The use of digital elevation models for detailed mapping of slope soils
Abstract
Formulation of the problem. The current stage of agricultural development in Ukraine requires highly informative soil maps.
One of the directions for creating such maps is the use of digital elevation models (DEM) as a spatial translator of soil characteristics.
The literary review has showed that despite the large volume of publications on this topic, a number of issues of soils digital mapping remain relevant.
The purpose of the article is to study the possibilities of using relief-ground indicative models in creation of detailed digital ground maps.
Methods. The research was conducted on the territory of the testing ground. During the field study of the landfill, it has been established that the soils are represented by chernozem typical of varying erosion degrees.
It is proposed to use a xeromorphism coefficient for the quantitative account of the landforms influence on soil formation, characterizing changes in hydrothermal conditions for a particular section of the topography, compared with the horizontal surface.
A detailed DTM was obtained, using the "Phantom 3" UAV.The derivative models of a number of topographic parameters were built on its basis later. A digital model of xeromorphism of the territory was built, transformed into a model of organic carbon (C) content.
The specified map shows initial conditions of the soil cover. It can be used as a standard to compare parameters of modern soils for assessment of extent of their degradation.
Comparison of this map with the map of actual C content has shown that average loss of C by soils of the studied area owing to anthropogenic degradation can be estimated at 5.1 kg on 1 t of the soil.
Results. Investigations have proved that the geoinformation analysis of landforms allows to quantitatively shape hydrothermal conditions of soil formation for a certain territory. The cartographic materials constructed on such a methodical approach characterize landscape potential on soil formations and reflect quasi-virgin land condition of the soil.
Scientific novelty and practical significance. The detailed soil maps, based on the results of field and laboratory soil studies, leading to potential soil assessment for DEM analysis in the article, allow to adequately estimate and objectively represent distribution of eroded and xeromorphic soils and their complexes on sloping lands.
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Obschesoyuznaya instruktsiya po pochvennyim obsledovaniyam i sostavleniuy krupnomasshtabnyih pochvennyih kart zemlepolzovaniay [All-Union instructions for soil surveys and compilation of large-scale soil maps of land use] (1973). M, Kolos, 94.
Tykhonenko D.H., HorinM. O., HeorhiA. O. ta in. (2001). Kartohrafiia gruntiv[Cartography of soils]: navch. posibnyk za red. D. H. Tykhonenka, vyd-vo Kharkivskyi derzhavnyi ahrarnyi universytet im. V.V.Dokuchaieva. Kharkiv, 321.
Achasov A.B. (2009). Gruntovo-heoinformatsiini zasady protyeroziinoi optymizatsii ahrolandshaftiv: teoriia i praktyka [Soil-geoinformation principles of anti-erosion optimization of agro-landscapes: theory and practice]: avtoref. dys. na zdobuttia nauk. stupenia dokt. sils. nauk: spets. 06.01.03 «Ahrogruntoznavstvo i ahrofizyka». Kyiv, 40.
Kostyichev P. A. (1949). Pochvyi chernozemno yoblasti Rossii, ih proishozhdenie i sostav [Soils of the chernozem region of Russia, their origin and composition]. M, Gos. izd-vo selskohoz. lit-ryi, 239.
Vyisotskiy G. N. (1906). Ob oroklimaticheskih osnovah klassifikatsiipochv [About the oroclimatic basis of soil classification]. Pochvovedenie,1, 1-18.
Demek Ya (1977). Teoriya sistem i izuchenielandshaftov [Systems theory and studied of landscapes]. Progress. M., 223.
Dzherrard A. Dzh. (1984). Pochvyi i formyirelefa [Soil and landforms]. Nedra. L., 204.
Furley P. (1971). Relationships between slope form and soil properties developed over chalk parent materials. Slopes, from and process. Ins. Br. Geograp. Spes. Publ, 3, 141-146.
Bishop, T.F.A., Minasny, B. (2005). Digital soil-terrain modelling: the predictive potential and uncertainty. In: Grunwald, S. (Ed.), Environmental Soil–Landscape Modeling: Geographic Information Technologies and Pe-dometrics. CRC Press, Boca Raton, FL, 185–213.
Aksoy, E., Özsoy, G. and Dirim, S.M. (2009) Soil Mapping Approach in GIS using Landsat Satellite Imagery and DEM Data. African Journal of Agricultural Research, 4, 1295-1302. http://www.academiciournals.org/iournal/AJAR/article-abstract/D00C9C238184
Ming, Z., Goosens, R. and Daels, L. (1993) Application of Satellite Remote Sensing to Soil and Land Use Mapping in the Rolling Hilly Areas. EARSeL Advances in Remote Sensing, 2, 34-44.
Dobos, E., Micheli, E., Baumgardner, M.F., Biehl, L. and Helt, T. (2000) Use of Combined Digital Elevation Model and Satellite Radiometric Data for Regional Soil Mapping. Geoderma, 97, 367-391. https://doi.org/10.1016/S0016-7061(00)00046-X.
