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Novel GIS and Remote Sensing- Based Techniques for Soils at European Scales

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Novel GIS and Remote Sensing- Based Techniques for Soils at European Scales 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) 1 JRC Ispra - IES Framework of the project Soil Thematic Strategy t or p up s s ta eed a n D a at D Communication European Soil Data OUR RESEARCH ACTIVITY Center Methods & Data 2 JRC Ispra - IES 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) Ö Need quantitative methods to map easy to interpret attributes - soil attribute information can be missing at appropriate scale Ö 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 Ö 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 Ö Need sampling techniques for augering 3 JRC Ispra - IES Innovation in images… uncertainty Soil type map 4 JRC Ispra - IES Core of the methodology Core To provide quantitative soil data, producible at low cost and easy- to-interpret-and-use (for other scientists and policy makers) How? By elaborating quantitative methods : - for mapping; - for estimating associated accuracy; Using easily accessible indirect soil information (auxiliary data) Name Digital Soil Mapping 5 JRC Ispra - IES n Presentation of Digital Soil Mapping methodology o DSM in practice (example of application) p Tools and guidelines addressed to soil data users 6 JRC Ispra - IES Digital Soil Mapping (DSM) Sampled data Soil observations Auxiliary data y y y y y y Soil inference Statistics y y system (spatial, attribute) Geostatistics Soil covariates (RS images, DEM…) Accuracy map Soil attributes Spatial accuracy Soil classes Soil functions Soil threats Erosion map Soil attribute map Scenario testing/ risk assessment Market / society Environment Suitability map 7 JRC Ispra - IES POLICIES / MANAGEMENT n Presentation of Digital Soil Mapping methodology o DSM in practice (example of application) p Tools and guidelines addressed to soil data users 8 JRC Ispra - IES DSM application example Heavy Metal Content in Zagreb County (Croatia) Author: Hengl (2006) 9 JRC Ispra - IES Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Spatial accuracy Soil classes Soil functions Soil threats Heavy Metal content Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT 10 JRC Ispra - IES Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Spatial accuracy Soil classes Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT 11 JRC Ispra - IES Zagreb county 1142 samples over 3700 km2: contents of Cu, Pb, Ni, Zn 12 JRC Ispra - IES Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Spatial accuracy Soil classes Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT 13 JRC Ispra - IES Zagreb county 14 JRC Ispra - IES Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Spatial accuracy Soil classes Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT 15 JRC Ispra - IES Regression-kriging Y n Multiple Linear Regression j Spatially continuous Punctual .. Yj = a1 X1 + a2X2 + … + an Xn + εj . ..... Soil variable j Auxiliary data i residuals j . .. ∑ aiXi i o Kriging (interpolation process according to p Summation of the two maps spatial autocorrelations of the γ εj variable) regression auxiliary data . kriging . residuals . regression- Semi-variance kriging soil variables 16 JRC Ispra - IES distance (m) Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Spatial accuracy Soil classes Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT 17 JRC Ispra - IES Soil attribute map 18 JRC Ispra - IES Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Spatial accuracy Soil classes Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT 19 JRC Ispra - IES Continuous maps of Heavy Metal Content Spatial accuracy map East 20 JRC Ispra - IES Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Spatial accuracy Soil classes Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment POLICIES / MANAGEMENT 21 JRC Ispra - IES Limitation scores 30 LS= 0.000114. HMC2.322 -1 b1 25 