New generation of soil data in slovakia processing and application
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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. 1980. 1985. 1990. 1995. 2000. 2005.

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New Generation of Soil Data in Slovakia – Processing and Application

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New generation of soil data in slovakia processing and application

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


Soil ladscape data for slovakia in time line

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)


Kpp db soil profile database

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


Cms p soil monitoring database

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


Peu db pedo ecological units database

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


Gfpp regional pedo geochemical database

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


Gcha pedo geochemical atlas database

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


Dpz remote sensing auxiliary data

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


Gdpps geo referenced database of agricultural soils

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


Gdpps database structure

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


Gdpps database operability proposal

GDPPS - Database operability proposal

Soil units polygons

Average soil profile

Expert knowledge based

processing rules

Average soil attributes

R. Skalský


What are methods used in digital soil landscape data processing in slovakia a short history

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


First methods and applications

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


Gis cartography and expert interpretation polygons as soterunit id in slovakia in 1 2 5 million

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


Soil degradation in central and eastern europe soveur

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


Sscri strategy for creation of regional pedo geochemical maps location

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


Sscri strategy for creation of regional pedogeochemical maps sampling strategy

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


Sscri strategy for creation of regional pedo geochemical maps geostatistics application

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


Sscri strategy for creation of regional pedo geochemical maps multivariate analyses fuzzy k means

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


Sscri strategy for creation of regional pedo geochemical maps study case 1 chvojnicka hilly land

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


New generation of soil data in slovakia processing and application

Number of samples with exceeded concentration of risk elements

V. Hutár


A study case 2 fuzzy based digital soil mapping in pova sky inovec mt

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


New generation of soil data in slovakia processing and application

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


New generation of soil data in slovakia processing and application

Fuzzy k-mean classification (centroids)

J. Balkovič & G. Čemanová


New generation of soil data in slovakia processing and application

5A

5B

5C

5E

5D

Interpolated rasters of membership values

J. Balkovič & G. Čemanová


New generation of soil data in slovakia processing and application

Digital diffuse soil map

obtained by

„pixel mixture“ technique

Juraj Balkovič & Gabriela Čemanová


A study case 3 digital map of potential water storage in soils zahorska lowland

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


New generation of soil data in slovakia processing and application

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


New generation of soil data in slovakia processing and application

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


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