodobie struktur i protsessov v litosfere po rezul'tatam fraktal'nogo i dinamicheskogo analiza: Dis. dokt. geol.-mineral. nauk (Self-similarity of structures and processes in the lithosphere by the results of fractal and dynamic analysis, Dr. geol.-mineral. sci. thesis), Moscow: Lomonosov Moscow State University, 2014, 281 p.
6. Kiryushin V.I., Kiryushin S.V., Agrotekhnologii (Agrotechnologies). Saint Petersburg: Lan', 2015, 464 p.
7. Kotlov I.P., Puzachenko Yu.G., The structure of the relief of the Russian Plain as a landscape forming factor, Landscape Study - Theory, Methods, Regional Studies, Practice, Proc. of the XI Landscape Conf, Moscow, Geogr. fak., Lomonosov Moscow State University, 2006, pp. 166-172. Available at: http://www.landscape.edu.ru/files/landscapeplanning2006.pdf.
8. Makeev A.O., Dubrovina I.V., Geografiya, genezis i evolyutsiya pochv Vladimirskogo opol'ya (Genesis, geography and evolution of the Vladimirskoe Opolye soils), Pochvovedenie, 1990, No. 7, pp. 5-26.
9. Puzachenko Yu.G., Onufrenya I.A., Analiz ierarkhicheskoi organizatsii rel'efa (Analyses of relief hierarchic organization), Izvestiya RAN, Seriya geograficheskaya, 2002, No. 4, pp. 29-38.
10. Rubtsova L.P., O genezise pochv Vladimirskogo opol'ya (Genesis of the soils in Vladimirskoe Opolye), Pochvovedenie, 1974, No. 6, pp. 17-27.
11. Savin I.Yu., Vernyuk Yu.I., Feraslis I., Vozmozhnosti ispol'zovaniya bespilotnykh letatel'nykh apparatov dlya operativnogo monitoringa produktivnosti pochv (Possible use of pilotless aircrafts for operative monitoring of the soil productivity), Dokuchaev Soil Bulletin, 2015, Vol. 80, pp. 47-55.
12. Shinkevich M.V., Vorob'eva N.G., Altyntsev M.A., Popov R.A., Arbuzov S.A., Florov A.V., Otsenka tochnosti plotnoi tsifrovoi modeli poverkhnosti i ortofotoplanov, poluchennykh po materialam aerofotos’emki s BLA serii Supercam (Estimation of the accuracy of a dense digital elevation model and orthophotos obtained from aerial photography with UAV Supercam series), Geomatika, 2015, No. 4, pp. 37-41.
13. Yakushev V.P., Lekomtsev P.V., Matveenko D.A., Petrushin A.F., Yakushev V.V., Primenenie distantsionnogo zondirovaniya v sisteme tochnogo zemledeliya (Application of remote sensing in the precision farming system), Vestnik Rossiiskoi akademii sel'skokhozyaistvennykh nauk, 2015, No. 1, pp. 23-25.
14. Advances in Digital Terrain Analysis, Zhou Q., Lees B., Tang G., (Eds.), 2008, XIV, 462 p.
15. Akar A., Evaluation of accuracy of dems obtained from uav-point clouds for different topographical areas, Int. J. Eng. Geo., 2017, Vol. 2, pp. 110-117, DOI: 10.26833/ijeg.329717.
16. Brogi C., Huisman J.A., Patzold S., von Hebel C., Weihermuller L., Kaufmann M.S., van der Kruk J., Vereecken H., Large-scale soil mapping using multi-configuration EMI and supervised image classification, Geoderma, 2019, Vol. 335, pp. 133-148, DOI: 10.1016/i.geoderma.2018.08.001.
17. Capolupo A., Pindozzi S., Okello K., Fiorentino N., Boccia L., Photogrammetry for environmental monitoring: The use of drones and hydrological models for detection of soil contaminated by copper, Sci. Total Environm., 2015, Vol. 514, pp. 298-306.
