New Generation of Soil Data in Slovakia – Processing and Application

1. Soil Science and Conservation Reseach Institute Department of Soil Science and Mapping New Generation of Soil Data in Slovakia – Processing and Application Jaroslava Sobocká Rastislav Skalský Juraj Balkovič Vladimír Hutár

2. 1980 1985 1990 1995 2000 2005 Soil/ladscape data for Slovakia: in time line CMS-P GFZP DPZ KPP-DB PEU-DB GCHA GDPPS • KPP-DB – Soil profile database • CMS-P – Soil Monitoring database • PEU-DB – Pedo-ecological units database • GFZP – Regional pedo-geochemical database • GCHA – Pedo-geochemical atlas database • GDPPS – Geo-referenced database of agricultural soils • DPZ – Remote sensing data (auxiliary data)

3. KPP-DBSoil profile database • Soil profile location (x,y) about 17 000 soil profiles of agricultural soils • Database tables - general soil profile atributes - genetic soil horizons attributes – morphological soil physical and chemical properties Soil profiles distribution within the territory of Slovakia and at regional level R. Skalský

4. CMS-P Soil monitoring database • Monitoring sites location (x,y), 318 sites on agricultural soilsDatabase tables – attributes for description of general soil profile properties – attributes for sequence of soil layers – morphological, chemical, physical properties – attributes for soil contamination Monitoring period– provided data in time series (5 year period sampling/recording frequency) Soil profiles distribution within the territory of Slovakia J. Kobza

5. PEU-DB Pedo-ecological units database pedo-ecological units (analogue version) • Spatial distribution of topic pedo-ecological units • soil-ecological attributes • soil production or economic attributes Spatial distribution of regional pedo-ecological unitsaccording to soil-ecological attributes B. Ilavská

6. GFPP Regional pedo-geochemical database spatial distribution of soil mapping units polygons soil profiles localization Tables general attribute data for soil profile soil horizon attribute data for surface and substrate horizon – selected soil physical and chemical properties soil contamination attributes for surface and substrate horizon – 15 risk elements Continous raster models (layers) of soil risk elements content at one-dimensional level Soil map 1:50 000 pH(H2O) J. Sobocká

7. GCHA pedo-geochemical atlas database • Soil profile localization (x, y), 5 200 points on both agricultural and forest soils table - attributes for description of general properties of soil profile - soil horizon attribute data for surface and substrate horizon – selected soil physical and chemical properties - soil contamination attributes for surface and substrate horizon – 36 risk elements • Publication – analogue interpolated maps of risk elements distribution across the Slovakia J. Čurlík, P. Šefčík

8. DPZ Remote sensing/auxiliary data Satelite images: time series from 1999, covering all territory of Slovakia (LANDSAT, SPOT, IRS) Digital ortophotomaps: covering all territory of Slovakia, valid for 2002/3, scale: 1:10 000 DEM: 30 and 50m resolution DEM for whole territory of Slovakia Interpretation example: USLE Based Erosion modelling M. Sviček, O. Rybár

9. GDPPS - Geo-referenced database of agricultural soils • New-fashioned soil database for Slovakia being built up since 2004 • Database representation of General soil survey of agricultural soils of Slovakia (in 1961 – 1970) • Modern database enabling application of wide range of pedometrics procedures Examples of analogue inputs R. Skalský

10. GDPPS -Database structure Database aproximation:raster base • Interpolated rasters, • spatial resolution 250m • applied onsoil profile data • Areal information about soil mapping units distribution • Soil profiles localization and attribute data related (same as for KPP-DB), possible number of soil profiles represented: about 200 000 • Set of continuous raster layers of soil analytical properties created for discrete depth intervals • Measured soil parameters as well as PTF/stationary models derived ones • Selected regions of Slovakia R. Skalský

11. GDPPS - Database operability proposal Soil units polygons Average soil profile Expert knowledge based processing rules Average soil attributes R. Skalský

12. 1980 1985 1990 1995 2000 2005 What are methods used in digital soil/landscape data processing in Slovakia: a short history Remote sensing data interpretation PCA, agglomerative cluster analyses Numerical taxonomy Geostatistics Fuzzy k-means GIS cartography, Expert interpretation Static/dynamic soil/landscape modelling