Dobos, E., Montanarella, L., Negre, T. and Erika Micheli, E. (2001) A Regional Scale Soil Mapping Approach us-ing Integrated AVHRR and DEM Data. International Journal of Applied Earth Observation and Geoinformation, 3, 30-42. https://doi.org/10.1016/S0303-2434(01)85019-4
Bell, J.C., Cunningham, R.L. and Havens, M.W. (1994) Soil Drainage Class Probability Mapping using a Soil-Landscape Model. Soil Science Society of America Journal, 58, 464-470. https://doi.org/10.2136/sssai1994.03615995005800020031x
Biggs, A., Slater, B. (1998). Using soil landscape and digital elevation models to provide rapid medium scale soil surveys on the Eastern Darling Downs, Queensland. In: Proceedings of the 16th World Congress of Soil Science. Montpellier, France.
Hammer, R.D., Young, N.C., Wolenhaupt, T.L., Barney, T.L. and Haithcoate, T.W. (1995). Slope Class Maps form Soil Survey and Digital Elevation Models. Soil Science Society of America Journal, 59, 509-519. https://doi.org/10.2136/sssai1995.03615995005900020034x
Bathgate, J.D., Duram, L.A. (2003). A geographic information systems based landscape classification model to enhance soil survey: a southern Illinois case study. Journal of Soil and Water Conservation, 58 (3), 119-127.
Schmidt, J., Andrew, R. (2005). Multi-scale landform characterization. Area 37, 341-350.
MacMillan, R.A.,Martin, T.C., Earle, T.J., McNabb, D.H. (2003). Automated analysis and classification of land-forms using high-resolution digital elevation data: applications and issues. Canadian Journal of Remote Sensing, 29 (5), 592–606.
Dobos, E., Daroussin, J.,Montanarella, L. (2005). An SRTM-based procedure to delineate SOTER Terrain Units on 1:1 and 1:5 million scales, EUR 21571 EN. Office for Official Publications of the European Communities, Luxembourg, 55 .
Hengl, T., Rossiter, D.G. (2003). Supervised landform classification to enhance and replace photointerpretation in semi-detailed soil survey. Soil Science Society of America Journal, 67 (5), 1810–1822.
Moore, I.D., Gessler, P.E. and Nielson, G.A. (1993). Soil Attribute Prediction using Terrain Analysis. Soil Science Society of America Journal 57, 443-452. https://doi.org/10.2136/sssai1993.03615995005700020026x.
Gessler, P.E., Chadwick, O.A., Chamran, F., Althouse, L. and Holmes, K. (2000). Modelling Soil Landscape and Ecosystem Properties using Terrain Attributes. Soil Science Society of America Journal, 64, 2046-2056. https://doi.org/10.2136/sssai2000.6462046x.
Hammer, R.D., Young, N.C., Wolenhaupt, T.L., Barney, T.L. and Haithcoate, T.W. (1995). Slope Class Maps form Soil Survey and Digital Elevation Models. Soil Science Society of America Journal, 59, 509-519. https://doi.org/10.2136/sssai1995.03615995005900020034x.
Achasov A. B. (2006). K voprosu vliyaniya relefa na gumusirovannost chernozemov[To the question of the influ-ence of relief on the humus content of chernozem]. Pochvovedenie, 2006, 931–938.
McKenzie, N.J., Ryan, P.J. (1999). Spatial prediction of soil properties using environmental correlation. Geoderma, 89 (1–2), 67–94.
McBratney, A.B., Mendonça Santos, M.L., Minasny, B. (2003). On digital soil mapping. Geoderma, 117 (1–2), 3–52.
Kempen, B., Brus, D.J. and Stoorvogel, J.J. (2011). Three-Dimensional Mapping of Soil Organic Matter Content using Soil Type-Specific Depth Functions. Geoderma, 162, 107-123. https://doi.org/10.1016/j.geoderma.2011.01.010.
Ten Caten, A., Dalmolin, R.S.D., Pedron, F.A. and Mendonca-Santos, M.L. (2012). Spatial Resolution of a Digital Elevation Model Defined by the Wavelet Function. Pesquisa Agropecuaria Brasileira, 47, 449-457. https://doi.org/10.1590/S0100-204X2012000300018.
Yang, Ren-Min, Zhang, Gan-Lin, Yang, F., Zhi, Jun-Jun, Yang,F.,Liu, F., Zhao, Yu-Guo,Li, De-Cheng.(2016). Pre-cise estimation of soil organic carbon stocks in the northeast Tibetan Plateau. Scientific Report, 1-10. http://media.springernature.com/m685/nature-assets/srep/2016/160224/srep21842/pdf/srep21842.pdf.
Duarte de Menezes, M., Silva, S. H. G., de Mello, C. R., Owens, P. R., CuriN. (2018). Knowledge-based digital soil mapping for predicting soil properties in tworepresentative watersheds. Scientia Agricola, 75. http:// www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000200144.
Silva, S. H. G.,Owens, P. R., Silva, B. M., de Oliveira, G. C., Duarte de MenezesM., Pinto L. C., Curi N. (2015).Evaluation of Conditioned Latin Hypercube Sampling as a Support for Soil Mapping and Spatial Vari-ability of Soil Properties. Soil Science Society of America Journal, vol.79(2), 603-611. https://www.researchgate.net/publication/273521274_Evaluation_of_Conditioned_Latin_Hypercube_Sampling_as_a_Support_for_Soil_Mapping_and_Spatial_Variability_of_Soil_Properties.