18. De Reu J., Bourgeois J., Bats M., Zwertvaegher A., Gelorini V., De Smedt P., Chu W., Antrop M., De Maeyer P., Finke P., Van Meirvenne M., Verniers J, Crombe P., Application of the topographic position index to heterogeneous landscapes, Geomorphology, 2013, Vol. 186, pp. 39-49.
19. Florinsky I.V., Digital Terrain Analysis in Soil Science and Geology (SecondEdition), Academic Press, 2016, 506 p.
20. Gruijter J.J. de, McBratney A.B., Minasny B., Wheeler I., Malone B.P., Stockmann U., Farm-scale soil carbon auditing, Geoderma, 2016, No. 265. pp. 120-130, DOI: 10.1016/i.geoderma.2015.11.010.
21. Guenette K.G., Hernandez-Ramirez G., Tracking the influence of controlled traffic regimes on field scale soil variability and geospatial modeling techniques, Geoderma, 2018, Vol. 328, pp. 66-78, DOI: 10.1016/i.geoderma.2018.04.026.
22. Eltnera A., Baumgart P., Maas H.-G., Faust, D., Multi-temporal UAV data for automatic measurement of rill and interrill erosion on loess soil, Earth Surface Processes and Landforms, 2014, Vol. 40, pp. 741-755, DOI: 10.1002/esp.3673.
23. Eltnera A., Maasa H.-G, Faus D., Soil micro-topography change detection at hillslopes in fragile Mediterranean landscapes, Geoderma, Vol. 313, 2018, pp. 217-232, DOI: 10.1016/i.geoderma.2017.10.034.
24. Nex F., Remondino F., UAV for 3Dmapping applications: a review, Appl. Geomat, 2014, Vol. 6(1). pp. 1-15, DOI: 10.1007/S12518-013-0120-X.
25. Marijan Car, Danijela June Kacunic, Meho-Sasa Kovacevie., Application of Unmanned Aerial Vehicle for Landslide Mapping, New Technologies in Engineering Geodesy and Multisensor Systems, 2016, TS6, pp. 549-560.
26. Minasny B., McBratney A., Digital soil mapping: A brief history and some lessons, Geoderma, 2016, Vol. 264, pt. B, pp. 301-311.
27. Pierrot-Deseilligny M., De Luca L., Remondino F., Automated image-based procedures for accurate artifacts 3D modeling and orthoimage generation, Geoinform.sFCE CTU J, 2011, Vol. 6, pp. 291-299.
28. Ruzgiene B., Berteska T., Gecyte S., Jakubauskiene E., Aksamitauskas V.C., The surface modelling based on UAV Photogrammetry and qualitative estimation, Measurement, 2015, Vol. 73, pp. 619-627, DOI: 10.1016/i.measurement.2015.04.018.
29. ТигсоИе D., Fractals and chaos in geology and geophysics, Cambridge University Press, 1992, 221 p.
30. Zeybeka M., §anlioglu I., Point cloud filtering on UAV based point cloud, Measurement, 2019, Vol. 133, pp. 99-111, DOI: 10.1016/i.measurement.2018.10.013.
31. Wieland R., Dalchow C., Detecting landscape forms using Fourier transformation and singular value decomposition (SVD), Computers & Geosciences, 2009, Vol. 35, pp. 1409-1414.
32. Weiss A.D., Topographic position and landforms analysis, Poster Presentation, ESRI Users Conference, San Diego, CA, 2001, URL: http://www.iennessent.com/downloads/tpi-poster-tnc18x22.pdf.
33. Woodget A.S., Carbonneau P.E., Visser F., Maddock I.P., Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry, Earth Surf. Process and Landforms, 2015, Vol. 40, pp. 47-64, DOI: 10.1002/esp.3613.
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For citations:
Minayev N.V., Nikitin A.A., Kozlov D.N. The scale levels identification for the plowland topography organization. Dokuchaev Soil Bulletin. 2019;(96):3-21. https://doi.org/10.19047/0136-1694-2019-96-3-21
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