13. First methods and applications Juráň, C.: Numerical ordination os soils on the base of General Survey of Agricultural soils, 1984 • 127 soil profile were described by these soil properties (vectors): Texture, soil structure, stoniness, soil consistence, pH in KCl, carbonate content, humus content, CEC, neoformation presence, depth of top horizons, depth of solum • Type of data: ranking of qualitative data • Type of standardization: standard deviation • Similarity coeficient: Manhattan metric • Agglomerative strategy: Non-weighted pair-group method Horváthová, J,: Contribution to the Numerical taxonomy method for soil classification,1985 Problems of clusters validation and interpretation J. Sobocká

14. GIS cartography and expert interpretationPolygons as SOTERunit_ID in Slovakia in 1:2.5 million 76 polygons were delineated and described in Slovakia J. Sobocká

15. Soil Degradation in Central and Eastern Europe (SOVEUR) Various maps producing relating to soil degradation status SOTER database formation and application in maps J. Sobocká

16. SSCRI strategy for creation of regional pedo -geochemical maps - location GPS Topography maps Orthophotomaps Satellite images Position location of soil description refer to :global coordinates (WGS 84 – latitude B (degree), longitude L (degree) ) :national grids (S-JTSK – X (meter) Y (meter)) V. Hutár

17. SSCRI strategy for creation of regional pedogeochemical maps - Sampling strategy Reference measurement: GPS position accuracy SSRI reference station SAMPLE ACCURACY – refer to the mapping method – with regard to map scale – with regard to sample design random cluster regular V. Hutár

18. SSCRI strategy for creation of regional pedo -geochemical maps – geostatistics application Searching for spatial dependence, analyzing the basic principles in space with regard on accuracy, scale and dimension V. Hutár

19. SSCRI strategy for creation of regional pedo -geochemical maps – multivariate analyses, fuzzy k-means Analysing the multivariate objects regarding a.) linear methods (PCA) b.) unimodal methods (CA) Non-hierarical classification of multiobjects using fuzzy k-means alghoritm is used to continuously classify the real-world objects V. Hutár

20. SSCRI strategy for creation of regional pedo -geochemical maps Study case 1: Chvojnicka hilly land A (A1), B and C limit appointed in the Decree no. 531/1994-540 • respecting the absolute value • respecting the calculated value for non-standard soil • linear gradient analysis were used to findings of statistical significance of Cox and silt for heavy metals accumulation V. Hutár

21. Number of samples with exceeded concentration of risk elements V. Hutár

22. A study case 2:Fuzzy-based digital soil mapping in Považsky Inovec Mt. • Point database: • Basic inputs: • Numeric • profile description • 90 soil profiles • 5 km2 J. Balkovič & G. Čemanová

23. Genetic horizons [cm] Scheme of numeric coding of soil properties: Input matrix Features of soil genesis: Soil stratification Colour: others profile data: J.Balkovič & G. Čemanová

24. Fuzzy k-mean classification (centroids) J. Balkovič & G. Čemanová

25. 5A 5B 5C 5E 5D Interpolated rasters of membership values J. Balkovič & G. Čemanová

26. Digital diffuse soil map obtained by „pixel mixture“ technique Juraj Balkovič & Gabriela Čemanová

27. A study case 3:Digital map of potential water storage in soils (Zahorska lowland) • Inputs (source KPP): • Sand content [%] • Silt content [%] • Clay content [%] J. Balkovič, T. Orfánus & R. Skalský

28. ROSETTA Rosetta model for estimation of van Genuchten eq. parameters and validation: KPP-DB Regionally defined PTF SAND, SILT, CLAY sandy silt PF-curve: Θr, Θs, α, n J. Balkovič, T. Orfánus & R. Skalský

29. Potential water storage in soils (up to 50 cm) W = 1000 (ΘFWC - ΘWP).h[mm] ΘFWC – field water capacity ΘWP - wilting point h - soil depth [0.5 m] J. Balkovič, T. Orfánus & R. Skalský