Novel GIS and Remote Sensing-based techniques for soils at European scales

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

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

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

Innovation in images… Soil type map uncertainty

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

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

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

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

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

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

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

Zagreb county

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

Soil attribute map

Spatial accuracy map East • Continuous maps of Heavy Metal Content

30 LS= 0.000114. HMC2.322 -1 25 b0 . HMCb1 -1if HMC ≥ X1 LS = 0 if HMC < X1 20 X2 Serious pollution Limitation scores 15 X1 Permissible (baseline) concentration 10 5 Pollution standards in Croatia 0 0 200 250 50 100 150 X1 mg. kg-1 ln(b0) X2 mg. kg-1 b1 Heavy metal concentration (mg kg-1) Cd 0.8 5 0.392 1.756 Cr 50 100 -9.083 2.322 100 Cu 50 -9.083 2.322 Ni 30 10 -7.897 2.322 Pb 60 50 -5.731 1.465 Zn 150 300 -11.634 2.322 Limitation scores Triantifalis et al., 2001 LS = 1 when HMC = X1 LS = 5 when HMC = X2 From Hengl in Dobos et al. (2006)

Pollution map

 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)

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

CONCLUSIONS

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

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

florence.carre@jrc.it hannes.reuter@jrc.it Thanks for your attention tomislav.hengl@jrc.it luiz.rodriguez-lado@jrc.it

ANNEXES

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: 77000 € instead of 115000 € (38000 €saved i.e. 33%)

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

Mapping of soil, by J.P. Legros (translated by V.A.K. Sharma). Science Publishers, Enfield, 2006. 409 pp ISBN 1-57808-363

http://eusoils.jrc.it/ESDB_Archive/eusoils_docs/other/EUR22123.pdfhttp://eusoils.jrc.it/ESDB_Archive/eusoils_docs/other/EUR22123.pdf

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

OSACA Classes OSACA distances SOIL MAPPING UNITS DISTANCES TO SMU SOIL MAP OF AISNE (FRANCE) AT 1:250.000 SCALE (Carré & Reuter) To be published in Elsevier (2007)

 Principal Component Analysis  Soil Types Basilicata P e r m u t e d D a t a M a t r i x Hierarchical Cluster Analysis Cr Ni Hg Cd Zn Pb Cu HG CR CD CU PB ZN NI Cr Ni Hg Cd Zn Pb Cu Calcaric Calcaric Fluvisol Fluvisol C A L C A R I C F L U Chromic Chromic Phaeozem Phaeozem C H R O M I C P H A E Chromic Chromic Luvisol Luvisol C H R O M I C L U V I Dystric Dystric Luvisol Luvisol D Y S T R I C L U V I Gleyic Gleyic Phaeozem Phaeozem G L E Y I C P H A E O Eutric Eutric Cambisol Cambisol E U T R I C C A M B I Calcaric Calcaric Phaeozem Phaeozem C A L C A R I C P H A Calcaric Calcaric Regosol Regosol C A L C A R I C R E G Calcaric Calcaric Gleysol Gleysol C A L C A R I C G L E Luvic Luvic Phaeozem Phaeozem L U V I C P H A E O Z Haplic Haplic Phaeozem Phaeozem H A P L I C P H A E O Calcaric Calcaric Cambisol Cambisol C A L C A R I C C A M 2 Humic Humic Umbrisol Umbrisol H U M I C U M B R I S 1 Vitric Vitric Andosol Andosol 0 V I T R I C A N D O S - 1 Soil contamination for Natura 2000 sites in Italy (Rodriguez-Lado) SOIL INFERENCE SYSTEM Heavy Metal Contents

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

Novel GIS and Remote Sensing-based techniques for soils at European scales