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JRC Ispra - IES 1 Novel GIS and Remote Sensing- based techniques for soils at European scales F. Carré, T. Hengl, H.I. Reuter, L. Rodriguez-Lado G. Schmuck.

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Presentation on theme: "JRC Ispra - IES 1 Novel GIS and Remote Sensing- based techniques for soils at European scales F. Carré, T. Hengl, H.I. Reuter, L. Rodriguez-Lado G. Schmuck."— Presentation transcript:

1 JRC Ispra - IES 1 Novel GIS and Remote Sensing- based techniques for soils at European scales F. Carré, T. Hengl, H.I. Reuter, L. Rodriguez-Lado G. Schmuck (LMNH Unit) & L. Montanarella (MOSES Action)

2 JRC Ispra - IES 2 Framework of the project Soil Thematic Strategy European Soil Data Center OUR RESEARCH ACTIVITY Data support Data needs Communication Methods & Data

3 JRC Ispra - IES 3 Innovation of the project Problem of traditional soil maps From a scientific point of view - traditional soil maps are not easy to understand (no methodology described, terminology understandable only by soil science community) - soil attribute information can be missing at appropriate scale From an economic point of view - Usually soil attributes and classes are represented with crisp boundaries coming from expert interpretation and there is no indication of the soil map quality Traditional soil surveys are very expensive because they need a lot of auger information Need quantitative methods to map easy to interpret attributes Need easy- to-use models (tools) for soil mapping Need to evaluate the accuracy of the soil maps Need sampling techniques for augering

4 JRC Ispra - IES 4 uncertainty Innovation in images… Soil type map

5 JRC Ispra - IES 5 To provide quantitative soil data, producible at low cost and easy- to-interpret-and-use (for other scientists and policy makers) Core - for mapping; To elaborate quantitative methods : How? - for estimating associated accuracy; Using easily accessible indirect soil information (auxiliary data) Core of the methodology Digital Soil Mapping Name

6 JRC Ispra - IES 6 DSM in practice (example of application) Presentation of Digital Soil Mapping methodology Tools and guidelines addressed to soil data users

7 JRC Ispra - IES 7 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracySoil threatsSoil functions Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT Sampled data Soil covariates (RS images, DEM…) Statistics Geostatistics Accuracy map Soil attribute map Suitability map Erosion map Digital Soil Mapping (DSM)

8 JRC Ispra - IES 8 DSM in practice (example of application) Presentation of Digital Soil Mapping methodology Tools and guidelines addressed to soil data users

9 JRC Ispra - IES 9 DSM application example Heavy Metal Content in Zagreb County (Croatia) Author: Hengl (2006)

10 JRC Ispra - IES 10 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracySoil threatsSoil functions Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT Heavy Metal content

11 JRC Ispra - IES 11 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracySoil threatsSoil functions Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

12 JRC Ispra - IES samples over 3700 km 2 : contents of Cu, Pb, Ni, Zn Zagreb county

13 JRC Ispra - IES 13 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracySoil threatsSoil functions Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

14 JRC Ispra - IES 14 Zagreb county

15 JRC Ispra - IES 15 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracySoil threatsSoil functions Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

16 JRC Ispra - IES 16 Regression-kriging Multiple Linear Regression Y j = a 1 X 1 + a 2 X 2 + … + a n X n + ε j Soil variable j residuals j Kriging YjYj a i X i i γεjγεj distance (m) Semi-variance (interpolation process according to spatial autocorrelations of the variable) Auxiliary data i Spatially continuousPunctual Summation of the two maps regression kriging regression- kriging auxiliary data residuals soil variables

17 JRC Ispra - IES 17 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracySoil threatsSoil functions Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

18 JRC Ispra - IES 18 Soil attribute map

19 JRC Ispra - IES 19 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracySoil threatsSoil functions Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

20 JRC Ispra - IES 20 Continuous maps of Heavy Metal Content Spatial accuracy map East

21 JRC Ispra - IES 21 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracySoil threatsSoil functions Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

22 JRC Ispra - IES 22 Limitation scores From Hengl in Dobos et al. (2006) Triantifalis et al., 2001 LS= b 0. HMC b1 -1 if HMC X1 0 if HMC < X Limitation scores Permissible (baseline) concentration Serious pollution Heavy metal concentration (mg kg -1 ) LS= HMC X1X1 X2X2 LS = 1 when HMC = X 1 LS = 5 when HMC = X 2 X 1 mg. kg -1 X 2 mg. kg -1 ln(b 0 ) b1b1 Cd Cr Cu Ni Pb Zn Pollution standards in Croatia

