New Generation of Soil Data in Slovakia – Processing and Application

Jaroslava Sobocká Rastislav SkalskýJuraj BalkovičVladimír Hutár

Soil Science and Conservation Reseach Institute Department of Soil Science and Mapping

Soil/ladscape data for Slovakia: in time line

1980 1985 1990 1995 2000 2005

KPP-DB PEU-DB

CMS-P

GFZP

GCHA

DPZ

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

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 Monitoring sites location (x,y),

318 sites on agricultural soils Database tables

– attributes for description of general soil profile properties

– attributes for sequence of soil layers – morphological, chemical, physical properties

– attributes for soil contaminationMonitoring 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 pedo-ecological

units (analogue version)Spatial

distribution of topic pedo-ecological units• soil-ecological attributes• soil production or economic attributesSpatial distribution of regional pedo-ecological unitsaccording to soil-ecological attributes

B. Ilavská

GFPP Regional pedo-geochemical database

spatial distribution of soil mapping units polygons

soil profiles localization Tablesgeneral 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

pH(H2O)

Soil map 1:50 000

J. Sobocká

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

Digital ortophotomaps: covering all territory of Slovakia, valid for 2002/3, scale: 1:10 000

Satelite images: time series from 1999, covering all territory of Slovakia (LANDSAT, SPOT, IRS)

DEM: 30 and 50 m resolution DEM for whole territory of Slovakia

Interpretation example: USLE Based Erosion modelling M. Sviček, O. Rybár

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

• 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

R. Skalský

Database aproximation: raster base

• Interpolated rasters, • spatial resolution 250m • applied on soil profile data

• 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

GDPPS - Database operability proposal

Soil units polygons

Average soil profile

Expert knowledge based

processing rulesAverage 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

PCA, agglomerative cluster analyses

Numerical taxonomy

GIS cartography, Expert interpretation

Geostatistics

Fuzzy k-means

Remote sensing data interpretation

Static/dynamic soil/landscape modelling

First methods and applications

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

Juráň, C.: Numerical ordination os soils on the base of General Survey of Agricultural soils, 1984Horváthová, J,: Contribution to the Numerical taxonomy method for soil classification,1985

Problems of clusters validation and interpretation

J. Sobocká

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)

SOTER database formation and application in maps

J. Sobocká

Various maps producing relating to soil degradation status

SSCRI strategy for creation of regional pedo -geochemical maps - location

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))

Satellite images

Topography maps

Orthophotomaps

GPS

V. Hutár

2.49rmsey

3.49rmse

x

4.29

rmsexy

vzdialenosť [m]

-25

-20

-15

-10

-5

0

5

10

15

20

25

-15 -10 -5 0 5 10 15v

X

Y

-40

-30

-20

-10

0

10

20

30

40

1

3601

7201

1080

1

1440

1

1800

1

2160

1

2520

1

dy

dx

vzdialenosť [m]

čas (s)

Reference measurement:GPS position accuracySSRI reference stationSAMPLE ACCURACY – refer to

the mapping method – with regard to map scale – with regard to sample design

V. Hutár

cluster regular random

SSCRI strategy for creation of regional pedogeochemical maps - Sampling strategy

Searching for spatial dependence, analyzing the basic principles in space with regard on accuracy, scale and dimension

directional variogram,direction

60º

anisotropic variation directional variogram,direction

150º

directional variogram, anisotropy 1.6

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000

Lag D istance

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

Variogram

D irection: 60.0 Tolerance: 36.0Nt

0 1000 2000 3000 4000 5000 6000 7000

Lag D istance

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

Variogra

m

D irection : 0 .0 To lerance: 90.0Nt

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000

Lag D istance

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

Variogra

m

D irection: 150.0 Tolerance: 36.0Nt

V. Hutár

SSCRI strategy for creation of regional pedo -geochemical maps – geostatistics application

R2 = -0.266

0

10

20

30

40

50

60

0 20 40 60 80

prach (%)

íl (%

)

Analysing the multivariate objects regardinga.) 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

R2 = 0.4009

-10

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60

piesok (%)

íl (

%)

100

100 0

0

10 90

20 80

30 70

40 60

50 50

60 40

70 30

80 20

1090

0102030405060708090100

% piesok

% íl

lp lh

sp shssh

ss

spisi ssi

ts

ti

% prach

humus

prach

5b

5c

5d

5e

CaCO3

pH_H20

il

piesok

5a

-1

-0.5

0

0.5

1

1.5

-1.5 -1 -0.5 0 0.5 1 1.5 humus prach CaCO3

pH_H20

il

piesok

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

-2 -1 0 1 2 3 4

skupina 5a

skupina 5b

skupina 5c

skupina 5d

skupina 5e

0

500

1000

1500

2000

2500

3000

3500

1.1 1.3 1.5 1.7 1.9

Phi

-dJ/

dphi

triedy_2

triedy_3

triedy_4

triedy_5

V. Hutár

SSCRI strategy for creation of regional pedo -geochemical maps – multivariate analyses, fuzzy k-means

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

SSCRI strategy for creation of regional pedo -geochemical maps Study case 1: Chvojnicka hilly land

PCA_chv

As

Hg

Mo

Ba

Co

Cr

Cu

Ni

Pb

V

Zn

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

1,2

-1,2 -1 -0,8 -0,6 -0,4 -0,2 0 0,2

RDA_chv

As

Ba

Hg

Mo

CoCr

Cu

Ni

Pb

VZn

% Cox

% íl

-0,2

0

0,2

0,4

0,6

0,8

1

1,2

-0,2 0 0,2 0,4 0,6 0,8 1 1,2

H.metal Ba Co Cr Cu Mo Ni V Zn

A-limit 8 1 5 39 7 37 10 2

B-limit     2     2    

Number of samples with exceeded concentration of risk elements

V. Hutár

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

Genetic horizons [cm]

Soil stratification

Colour:

Features of soil genesis:

others profile data:

Input matrix

Scheme of numericcoding of soil properties:

J.Balkovič & G. Čemanová

Fuzzy k-mean classification (centroids)

J. Balkovič & G. Čemanová

5A 5B 5C

5E5D

Interpolated rasters of membership values

J. Balkovič & G. Čemanová

Digital diffuse soil map obtained by„pixel mixture“ techniqueJuraj Balkovič & Gabriela Čemanová

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

Rosetta model for estimation of van Genuchten eq. parameters and validation:

SAND, SILT, CLAY

ROSETTA

PF-curve:Θr, Θs, α, n

Regionally defined PTFKPP-DB

sandy silt

J. Balkovič, T. Orfánus & R. Skalský

ΘFWC – field water capacityΘWP - wilting pointh - soil depth [0.5 m]

W = 1000 (ΘFWC - ΘWP).h [mm]

Potential water storage in soils (up to 50 cm)

J. Balkovič, T. Orfánus & R. Skalský