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Soil profile distance measures and classifications

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Presentation on theme: "Soil profile distance measures and classifications"— Presentation transcript:

1 Soil profile distance measures and classifications
Carré & Jacobson Soil profile distance measures and classifications Application to an Australian soil dataset B A D(A;B) Carré & Jacobson Pedometrics 2007, 27-29/08/2007

2 To make a quantitative tool for soil profile classification
Objectives OSACA presentation 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 Application To test the quantitative tool relatively to  Soil taxonomy purpose  Available Water Capacity prediction 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

3 OSACA principles Horizon classification Solum classification
horizons Soil observations Min. # Horizon centroids Max. # Horizon centroids Min+1. # Horizon centroids Calculation of distances Soil types Calculation of new centroids Min+1 # solum centroids Max # solum centroids Min. # solum centroids Solum classification Dintra/Dinter ratio Dintra/Dinter ratio optimization Horizon types Calculation of distances User requirements  Range of horizon classes  Range of solum classes Calculation of new centroids Carré & Jacobson

4 Result table OSACA principles
Soil observations If user has references, only one iteration D(obs,ref) Soil types 1 0.7 1.3 0.1 0.3 B 2 3 4 5 A C D REF 2.5 3.0 0.2 1.2 0.4 0.8 0.6 1.5 0.0 1.9 Result table dmin Soil observations Soil types Carré & Jacobson

5 OSACA distances  Horizon distances between horizon i and horizon k having n soil variables Euclidian distance Manhattan distance Carré & Jacobson

6  D  (# D) OSACA distances  Solum distances Pedological distance x y
z 0 cm 30 cm 80 cm 100 cm Observation 0 cm 20 cm 60 cm 120 cm A B C Reference D(x,A) + D(x,B) + D(y,B) + D(y,C) + D(z,C) + D(?,C) Carré & Jacobson

7  D*e OSACA distances  Solum distances Max depth Utilitarian distance
y z 0 cm 30 cm 80 cm 100 cm Observation 0 cm 20 cm 60 cm 120 cm A B C Reference D(x,A) + D(x,B) + D(y,B) + D(y,C) + D(z,C) + D(?,C) Carré & Jacobson

8  D  (# D) OSACA distances  Solum distances Joint distance x y z
0 cm 30 cm 80 cm 100 cm Observation 0 cm 20 cm 60 cm 120 cm A B C Reference x y z 0.3 0.8 1 0.16 0.5 1 A B C D(x,A) + D(x,B) + D(y,B) + D(y,C) + D(z,C) Carré & Jacobson

9 Application Edgeroi area N 341 soil profiles   Altitude  Slope
 Aspect  Plan curvature  Profile curvature  CTI DEM 25m  Landsat ETM 7c  LS Panchromatic  NDVI  Clay Index (5/7) RS 30m  SPOT 4  NDVI RS 20m N  K  U  Th  2m  Soil variables  AWC  9 order names/ 20 subgroup names 341 soil profiles  Carré & Jacobson

10 Standardization of soil variables (320 soil profiles)
Application Profile description 0 cm 10 cm 20 cm 30 cm 40 cm 70 cm 80 cm 120 cm 130 cm s.depth 0 cm 10 cm 20 cm 30 cm 40 cm 70 cm 80 cm 120 cm 130 cm  Sand (%)/ Silt (%)/ Clay (%)  pHH20  CaCO3  EC  C  Ca,Mg,Na  R, G, B (0- 255)  Munsell Colour (Hue, Value, Chroma) Illuminant C two degrees observer (C 1931) Standardization of soil variables (320 soil profiles)  Solum depth Carré & Jacobson

11 Tests  Testing OSACA classifications against soil taxonomy
1st question: Do we have enough data to speak about pedogenesis? Rudo Teno Verto Kuro Sodo Chromo Calcaro Dermo Kando Clay>35% 1 throughout Cracks & slickenside Texture contrast Sodic subsoil B2 pH<5.5 B2 pH>5.5 Calcareous Lack texture contrast Structured B2 Massive B2 Rudiment B  Texture  pHH20  CaCO3  EC  Munsell Colour  Solum depth  Ca,Mg,Na  C Pool of soil variables Sufficient pool of variables Isbell et al. (1997) from Minasny & McBratney (to be published) Carré & Jacobson Lack in DB

12 13 PC Landscape attributes
Tests  Testing OSACA classifications against soil taxonomy Testing OSACA results against the 20 taxonomy suborders Allocation comparison ² test R²(U) likelihood-ratio 18-25 Horizon classes 13 Soil variables 15 Landscape attributes 13 PC Landscape attributes PCA Terron classes 15-25 soil classes 15-25 terron classes OSACA run 35-45 soil classes 35-45 terron classes 19 classes 45 classes Pedological distance 18 classes 45 classes R= 40% R= 51% R= 38% R= 51% R= 41% R= 52% R= 38% R= 52% 20 classes 43 classes Utilitarian distance 24 classes 43 classes R= 30% R= 49% R= 33% R= 48% R= 31% R= 51% R= 34% R= 49% 20 classes 45 classes Joint distance 19 classes 44 classes R= 29% R= 62% R= 29% R= 50% R= 30% R= 63% R= 30% R= 52% Carré & Jacobson confusions (dmin+ 10%)

13  FC & PWP Tests Pedological distance Utilitarian distance
 Testing OSACA classifications against Available Water Capacity (AWC) AWC1m predicted by Minasny (2007) from: Sand, Org C Bulk Density (BD) Sand, Clay, BD Field Capacity (FC) & Permanent Wilting Point (PWP)  FC & PWP hor 1m AWC AWCi= K + tdit + i t=1 C R²adj prediction SOIL TERRON 19 classes 45 classes Pedological distance 18 classes 45 classes R²adj= 77% R²adj= 82% R²adj= 72% R²adj= 83% 20 classes 43 classes Utilitarian distance 24 classes 43 classes R²adj= 57% R²adj= 71% R²adj= 58% R²adj= 69% 20 classes 45 classes Joint distance 19 classes 44 classes R²adj= 57% R²adj= 70% R²adj= 58% R²adj= 69% Carré & Jacobson

14 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.  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.  The distances can be afterwards used for mapping purposes and for deriving uncertainties associated to predictions (DSM purposes). Carré & Jacobson

15 ! Soon downloadable at http://eusoils.jrc.it !
Enter your observations First page of the OSACA Web application  Enter your references (if you have some) Carré & Jacobson

16  Enter the number of horizon classes
 Enter the number of soil classes you want Second page to enter your choices (in terms of types of distance and range of final classes you want) Carré & Jacobson

17 Get the results (tables)
Third page is to get all the results ie all the distances between observations and references or centroids (horizon & soil profiles). In the results you have also some ideas about the quality of the classification Dintra/Dinter ratios. Carré & Jacobson


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