Sindayihebura, A., Ottoy,S., Dondeyne, S., Van Meirvenne, M., Van Orshoven, J. (2017).Comparing digital soil mapping techniques for organic carbon and clay content: Case study in Burundi's central plateaus. Catena, 156, 161-175. https://www.sciencedirect.com/science/article/pii/S034181621730125X.
Bulyigin, S.Yu., Achasov, A.B., Lisetskiy, F.N. (2012). Ispolzovanie integralnogo analiza dannyih distantsionnogo zondirovaniya i tsifrovyih modeley relefa pri kartografirovanii pochvennogo pokrova chernozemnoy zonyi [The use of integral analysis of remote sensing data and digital elevation models for mapping the soil cover of the chernozem zone]. Nauchnyie vedomosti Belgorodskogo gosudarstvennogo universiteta. Seriya: Estestvennyie nauki. https://cyberleninka.ru/article/n/ispolzovanie-integralnogo-analiza-dannyh-distantsionnogo-zondirovaniya-i-tsifrovyh-modeley-reliefa-pri-kartografirovanii-pochvennogo.
Minasny, B., McBratney, A.B. (2006). A conditioned Latin hypercube method for sampling in the presence of ancil-lary information. Computers & Geosciences, 32 (9), 1378–1388.
Brus, D.J., Heuvelink, G.B.M. (2007). Optimization of sample patterns for universal kriging of environmental vari-ables. Geoderma, 138 (1–2), 86–95.
Moore ID, Gessler PD, Nielsen GA, Peterson GA (1992). Terrain analysis for soil-specific crop management. In proceedings book, A workshop on research and development issues, Minnesota Extension Service, 12-16 April, 27-57.
Bayramin I (2001). Using geographic information system and remote sensing techniques in making pre-soil survey. In proceedings book, 15th Int. symposium on desertification ISD. Soil Sci. Society of Turkey, 1317 June 2000, Konya, Turkey.
Lisetskiy F. N., ChepelevO. A. (2003).Klimaticheskaya obuslovlennost pochvoobrazovaniya v Tsentralnom Cher-nozeme [Climatic conditionality of soil formation in the Central Chernozem Region]. Vestnik VGU.Seriya: Geografiya i geoekologiya, 2003, 2, 15-23.
Shpedt A. A., Purlaur V. K., Mihaylenko N. V., Kuzmin P. V. (2004). Vliyanie mezorelefa na produktivnost zernovyih kultur i plodorodie chernozema Krasnoyarskoy stepi [The effect of mesorelief on the productivity of grain crops and fertility of the chernozem soil of the Krasnoyarsk steppe].Pochvovedenie, 10, 1228-1234.
Miller J. O., Galbraіth J. M., Danіels W. L. (2004). Soіl Organіc Carbon Content іn Frіgіd Southern Appalachіan Mountaіn Soіls. Soіl Scі. Soc. Am. J. 68:194, 203.
Chinilin A.V., Savin I.Yu. (2018). Krupnomasshtabnoe tsifrovoe kartografirovanie soderzhaniya organicheskogo ugleroda pochv s pomoschyu metodov mashinnogo obucheniya [Large-scale digital mapping of soil organic car-bon content with using machine learning methods]. Byul. Pochv. in-taim. V.V. Dokuchaeva, 91, 46-62, doi: 10.19047/0136-1694-2018-91-46-62.
Achasov A.B. (2008). Vykorystannia tsyfrovykh modelei reliefu pry doslidzhenni gruntovoho pokryvu [Use of digi-tal models of relief in the study of soil cover]. VisnykKhNAU. Ser. «Gruntoznavstvo, ahrokhimiia, zemlerobstvo, lisovehospodarstvo», 1, 157–159.
Siedov A.O. (2018). Mozhlyvosti vykorystannia BPLA serednoho tsinovoho sehmentu dlia kartohrafuvannia silskohospodarskykh resursiv [Possibilities of use of the UAVs of the average price segment for mapping of agri-cultural resources]. Visnyk KhNU imeni V.N. Karazkhina. Seriia «Ekolohiia», 18, 22-29.
Achasov A.B. (2006). DeyakI aspekti formalIzatsiyi gIdrotermIchnih umov gruntoutvorennya [Some aspects of the formalization of hydrothermal conditions of soil formation]. Visnik agrarnoyi nauki, 9, 17-21.
Achasov A. B. (2006). K voprosu vliyani yarelefa na gumusirovannost chernozemov [To the question of the influ-ence of relief on the humus content of chernozem]. Pochvovedenie, 9, 931-938.
Polupan M. I., Solovei V. B., Velychko V. A. (2005). Klasyfikatsiia gruntiv Ukrainy [Classification of soils of Ukraine]: za redaktsiieiu doktora silskohospodarskykh nauk M. I. Polupana. NNTs "Instytut hruntoznavstva ta ahrokhimii imeni O. N. Sokolovskoho, Kyiv.
