Presentation is loading. Please wait.

Presentation is loading. Please wait.

G. Schmuck (LMNH Unit) & L. Montanarella (MOSES Action)

Similar presentations


Presentation on theme: "G. Schmuck (LMNH Unit) & L. Montanarella (MOSES Action)"— Presentation transcript:

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

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

3 Problem of traditional soil maps
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)  Need quantitative methods to map easy to interpret attributes - soil attribute information can be missing at appropriate scale  Need easy- to-use models (tools) for soil mapping - Usually soil attributes and classes are represented with crisp boundaries coming from expert interpretation and there is no indication of the soil map quality  Need to evaluate the accuracy of the soil maps From an economic point of view Traditional soil surveys are very expensive because they need a lot of auger information  Need sampling techniques for augering

4 Innovation in images… uncertainty Soil type map

5 Core of the methodology
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) Digital Soil Mapping Name

6  Presentation of Digital Soil Mapping methodology
 DSM in practice (example of application)  Tools and guidelines addressed to soil data users

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

8  Presentation of Digital Soil Mapping methodology
 DSM in practice (example of application)  Tools and guidelines addressed to soil data users

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

10 Heavy Metal content POLICIES / MANAGEMENT Soil observations
Auxiliary data Heavy Metal content Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

11 POLICIES / MANAGEMENT Soil observations Auxiliary data
Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

12 Zagreb county 1142 samples over 3700 km2: contents of Cu, Pb, Ni, Zn

13 POLICIES / MANAGEMENT Soil observations Auxiliary data
Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

14 Zagreb county

15 POLICIES / MANAGEMENT Soil observations Auxiliary data
Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

16 . Regression-kriging . Yj ∑ aiXi i  Multiple Linear Regression
Spatially continuous Punctual Yj = a1 X1 + a2X2 + … + an Xn + εj Soil variable j Auxiliary data i residuals j  Kriging (interpolation process according to spatial autocorrelations of the variable)  Summation of the two maps . γεj distance (m) Semi-variance regression auxiliary data kriging residuals regression-kriging soil variables

17 POLICIES / MANAGEMENT Soil observations Auxiliary data
Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

18 Soil attribute map

19 POLICIES / MANAGEMENT Soil observations Auxiliary data
Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

20 Continuous maps of Heavy Metal Content
Spatial accuracy map East

21 POLICIES / MANAGEMENT Soil observations Auxiliary data
Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT

22 Limitation scores 5 10 15 20 25 50 100 150 200 250 30 Limitation scores Permissible (baseline) concentration Serious pollution Heavy metal concentration (mg kg-1) LS= HMC X1 X2 LS = b0 . HMCb if HMC ≥ X1 if HMC < X1 X mg. kg-1 X mg. kg-1 ln(b0) b1 Cd Cr Cu Ni Pb Zn 0.8 50 30 150 5 100 10 60 300 0.392 -9.083 -7.897 -5.731 1.756 2.322 1.465 Pollution standards in Croatia Triantifalis et al., 2001 LS = 1 when HMC = X1 LS = 5 when HMC = X2 From Hengl in Dobos et al. (2006)

23 Pollution map

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

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 CONCLUSIONS

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

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

29 Thanks for your attention
Thanks for your attention

30 ANNEXES

31 For physical soil parameters
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 Economic gain of DSM For physical soil parameters: DSM allows for saving 2/3 of the sampling 450 samples 150 samples (1125 €) 2250€ SAVED 1500 Km2 (3375 €) For chemical soil parameters: DSM allows for saving 2/3 of the sampling 450 samples 300 samples (30000 €) 15000€ SAVED 1500 Km2 (45000 €)

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

34

35 Set of soil observations
C D Set of soil references Principles Set of soil observations 1 2 6 5 3 4 7 8 11 10 9 12 13 14 15 OSACA Software 1 2 3 4 5 A B C D REF 0.7 2.5 3.0 0.2 1.2 0.1 0.4 1.3 0.8 0.6 1.5 0.0 0.3 1.9 Result table dmin

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

37 Soil contamination for Natura 2000 sites in Italy (Rodriguez-Lado)
SOIL INFERENCE SYSTEM Principal Component Analysis Heavy Metal Contents Soil Types Hierarchical Cluster Analysis P e r m u t d D a M i x - 1 2 C A L R I F U H O E V Y S T G B Z N CR NI HG CD ZN PB CU Calcaric Fluvisol Chromic Phaeozem Luvisol Dystric Gleyic Eutric Cambisol Regosol Gleysol Luvic Haplic Humic Umbrisol Vitric Andosol Cr Ni Hg Cd Zn Pb Cu Basilicata Cr Ni Hg

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


Download ppt "G. Schmuck (LMNH Unit) & L. Montanarella (MOSES Action)"

Similar presentations


Ads by Google