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| Titel |
The creation of a digital soil map for Cyprus using decision-tree classification techniques |
| VerfasserIn |
Corrado Camera, Zomenia Zomeni, Adriana Bruggeman, Joy Noller, Andreas Zissimos |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250090762
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| Publikation (Nr.) |
 EGU/EGU2014-5017.pdf |
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| Zusammenfassung |
| Considering the increasing threats soil are experiencing especially in semi-arid,
Mediterranean environments like Cyprus (erosion, contamination, sealing and salinisation),
producing a high resolution, reliable soil map is essential for further soil conservation studies.
This study aims to create a 1:50.000 soil map covering the area under the direct control of the
Republic of Cyprus (5.760 km2).
The study consists of two major steps. The first is the creation of a raster database of
predictive variables selected according to the scorpan formula (McBratney et al., 2003). It is
of particular interest the possibility of using, as soil properties, data coming from three older
island-wide soil maps and the recently published geochemical atlas of Cyprus (Cohen et al.,
2011). Ten highly characterizing elements were selected and used as predictors in the present
study. For the other factors usual variables were used: temperature and aridity index for
climate; total loss on ignition, vegetation and forestry types maps for organic matter;
the DEM and related relief derivatives (slope, aspect, curvature, landscape units);
bedrock, surficial geology and geomorphology (Noller, 2009) for parent material and
age; and a sub-watershed map to better bound location related to parent material
sources.
In the second step, the digital soil map is created using the Random Forests package in R.
Random Forests is a decision tree classification technique where many trees, instead of a
single one, are developed and compared to increase the stability and the reliability of the
prediction. The model is trained and verified on areas where a 1:25.000 published soil maps
obtained from field work is available and then it is applied for predictive mapping to the other
areas.
Preliminary results obtained in a small area in the plain around the city of Lefkosia, where
eight different soil classes are present, show very good capacities of the method. The
Ramdom Forest approach leads to reproduce soil classes with a success percentage included
between 88% (calcaric fluvic Cambisols and vertic Cambisols) and 96% (skeletric calcaric
Regosols and calcaric lithic Leptosols).
In a parallel study, characteristic profiles are assigned to every soil class. The soil classes
will be field checked with mapped and unmapped areas including points with new soil
properties.
This study is part of the AGWATER project (AEIFORIA/GEORGO/0311(BIE)/06),
co-financed by the European Regional Development Fund and the Republic of Cyprus
through the Research Promotion Foundation.
Cohen, D.R., Rutherford, N.F., Morisseau, E., and Zissimos, A.M.: Geochemical Atlas of
Cyprus. Sydney: UNSW Press; 2011.
McBratney, A.B., Mendonça Santos, M.L., and Minasny, B.: On digital soil mapping.
Geoderma 117, 3-52, 2003
Noller, J.: The Geomorphology of Cyprus. Cyprus Geological Survey, Open File Report,
269 p, 2009. |
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