Abstract
In this chapter we present an automated approach to correct the delineation of nominal soil and environmental datasets based on auxiliary metric attributes, aiming to enhance positional accuracy. The detection of uncertainties is based on different spatial and non-spatial approaches. The methodological framework mainly consists of nearest neighbour approaches and comprises supervised feature selection, different ensemble classification techniques, as well as spatial and non-spatial smoothing and generalization approaches. The method is described and applied to an artificial dataset as well as a 1:50 000 German soil map and a 1:1 000 000 geological map of the Republic of Niger.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Andonova, S., Elisseeff, A., Evgeniou, T., Pontil, M., 2002. A simple algorithm for learning stable machines. In: F. van Harmelen (Ed.), Proceedings of the Fifteenth Eureopean Conference on Artificial Intelligence. IOS Press, Amsterdam. pp. 513–517.
Akkus, A., Güvenir, H.A., 1996. K nearest neighbor classification on feature projections. In: L. Saitta (Ed.), Machine Learning: Proceedings of the Thirteenth International Conference. Morgan Kaufmann, San Francisco, CA. pp. 12–19.
Alpaydin, E., 1997. Voting over multiple condensed nearest neighbors. Artificial Intelligence Review 11, 115–132.
Bay, S.D., 1999. Nearest neighbor classification from multiple feature subsets. Intelligent Data Analysis 3, 191–209.
Behrens, T., 2003. Digitale Reliefanalyse als Basis von Boden-Landschafts-Modellen – am Beispiel der Modellierung periglaziärer Lagen. Boden und Landschaft 42, 189p. Giessen.
Behrens, T., Föcken, U., Spies, E.-D., Goldschmitt, M., 2005. Digital soil mapping using artificial neural networks. Journal of Plant Nutrition and Soil Science 168, 21–33.
Behrens, T., Scholten, T., 2006a. Digital soil mapping in Germany – a review. Journal of Plant Nutrition and Soil Science 169, 434–443.
Behrens, T., Scholten, T., 2006b. Chapter 25. A comparison of data-mining techniques in predictive soil mapping. In: Lagacherie, P., McBratney, A.B., Voltz, M. (Eds.), Digital Soil Mapping: An Introductory Perspective. Developments in Soil Science, Vol. 31. Elsevier, Amsterdam. pp. 353–364.
Bishop, T.F.A., Minasny, B., McBratney, A.B., 2006. Uncertainty analysis for soil-terrain models. International Journal of Geographical Information Science 20, 117–134.
Breiman, L., 1996. Bagging predictors. Machine Learning 24, 123–140.
Breiman, L., 2001. Random forests. Machine Learning 45, 5–32.
Brighton, H., Mellish, C., 2002. Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery 6, 153–172.
Bühlmann, P., Yu, B., 2002. Analyzing bagging. Annals of Statistics 30, 927–961.
Burrough, P.A., McDonnell, R.A., 1998. Principles of Geographical Information Systems. Oxford University Press, New York.
Dietrich, W.E., Montgomery, D.R., 1998. SHALSTAB: a digital terrain model for mapping shallow landslide potential, NCASI (National Council of the Paper Industry for Air and Stream Improvement), Corvallis OR. Technical Report, 29p.
Fix, E., Hodges, J.L., 1951. Discriminatory analysis – nonparametric discrimination: consistency properties. Technical Report Project 21-49-004, Report Number 4, USAF School of Aviation Medicine, Randolf Field, Texas.
Freund, Y., Schapire, R.E., 1996. Experiments with a new boosting algorithm, In: L. Saitta (Ed.), Machine Learning: Proceedings of the Thirteenth International Conference. Morgan Kaufmann, San Francisco, CA, pp. 148–156.
Greigert, J., 1961. Réologique de reconnaissance du Bassin des Iullemeden 1:1 Mio. BRGM, Niamey, Niger.
Hamamoto, Y., Uchimura, S., Tomita, S., 1997. A bootstrap technique for nearest neighbor classifier design. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 73–79.
Herrmann, L., Vennemann, K., Stahr, K., von Oppen, M., 1999. Atlas of Natural and Agronomic Resources of Niger and Benin. http://www.uni-hohenheim.de/∼atlas308/startpages/page2/ english/content/title_en.htm.
Heuvelink, G.B.M., 1998, Error Propagation in Environmental Modelling with GIS. Taylor & Francis, London, 144p.
HLUG (Hessische Landesamt für Umwelt und Geologie), 2002. Erläuterungen zur Bodenkarte von Hessen 1:50000. HLUG, 575p.
Ho, T.K., 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 832–844.
Jenson, S.K., Domingue, J.O., 1988. Extracting topographic structure from digital elevation data for Geographic Information System analysis. Photogrammetric Engineering and Remote Sensing 54, 1593–1600.
Kira, K., Rendell, L., 1992. A practical approach to feature selection. In D.H. Sleeman, P. Edwards (Eds.), Proceedings of the Ninth International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA, pp. 249–256.
Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J., 1995. The Learning Vector Quantization Program Package, Version 3.1. http://www.cis.hut.fi/research/lvq_pak/.
Kononenko, I., 1994. Estimating attributes: analysis and extensions of RELIEF. In: L. De Raedt, F. Bergadano, (Eds.), European Conference on Machine Learning. Springer, Heidelberg, pp. 171–182.
Lagacherie, P., Holmes, S., 1997. Addressing geographical data errors in a classification tree soil unit prediction. International Journal of Geographical Information Science 11, 183–198.
Liu, H., Motoda, H., 1998. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Norwell, MA, 244p.
Liu, H., Motoda, H., 2001. Instance Selection and Construction for Data Mining. Kluwer Academic Publishers, Norwell, MA, 448p.
McBratney, A.B., Mendonça Santos, M.L., Minasny, B., 2003. On digital soil mapping. Geoderma 117, 3–52.
Perkal, J., 1966. An Attempt at Objective Generalization. Geodéfia, 7(2), 130–142.
Robinson, A.H., Sale, R.D., Morrison, J.L., Muehrcke, P.C., 1984. Elements of Cartography, 5th edition. John Wiley & Sons, New York.
Schmidt, K., Behrens, T., Scholten, T., 2008. Instance selection and classification tree analysis for large spatial datasets in digital soil mapping. Geoderma (accepted).
Skurichina, M., Duin, R.P.W., 2001. Bagging and the random subspace method for redundant feature spaces. In: J. Kittler, F. Roli (Eds.), Multiple Classifier Systems, Proceedings Second International Workshop MCS 2001 (Cambridge, UK, July), Lecture Notes in Computer Science, Vol. 2096. Springer Verlag, Berlin, pp. 1–10.
Wilson, D.L., 1972. Asymptotic properties of nearest neighbour rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics 2, 408–420.
Zeidat, N., Wang, S., Eick, C., 2005. Dataset Editing Techniques: A Comparative Study: Department of Computer Science, University of Houston. http://www.cse.unr.edu/bebis/ cs679/PaperPresentations/ZWE05.pdf.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Behrens, T., Schmidt, K., Scholten, T. (2008). An Approach to Removing Uncertainties in Nominal Environmental Covariates and Soil Class Maps. In: Hartemink, A.E., McBratney, A., Mendonça-Santos, M.d. (eds) Digital Soil Mapping with Limited Data. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8592-5_18
Download citation
DOI: https://doi.org/10.1007/978-1-4020-8592-5_18
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-8591-8
Online ISBN: 978-1-4020-8592-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)