JRC Ispra - IES 1/17 Carré & Jacobson Soil profile distance measures and classifications A B Carré...

Carré & Jacobson

JRC Ispra - IES 1/17

Soil profile distance measures and classifications

A B

Carré & Jacobson

D(A;B)

Application to an Australian soil dataset

Pedometrics 2007, 27-29/08/2007

Carré & Jacobson


Objectives

To make a quantitative tool for soil profile classification

Which provides different types of distances according to the soil classification purpose

Which is able to make supervised classification (principle of soil taxonomies) or unsupervised classification

To test the quantitative tool relatively to

Soil taxonomy purpose

Available Water Capacity prediction

OSACA presentation

Application

Focusing on

The input content of the classification (soil against terron)

The number of classes (level of taxonomic detail)

The classes robusteness (sensitivity of the distances)

Carré & Jacobson


OSACA principles

Soil observations Horizon classification

horizons

User requirements

Range of horizon classes

Range of solum classes

Min. # Horizon

centroids Calculation of

distances

Calculation of new

centroidsDintra/Dinter ratio

Min+1. # Horizon

centroids

Max. # Horizon

centroids

Horizon types

Solum classification

Min. # solum

centroids

Calculation of

distances Calculation

of new centroids

Dintra/Dinter ratio

Min+1 # solum

centroids

Max # solum

centroids

Soil types

optimization

Carré & Jacobson


OSACA principles

Soil observations

Soil types

D(obs,r

ef)

1 0.7 1.30.1 0.3 B 0.1

2

3

4

5

A B C D REF

2.5

3.0

0.2

1.20.1

0.4

0.8

0.6

0.61.5

0.1

0.1

0.0

0.1

1.2

1.9

A

C

B

B

Result table

dmin

0.1

0.1

0.1

0.1

Soil observations

Soil types

If user has references, only one iteration

Carré & Jacobson


OSACA distances

Horizon distances between horizon i and horizon k having n soil variables

Euclidian distance

Manhattan distance

Carré & Jacobson


OSACA distances

Solum distances

D(x,A)

D(x,B)

D(y,B)

D(y,C)

D(z,C)

D(?,C)

x

y

z

0 cm

30 cm

80 cm

100 cm

Observation

0 cm

20 cm

60 cm

120 cm

A

B

C

Reference

Pedological distance

+

+

+

+

+

D

(# D)

Carré & Jacobson


OSACA distances

Solum distances

D(x,A)

D(x,B)

D(y,B)

D(y,C)

D(z,C)

D(?,C)

x

y

z

0 cm

30 cm

80 cm

100 cm

Observation

0 cm

20 cm

60 cm

120 cm

A

B

C

Reference

Utilitarian distance D*e

Max depth

+

+

+

+

+

Carré & Jacobson


OSACA distances

Solum distances

D(x,A)

D(x,B)

D(y,B)

D(y,C)

D(z,C)

x

y

z

0 cm

30 cm

80 cm

100 cm

Observation

0 cm

20 cm

60 cm

120 cm

A

B

C

Reference

Joint distance

x

y

z

0

0.3

0.8

1

0

0.16

0.5

1

A

B

C

+

+

+

+

D

(# D)

Carré & Jacobson


ApplicationEdgeroi area

341 soil profiles

Altitude

Slope

Aspect

Plan curvature

Profile curvature

CTIDEM 25m

Landsat ETM 7c

LS Panchromatic

NDVI

Clay Index (5/7)

RS 30m

K

U

Th

2m

SPOT 4

NDVI

RS 20m

Soil variables

9 order names/ 20 subgroup names

AWC

N

Carré & Jacobson


Application

Sand (%)/ Silt (%)/ Clay (%)

pHH20

CaCO3

EC

Munsell Colour (Hue, Value, Chroma)

Solum depth

Profile description

0 cm

10 cm

20 cm

30 cm

40 cm

70 cm

80 cm

120 cm

130 cm

C

R, G, B (0- 255)

Illuminant C two degrees observer (C 1931)http://WalkillColor.com

Standardization of soil variables (320 soil profiles)

0 cm

10 cm

20 cm

30 cm

40 cm

70 cm

80 cm

120 cm

130 cm

s.depth

Ca,Mg,Na

Carré & Jacobson


Tests

Testing OSACA classifications against soil taxonomy

1st question: Do we have enough data to speak about pedogenesis?

