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JRC Ispra - IES 1/17 Carré & Jacobson Soil profile distance measures and classifications A B Carré & Jacobson D(A;B) Application to an Australian soil.

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Presentation on theme: "JRC Ispra - IES 1/17 Carré & Jacobson Soil profile distance measures and classifications A B Carré & Jacobson D(A;B) Application to an Australian soil."— Presentation transcript:

1 JRC Ispra - IES 1/17 Carré & Jacobson 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

2 JRC Ispra - IES 2/17 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)

3 JRC Ispra - IES 3/17 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 centroids D intra /D inter ratio Min+1. # Horizon centroids Max. # Horizon centroids Horizon types Solum classification Min. # solum centroids Calculation of distances Calculation of new centroids D intra /D inter ratio Min+1 # solum centroids Max # solum centroids Soil types optimization

4 JRC Ispra - IES 4/17 Carré & Jacobson OSACA principles Soil observations Soil types D(obs,ref) B ABCDREF A C B B Result table dmin 0.1 Soil observations Soil types If user has references, only one iteration

5 JRC Ispra - IES 5/17 Carré & Jacobson OSACA distances Horizon distances between horizon i and horizon k having n soil variables Euclidian distance Manhattan distance

6 JRC Ispra - IES 6/17 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)

7 JRC Ispra - IES 7/17 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

8 JRC Ispra - IES 8/17 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 A B C D (# D)

9 JRC Ispra - IES 9/17 Carré & Jacobson Application Edgeroi area 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 K U Th 2m SPOT 4 NDVI RS 20m Soil variables 9 order names/ 20 subgroup names AWC N

10 JRC Ispra - IES 10/17 Carré & Jacobson Application Sand (%)/ Silt (%)/ Clay (%) pH H 2 0 CaCO 3 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) 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

11 JRC Ispra - IES 11/17 Carré & Jacobson Tests Testing OSACA classifications against soil taxonomy 1 st question:Do we have enough data to speak about pedogenesis? Texture pH H 2 0 CaCO 3 EC Munsell Colour Solum depth Ca,Mg,Na C Pool of soil variables Sufficient pool of variables RudoTenoVertoKuroSodoChromoCalcaroDermoKando Clay>35% throughout Cracks & slickenside Texture contrast Sodic subsoil B2 pH< B2 pH> Calcareous Lack texture contrast Structured B Massive B Rudiment B Isbell et al. (1997) from Minasny & McBratney (to be published) Lack in DB

12 JRC Ispra - IES 12/17 Carré & Jacobson Tests Testing OSACA classifications against soil taxonomy Testing OSACA results against the 20 taxonomy suborders 13 Soil variables OSACA run 15 Landscape attributes PCA 13 PC Landscape attributes Horizon classes soil classes Pedological distance Utilitarian distance Joint distance Terron classes soil classes terron classes terron classes 19 classes 20 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 (d min + 10%) Allocation comparison ² test R²(U) likelihood-ratio

13 JRC Ispra - IES 13/17 Carré & Jacobson Tests Testing OSACA classifications against Available Water Capacity (AWC) AWC 1m predicted by Minasny (2007) from: Sand, Clay, BD Sand, Org C Bulk Density (BD) Field Capacity (FC) & Permanent Wilting Point (PWP) AWC FC & PWP hor 1m AWC i = K + t d it + i t=1 C Pedological distance Utilitarian distance Joint distance 19 classes 20 classes 45 classes 43 classes R²adj= 77% 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= 83% R²adj= 69% SOIL TERRON R²adj prediction

14 JRC Ispra - IES 14/17 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.

15 JRC Ispra - IES 15/17 Carré & Jacobson Enter your observations Enter your references (if you have some) ! Soon downloadable at !http://eusoils.jrc.it

16 JRC Ispra - IES 16/17 Carré & Jacobson Enter the number of horizon classes Enter the number of soil classes you want

17 JRC Ispra - IES 17/17 Carré & Jacobson Get the results (tables)


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