High resolution digital soil mapping in the Vaskereszt forest reserve

Illés Gábor; Kovács Gábor; Heil Bálint

Bulletin of Forestry Science / Volume 1 / Issue 1 / Pages 29-43
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Gábor Illés, Gábor Kovács & Bálint Heil

Correspondence

Correspondence: Illés Gábor

Postal address: H-1277 Budapest, Pf. 17.

e-mail: illesg[at]erti.hu

Abstract

Using the digital soil mapping methods we made the soil map of Vaskereszt forest reserve. Soil samples were collected applying stratified random sampling. 138 sample sites were appointed where soil-types were determined. We used the digital elevation model, soil data, and geological data in order to produce soil map. To predict soil information for the areas between sample points general discriminant-, classification tree, and artificial neural network analysis were applied, in which relief and geological variables were predictors. Soil map was developed first using each method separately, and second using them simultaneously. Their prediction accuracies were compared. We concluded that these methods are able to derive soil maps however the classification accuracies are uneven, ranging between 66-92%. The soil map that was derived by the joint application of the three methods obtained 10% improvement in the overall accuracy.

Keywords: digital soil mapping, spatial prediction, forest reserve

References21

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Bakacsi, Zs.; Kuti, L.; Pásztor, L.; Vatai, J.; Szabó, J. and Müller, T. 2010: Method for the compilation of a stratified and harmonized soil physical database using legacy and up-to-date data sources. Agrokémia és Talajtan 59: 39–46. DOI: 10.1556/Agrokem.59.2010.1.5

Behrens, T.; Förster, H.; Scholten, T.; Steinrücken, U.; Spies, E-D. and Goldschmitt, M. 2005: Digital soil mapping using artificial neural networks. Journal of Plant Nutrition and Soil Science 168: 21–33. DOI: 10.1002/jpln.200421414

Behrens, T. and Scholten, T. 2007: A comparison of data-mining techniques in predictive soil mapping. In: Lagacherie et al (eds): Digital Soil Mapping an Introductory Perspective. Developments in Soil Science 31: 353–365. DOI: 10.1016/s0166-2481(06)31025-2

Behrens, T.; Schmidt, K. and Scholten, T. 2008: An approach to removing uncertainties in nominal environmental covariates and soil class maps. In: Hartemink, A.E.; McBratney, A.B. and Mendonça Santos, M.L. (eds): Digital Soil Mapping withlimited data. Springer. 213–224. DOI: 10.1007/978-1-4020-8592-5_18

Bishop, T.F.A.; Minasny, B. and McBratney, A.B. 2006: Uncertainty analysis for soil-terrain models. International Journal of Geographical Information Science 20 (2): 117–134. DOI: 10.1080/13658810500287073

Broyden, C.G. 1970: The convergence of a class of double-rank minimization algorithms. Journal of the Institute of Mathematics and Its Applications. 6: 76–90. DOI: 10.1093/imamat/6.1.76

Carré, F. and Boettinger, J.L. 2008: Synthesis and priorities for future work in digital soil mapping. In: Hartemink, A.E.; McBratney, A.B. and Mendonça Santos, M.L. (eds): Digital Soil Mapping with limited data. Springer. 399–403. DOI: 10.1007/978-1-4020-8592-5_35

Dobos, E. and Hengl, T. 2009: Soil Mapping Applications. In: Hengl, T., and Reuter, H.I. (eds): Geomorphometry Concepts, Software, Applications. Elsevier. 461–479. DOI: 10.1016/s0166-2481(08)00020-2

Giasson, E.; Clarke, R.T.; Inda, A.V.; Merten, G.H. and Tornquist, C.G. 2006: Digital soil mapping using multiple logistic regressions on terrain parameters in Southern Brazil. Scientia Agricola 63 (3): 262–268. DOI: 10.1590/s0103-90162006000300008

Hengl, T. and Reuter, H.I. (eds) 2007: Geomorphometry Concepts, Software, Application. Developments in Soil Science 33: 765 pp.

Jenness, J. 2006: Topographic Position Index (tpi_jen.avx) extension for ArcView 3.x, v. 1.3a. Jenness Enterprises. [Online] URL

Lagacherie, P.; McBratney, A.B. and Voltz, M. (eds) 2007: Digital Soil Mapping an Introductory Perspective. Developments in Soil Science 31: 595 p. DOI: 10.1016/s0166-2481(06)x3100-8

Lagacherie, P. 2008: Digital Soil Mapping: State of the Art. In: Hartemink, A.E.; McBratney, A.B. and Mendonça Santos, M.L. (eds): Digital Soil Mapping with limited data. Springer. 3–15. DOI: 10.1007/978-1-4020-8592-5_1

Mayr, T.R. and Palmer, B. 2007: Digital Soil Mapping: An England and Wales perspective. In: Lagacherie et al (eds): Digital Soil Mapping an Introductory Perspective. Developments in Soil Science 31: 365–377. DOI: 10.1016/s0166-2481(06)31026-4

McBratney, A.B.; Mendonça Santos, M.L. and Minasny, B. 2003: On digital soil mapping. Geoderma 117: 3–52. DOI: 10.1016/S0016-7061(03)00223-4

Minasny, B. and McBratney, A.B. 2007a: Spatial prediction of soil properties using EBLUP with the Matern covariance function. Geoderma 140 (4): 324–336. DOI: 10.1016/j.geoderma.2007.04.028

Minasny, B. and McBratney, A.B. 2007b: Incorporating taxonomic distance into spatial prediction and digital mapping of soil classes. Geoderma 142: 285–293. DOI: 10.1016/j.geoderma.2007.08.022

Pásztor, L.; Szabó, J. and Bakacsi, Zs. 2010: Digital processing and upgrading of legacy data collected during the 1:25.000 scale Kreybig soil survey. Acta Geodaetica et Geophysica Hungarica 45: 127–136. DOI: 10.1556/ageod.45.2010.1.18

Scull, P.; Franklin, J.; Chadwick, O.A. and McArthur, D. 2003: Predictive Soil Mapping: a review. Progress in Physical Geography 27 (2): 171–197. DOI: 10.1191/0309133303pp366ra

Smith, M.P.; Zhu, A.X.; Burt, J.E. and Stiles, C. 2006: The effects of DEM resolution and neighborhood size on digital soil. Geoderma 137 (1–2): 58–69. DOI: 10.1016/j.geoderma.2006.07.002

Zhu, J.; Morgan, C.L.S.; Norman, J.M.; Yue Wei and Lowery, B. 2004: Combined mapping of soil properties using a multiscale tree-structured spatial model. Geoderma 118: 321–334. DOI: 10.1016/s0016-7061(03)00217-9

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Illés, G., Kovács, G. & Heil, B. (2011): High resolution digital soil mapping in the Vaskereszt forest reserve. Bulletin of Forestry Science, 1(1): 29-43. (in Hungarian)

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Volume 1, Issue 1
Pages: 29-43

First published:
1 September 2011

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