Texture

pHH20

CaCO3

EC

Munsell Colour

Solum depth

Ca,Mg,Na

C

Pool of soil variables

Sufficient pool of

variables

Rudo Teno Verto Kuro Sodo Chromo Calcaro Dermo Kando

Clay>35% 0 0 1 0 0 0 0 1 0

throughout

Cracks & 0 0 1 0 0 0 0 0 0

slickenside

Texture contrast 0 0 0 1 1 1 0 0 0

Sodic subsoil 0 0 1 0 1 0 0 0 0

B2 pH<5.5 0 1 1 1 0 0 0 1 1

B2 pH>5.5 0 1 0 0 1 1 1 1 1

Calcareous 0 0 0 0 1 0 1 1 0

Lack texture 1 1 1 0 0 0 1 1 1

contrast

Structured B2 0 1 1 1 1 1 1 1 0

Massive B2 0 1 0 0 0 0 0 0 1

Rudiment B 1 0 0 0 0 0 0 0 0

Isbell et al. (1997) from Minasny & McBratney (to be published)

Lack in DB

Carré & Jacobson


Tests

Testing OSACA classifications against soil taxonomy

Testing OSACA results against the 20 taxonomy suborders

13 Soil variables

OSACA run OSACA run

15 Landscape attributes

PCA

13 PC Landscape attributes

18-25 Horizon classes

15-25 soil classes


Utilitarian distance

Joint distance

Terron classes

35-45 soil classes

15-25 terron classes

35-45 terron classes

19 classes

20 classes

20 classes

45 classes

45 classes

43 classes

R= 40%

R= 29%

R= 30%

R= 51%

R= 62%

R= 49%

18 classes

19 classes

24 classes

45 classes

44 classes

43 classes

R= 38%

R= 29%

R= 33%

R= 51%

R= 50%

R= 48%

R= 41% R= 52% R= 38% R= 52%

R= 31% R= 51% R= 34% R= 49%

R= 30% R= 63% R= 30% R= 52%confusions (dmin+ 10%)

Allocation comparison

² test

R²(U) likelihood-ratio

Carré & Jacobson


Tests

Testing OSACA classifications against Available Water Capacity (AWC)

AWC1m predicted by Minasny (2007) from:

Sand, Clay, BD

Sand, Org C Bulk Density (BD)

Field Capacity (FC) & Permanent Wilting Point (PWP)

AWC FC & PWPhor

1m

AWCi= K + tdit + it=1

C


Utilitarian distance

Joint distance

19 classes

20 classes

20 classes

45 classes

45 classes

43 classes

R²adj= 77%

R²adj= 57%

R²adj= 57%

R²adj= 82%

R²adj= 70%

R²adj= 71%

18 classes

19 classes

24 classes

45 classes

44 classes

43 classes

R²adj= 72%

R²adj= 58%

R²adj= 58%

R²adj= 83%

R²adj= 69%

R²adj= 69%

SOIL TERRON

R²adj prediction

Carré & Jacobson


Conclusions

OSACA is a good tool for transforming soil observations into quantitative classes. As a WebApplication, it is easy to use and as an open source, it can be modified.

The quantitative soil and terron classes formed by OSACA are significatively correlated with soil taxonomy (if enough soil variables to describe the pedogenesis) and secondary soil variables.

For dealing with soil taxonomy, the soil classes are better predictors than the terron classes. Joint distance and pedological distances give better correlations between soil classes and Australian soil subgroups.

The distances can be afterwards used for mapping purposes and for deriving uncertainties associated to predictions (DSM purposes).

For dealing with secondary soil variable, the pedological distance is the best predictor. With low and high number of classes, soil seems to be the best predictor. The terron does not increase so much the prediction.

The allocation confusion due to close distances changes 1% of the correlations.

Carré & Jacobson


Enter your observations

Enter your references (if you have some)

! Soon downloadable at http://eusoils.jrc.it !

Carré & Jacobson


Enter the number of horizon classes

Enter the number of soil classes you want

Carré & Jacobson


Get the results (tables)

JRC Ispra - IES 1/17 Carré & Jacobson Soil profile distance measures and classifications A B Carré...

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