b0 . HMC -1 if HMC ≥ X1 LS = 0 if HMC < X1 20 X2 Serious 15 pollution X1 Permissible 10 (baseline) Limitation scores concentration 5 Pollution standards in Croatia 0 0 50 100 150 200 250 X X ln(b ) b 1 2 0 1 -1 mg. kg-1 mg. kg-1 Heavy metal concentration (mg kg ) Cd 0.8 5 0.392 1.756 Triantifalis et al., 2001 Cr 50 100 -9.083 2.322 Cu 50 100 -9.083 2.322 LS = 1 when HMC = X Ni 30 10 -7.897 2.322 Ö 1 Pb 50 60 -5.731 1.465 Zn 150 300 -11.634 2.322 LS = 5 when HMC = X2 22 JRC Ispra - IES From Hengl in Dobos et al. (2006) Pollution map 23 JRC Ispra - IES n Presentation of Digital Soil Mapping methodology o DSM in practice (example of application) p Tools and guidelines addressed to soil data users - Technical manual / textbook to process DEMs (Hengl & Reuter) 24 JRC Ispra - IES 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 25 JRC Ispra - IES CONCLUSIONS 26 JRC Ispra - IES Present / Future of DSM Typology of Mapping of the Erosion (wind, soil pollutions ecosystem water…) continuum Interpretation Digital Soil Modelling of soil soil attributes with Mapping scenarios RS data Continuous Improving soil EU soil map classification Soil sampling tool Actual work For 2007 27 JRC Ispra - IES Support to FP7 Health agriculture Risk Inputs for biomass assessment prediction Digital Soil Information and Mapping communication techno. Auxiliary data inputs for STS needs and other directives Energy Environment Input for soil - forest continuum 28 JRC Ispra - IES [email protected] [email protected] Thanks for your attention [email protected] JRC Ispra - IES [email protected] ANNEXES 30 JRC Ispra - IES 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%) 31 JRC Ispra - IES Economic gain of DSM For physical soil parameters: DSM allows for saving 2/3 of the sampling 2250€ 1500 Km2 450 samples 150 samples (3375 €) (1125 €) SAVED For chemical soil parameters: DSM allows for saving 2/3 of the sampling 15000€ 1500 Km2 450 samples 300 samples (45000 €) (30000 €) SAVED 32 JRC Ispra - IES Mapping of soil, by J.P. Legros (translated by V.A.K. Sharma). Science Publishers, Enfield, 2006. 409 pp ISBN 1-57808-363 33 JRC Ispra - IES http://eusoils.jrc.it/ESDB_Archive/eusoils_docs/other/EUR22123.pdf 34 JRC Ispra - IES B Principles A 3 4 1 2 C 7 D 6 5 11 Set of soil references 9 10 8 OSACA Software 15 Result table 12 13 14 ABCDREF dmin 1 0.7 0.1 0.3 1.3 B 0.1 2 2.5 1.5 0.1 0.6 C 0.1 Set of soil observations 3 0.6 0.1 1.2 0.4 B 0.1 4 0.8 0.1 1.9 0.2 B 0.1 35 JRC Ispra - IES 5 0.1 1.2 0.0 3.0 A 0.1 SOIL MAP OF AISNE (FRANCE) AT 1:250.000 SCALE (Carré & Reuter) OSACA Classes OSACA distances SOIL MAPPING UNITS To be published in 36 JRC Ispra - IES DISTANCES TO SMU Elsevier (2007) Soil contamination for Natura 2000 sites in Italy (Rodriguez-Lado) FACTOR(1) FACTOR(2) FACTOR(3) FACTOR(4) CRNI NI CR NI CR F F ) A A 1 ( C C R T SOIL INFERENCE SYSTEM CU CU CU T ZN ZN ZN HG O O O HG HG CD PB PB CD CDPB T R R C ( ( 1 1 A ) ) F F PB PB PB F ) CU CU CU A A 2 ( C CD CD CD C R T n NI T CR HG O NI NI O O HG HG CR CR ZN ZN ZN T R R C ( ( 2 2 A ) Principal ) F F ZN ZN ZN F ) A CD CD CD A 3 ( C Component C R T CR T PB NI CR PB CRPB HG O O O HG CU HGNI CU CUNI T R R C ( ( 3 Analysis 3 A ) ) F HG HG HG F F ) A A 4 ( C C R T PB PB PB T CRNI O CD CD CD O O CR CR ZN ZN NI NI ZN T R CU CU CU R C ( ( 4 4 A ) ) Heavy Metal Contents F FACTOR(1) FACTOR(2) FACTOR(3) FACTOR(4) o BasilicataPermut ed Data Matrix Cr Ni Hg Cd Zn Pb Cu Hierarchical R I G D N B U CrC NiN HHg CdC ZZn PbP CuC CalCAcarLicCAFlRuvisolIC FLU Soil Types Cluster ChCHromiROc PMhaeozeIC PmHAE ChromCHRicO MLuvisolIC LUVI Analysis DDystricYSTLRuvisolIC LUVI GlGeyLicEYPhICaeozePHmAEO EutrEUicTRCaICmbCisoAl MBI CalcarCAicLCPhAaeozeRICmPHA CaCAlcaLricCAReRgICosolREG CaCAlcaLricCAGRleyICsolGLE LLuviUcVIPChaeozePHAEOm Z HaplHAicPLPhICaeozePHmAEO CaCAlcaricLCCaARmbICisoCAl M HumicHUMIUmbrC UMBisol RIS 2 1 VIVitriTRc ICAndoANsolDOS 0 -1 37 JRC Ispra - IES Climate erodibility of agriculture soils (Reuter) Reuter In Reuter et al.
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