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Email this article Digital Terrain Model Derivatives Analysis with the Aim of Identifying Specific Soil Types in Young Post-Glacial Topography with a Vector Approach
Abstract
This article discusses a study conducted in order to analyse selected Digital Terrain Model (DTM) derivates in diverse young post-glacial topographic profiles with the aim of identifying terrain features that could be related to the soils that formed there. The area under investigation is within the reach of the youngest Vistulian Glaciation, in the north-east of Poland. The main goal of the study was to reveal indirect relationships between a lithological soil type and terrain forms, which transpire from DTM derivatives. This can directly help to assign the type of soil in the area to one of the three soil types: a) made of sand, b) made of loam, c) wet-soils. The starting point for the research undertaken was the landscape approach to soil modelling and the article deals with medium scales. Derivatives were analysed using vector data notation, focusing on selected derivative values and their spatial location in relation to one another. The results obtained indicate the possibility of using this approach as an auxiliary approach in soil mapping of areas for which the quality of source materials (such as precipitation geometry) is low. Thus, they can be of assistance in improving the existing soil maps of selected scales. The trend revealed in the obtained results of DTM analysis can be considered as a contribution to realisation of assumptions of a study in digital soil mapping with the use of selected methods of AI.
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Behrens, T., Karsten, S., MacMillan, R.A., Rossel, A., 2018. Multi-Scale Digital Soil Mapping with Deep Learning. Scientific Reports, 8, article no. 15244.
Bell, J.C., Cunningham, R.L., Havens, M.W., 1992. Calibration and Validation of a Soil-Landscape Model for Predicting Soil Drainage Class. Soil Society of America Journal, 56(6): 1860–1866.
Bell, J.C., Cunningham, R.L., Havens, M.W., 1994. Soil Drainage Class Probability Mapping Using a Soil-Landscape Model. Soil Society of America Journal, 58(2): 464–470.
Białousz, S., 1969. The Impact of the Morphogenesis of the Masurian Lake District on the Formation of Soils and the Conclusions for the Geodetic Shaping of the Boundaries of Agricultural Economy Units (in Polish), PhD dissertation.
Białousz, S., 2015. Support in Updating and Harmonization of the European Soil Database and Map. Joint Research Centre, European Commission, ISPRA, pp. 1–101.
Debella-Gilo, D., Etzelmüller, B., 2009. Spatial Prediction of Soil Classes Using Digital Terrain Analysis and Multinomial Logistic Regression Modeling Integrated in GIS: Examples from Vestfold County, Norway. CATENA, 77(1): 8–18.
Deumilch, D., 2010. A Multiscale Soil-Landform Relationship in the Glacial-Drift Area Based on Digital Terrain Analysis and Soil Attributes. Journal of Plant Nutrition and Soil Science, 173(6): 843–851.
Dobos, E., Micheli, E., Baumgardner, M., Biehl, L., Helt, T., 2000. Use of Combined Digital Elevation Model and Satellite Radiometric Data for Regional Soil Mapping. Geoderma, 97(3–4): 367–391.
Dobos, E., Montanarella, L., Nègre, T., Micheli, E., 2001. A Regional Scale Soil Mapping Approach Using Integrated AVHRR and DEM Data. International Journal of Applied Earth Observation and Geoinformation, 3(1): 30–42.
Dobos, E., Carré, F., Hengl, T., Reuter, H.I., Tóth, G., 2006. Digital Soil Mapping as a Support to Production of Functional Maps. EUR 22123 EN, p. 68. Offie for Offiial Publications of the European Communities, Luxemburg.
Dobos, E., Norman, B., Worstell, B., et al. 2002. The Use of DEM and Satellite Data for Regional Scale Soil Databases. Agrokémiaés Talajtan, 51(1–2): 263–272.
Ellili-Bargaoui, Y., Malone, B.P., Michot, D., Minasny, B., Vincent, S., Walter, C., Lemercier, B., 2020. Comparing Three Approaches of Spatial Disaggregation of Legacy Soil Maps Based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classifiation Trees (DSMART) Algorithm. SOIL, 6: 371–388.
Gerrard, A.J., 1992. Soil Geomorphology. Chapman and Hall, London.
Gessler, P.E., Moore, I.D., McKenzie, N.J., Ryan, P.J., 1995. Soil-Landscape Modelling and Spatial Prediction of Soil Attributes. International Journal of Geographical Information Systems, 9(4): 421–432.
Gessler, P.E., Chadwick, O.A., Chamran, F., Althouse, L., Holmes, K., 2000. Modeling Soil-Landscape and Ecosystem Properties Using Terrain Attributes. Soil Science Society of America Journal, 64(6): 2046–2056.
Jenny, H., 1941. Factors of Soil Formation: A System of Quantitative Pedology. McGraw-Hill Book Company, Inc., New York.
Malone, B.P., Minasny, B., Mcbratney, A.B., 2009. Mapping Continuous Soil Depth Functions in the Edgeroi District, NSW, Australia, Using Terrain Attributes and Other Environmental Factors. Proceedings of Geomorphometry.
Miklaszewski, S., 1901. Soils of Polish Lands (in Polish).
Moore, I.D., Nielsen, G.A.E., Peterson, G.A., Gessler, P.E., 1993. Soil Attribute Prediction Using Terrain Analysis. Soil Science Society of America Journal, 57(2): 443–452.
Qiyong, Y., Zhang, F., Jiang, Z., Li, W., Zhang, J., Zeng, F., Li, H., 2014. Relationship Between Soil Depth and Terrain Attributes in Karst Region in Southwest China. Journal of Soils and Sediments, 14: 1568–1566.
Radło-Kulisiewicz, M., 2019. The Use of DTM Derivatives in Modeling Soil Cover in a Young Glacial Landscape for a Database of Soils with a Level of Generalization Corresponding to 1:250 000 Scale Maps (in Polish), PhD dissertation, Ofiyna Wydawnicza PW, Warszawa.
Schillaci, C., Braun, A., Kropáček, J., 2015. Terrain Analysis and Landform Recognition. In: Geomorphological Techniques, L. Clarke, J. Nield (eds.). British Society for Geomorphology (online edition).
Stahler, A.N., 1957. Quantitative Analysis of Watershed Geomorphology. EOS Sciences News.
Thomas, A.L., King, D., Dambrine, E., Couturier, A., Roque, J., 1999. Predicting Soil Classes with Parameters Derived from Relief and Geologic Materials in a Sandstone Region of the Vosges Mountains (Northeastern France). Geoderma, 90(3–4): 291–305.
Vaysse, K., Lagacherie, P., 2017. Using Quantile Regression Forest to Estimate Uncertainty of Digital Soil Mapping Products. Geoderma, 291: 55–64.
Wadoux, A., Brus, D.J., Heuvelink, G.B.M., 2019a. Sampling Design Optimization for Soil Mapping with Random Forest. Geoderma, 355.
Wadoux, A., Padarian, J., Minasny, B., 2019b. Multi-Source Data Integration for Soil Mapping Using Deep Learning. SOIL, 5(1): 107–119.
Wilson, J., Gallant, J. (eds.), 2000. Terrain Analysis. Principles and Applications. John Wiley & Sons, Inc.
Ziadat, F.M., 2005. Analyzing Digital Terrain Attributes to Predict Soil Attributes for a Relatively Large Area. Soil Science Society of America Journal, 69(5): 1590–1599.
DOI: http://dx.doi.org/10.17951/pjss.2021.54.1.123-138
Date of publication: 2021-06-29 19:03:10
Date of submission: 2021-03-19 12:51:51
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