23 JRC Ispra - IES 23 Pollution map

24 JRC Ispra - IES 24 - Technical manual / textbook to process DEMs (Hengl & Reuter) DSM in practice (example of application) Presentation of Digital Soil Mapping methodology Tools and guidelines addressed to soil data users

25 JRC Ispra - IES 25 Geomorphometry book (Hengl & Reuter) DEM is the main source of data for DSM (70%) Technical manual / textbook to process DEMs and extract surface parameters and objects

26 JRC Ispra - IES 26 CONCLUSIONS

27 JRC Ispra - IES 27 Digital Soil Mapping Soil sampling Continuous soil classification Interpretation of soil attributes with RS data Erosion (wind, water…) tool Actual work For 2007 Typology of soil pollutions Mapping of the ecosystem continuum Modelling soil scenarios Improving EU soil map Present / Future of DSM

28 JRC Ispra - IES 28 Digital Soil Mapping Support to FP7 Risk assessment Health Inputs for biomass prediction agriculture Auxiliary data needs Information and communication technology Input for soil - forest continuum Energy inputs for STS and other directives Environment

29 JRC Ispra - IES 29 Thanks for your attention

30 JRC Ispra - IES 30 ANNEXES

31 JRC Ispra - IES 31 Economic gain of DSM For physical soil parameters We consider that DSM allows for saving 2/3 of the sampling So for an area of 3700 km² where 1150 samples were measured, only 380 should be observed. 20 profile observations/ day can be done, paid around 150 Total cost: 2850 instead of 8625 (5775 i.e. 67% saved) For chemical soil parameters We consider that DSM allows for saving 1/3 of the sampling So for an area of 3700 km² where 1150 samples were measured, 770 should be measured. 1 profile measurement with 10 HMC + pH, OC, P, K, N is estimated to cost ~100 Total cost: instead of (38000 saved i.e. 33%)

32 JRC Ispra - IES 32 Economic gain of DSM For physical soil parameters: DSM allows for saving 2/3 of the sampling 1500 Km samples (3375 ) 150 samples (1125 ) 2250 SAVED For chemical soil parameters: DSM allows for saving 2/3 of the sampling 1500 Km samples (45000 ) 300 samples (30000 ) SAVED

33 JRC Ispra - IES 33 Mapping of soil, by J.P. Legros (translated by V.A.K. Sharma). Science Publishers, Enfield, pp ISBN

34 JRC Ispra - IES 34

35 JRC Ispra - IES 35 Principles Set of soil observations A B C D Set of soil references OSACA Software ABCDREF A B C B B Result table dmin 0.1

36 JRC Ispra - IES 36 SOIL MAP OF AISNE (FRANCE) AT 1: SCALE (Carré & Reuter) To be published in Elsevier (2007) SOIL MAPPING UNITS OSACA Classes DISTANCES TO SMU OSACA distances

37 JRC Ispra - IES 37 SOIL INFERENCE SYSTEM Principal Component Analysis Soil contamination for Natura 2000 sites in Italy (Rodriguez-Lado) Soil Types Hierarchical Cluster Analysis Heavy Metal Contents Permuted Data Matrix CALCARIC FLU CHROMIC PHAE CHROMIC LUVI DYSTRIC LUVI GLEYIC PHAEO EUTRIC CAMBI CALCARIC PHA CALCARIC REG CALCARIC GLE LUVIC PHAEOZ HAPLIC PHAEO CALCARIC CAM HUMIC UMBRIS VITRIC ANDOS CR NI HG CD ZN PB CU CalcaricFluvisol ChromicPhaeozem ChromicLuvisol DystricLuvisol GleyicPhaeozem EutricCambisol CalcaricPhaeozem CalcaricRegosol CalcaricGleysol LuvicPhaeozem HaplicPhaeozem CalcaricCambisol HumicUmbrisol VitricAndosol Cr Ni HgCdZnPbCu Basilicata CalcaricFluvisol ChromicPhaeozem ChromicLuvisol DystricLuvisol GleyicPhaeozem EutricCambisol CalcaricPhaeozem CalcaricRegosol CalcaricGleysol LuvicPhaeozem HaplicPhaeozem CalcaricCambisol HumicUmbrisol VitricAndosol Cr Ni HgCdZnPbCu

38 JRC Ispra - IES 38 Reuter In Reuter et al. (2006) Wind Speed [m/s] Climate erodibility of agriculture soils (Reuter)


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